Factors affecting the sorting effect of ore color sorter For the ore color sorter, I believe most mining enterprises have long been familiar with. So, ore color sorter in the process of widely used in the mining industry sorting work, whether it can meet the needs of all customers, its sorting effect by which factors determine or influence it, Mingde photoelectric after a long-term use of our ore color sorter customer analysis, for different materials, different enterprises, we summarized some of the content.       We often say, ore color sorter is a new automatic equipment, some people say ore color sorter is a kind of "robot", usually, ore color sorter mainly by the feeding system, photoelectric system, control system and sorting system four system composition. As a kind of ore sorting automation equipment, and weigh the four systems and judge the value of its work or mainly depends on the ore color sorter color sorting accuracy, processing capacity, raw materials containing impurities, selection of net rate and other factors.   In the actual production process, these factors are one, there are mutual influence and constraints of the relationship, must be examined at the same time. Our company (Mingde photoelectric) developed a new ore color sorter, in the software upgrade and spray valve response speed to meet the case at the same time, the servo system movement speed, the highest speed of the conveyor belt and the purity of raw materials will affect the color separation accuracy and the size of the processing capacity per unit of time; In addition, some manufacturers in the actual sorting process, the bad material cycle sorting, greatly reducing the rate of finished ore, in the effective time The efficiency of the qualified products is reduced within the effective time, which leads to the increase of the take-out ratio. If the take-out ratio is set too high, it will affect the selection rate and processing capacity of these two indicators; if set too low, the selected waste contains too much normal material, will cause waste. In a comprehensive manner, improve the sensitivity, the better the color separation effect, the greater the belt-out ratio; and the output requirements are also a direct impact on the color separation effect and belt-out ratio. Therefore, after analysis, we have increased the track speed of the color sorter. Compared with other manufacturers, our crawler speed is several times of the same industry manufacturers. They want to improve the output, in can not solve the track speed problem, can only increase the material coverage, in the field production, such coverage will seriously reduce the effective distance between the materials, in the sorting will reduce the resolution, resulting in a lot of bad material "escape", therefore, such a way will only make the material to increase the take-out ratio, and further reduce the separation rate. More reduce the net selection rate. In the long run, for our mining manufacturers, simply can not meet the production requirements, let alone recover costs and increase efficiency.       If you can not achieve the expected color separation effect, in addition to improper debugging, there is a little influence is the rate of raw material impurities. Low rate of impurities, impurities between the interval is large, leaving the servo system response time is long, you can also increase the speed of the conveyor belt to improve production. Similarly, the higher the initial purity of the raw material, the less impurities, the higher the accuracy of color selection, the higher the accuracy, the higher the yield. The normal materials and defective materials in the raw materials must exist a certain color difference; the smaller the color difference, the more difficult the color separation, the worse the performance index, the election rate of materials of different colors is also different.     The factor that has the greatest impact on the quality is the problem of color separation accuracy, we know that the high requirements for the finished material is generally to achieve high precision color separation. For example, we take quartz sand as an example, if it is used to make quartz plates, it can not be sloppy, quartz sand for quartz plates must be high purity, quartz sand can not have a little yellow skin on the surface, the hardness of the stone should also be good, so that the quartz plates made to meet the standards of the industry. In addition, high-end quartz used for export, manufacturing wafers, aerospace business, etc. requires even higher quality products. Conversely, quartz used as a filler in general glass, paint, rubber, plastic, etc. is not as demanding. A certain doping rate does not affect the application, so the yield can be pursued appropriately.       Color sorter as a typical optical-electro-mechanical integration system, involving optical-electro-mechanical soft innovation and application in various fields, the speed of upgrading is very fast, is a typical innovative technology products, mining enterprises in the use of caution, to do a full understanding of the color sorter manufacturers, the color sorter operation of in-depth understanding, so that it plays a due investment benefits.

How to choose ore color separator manufacturers for mining companies In order to improve the production efficiency  , to achieve satisfactory production results, the choice of good  equipment is very necessary, then, for the ore industry, how to choose a good ore sorting equipment?   Different places of ore, because of its ore grade, mineral structure, mineral form, mineral fugacity, and particle size and other ore properties are different beneficiation process and technical conditions are different, so the beneficiation technology can not simply apply others, but must use their own ore beneficiation test to determine whether the mineral has the value of use. Therefore, for such cases, we generally recommend that ore manufacturers bring materials or mail materials to equipment manufacturers to test whether the other party's equipment to meet their own ore products. Then visit the factory on site, listen to the technical characteristics of the manufacturer's analysis of the equipment, visit the long-term use of customers.   　　Stone production equipment selection should be the characteristics of the ore raw materials to determine the type of equipment, from the test or other engineering experience to obtain the index value for the calculation of the process flow, to determine the quality requirements of the combination of equipment form and configuration, to determine the production capacity of the equipment model and quantity.   　　In the selection of equipment priority is given to equipment that is easy to operate, reliable in operation, low energy consumption and operation and management costs, at the same time, the load factor of the equipment, the particle size of the product to be processed, the amount of variation in the grading is also an important selection factor.   On the issue of quality, we must consider the manufacturer of the production equipment, to see whether the manufacturer is a formal company, whether the strength is strong, whether the reputation is trustworthy, it is recommended that it is best to personally visit the site to see if it meets their required standards. At present, the main ore equipment is ore color separator, flotation machine, magnetic separator, etc.. Among them, ore color separator has now played a vital role in the market, and its application is increasingly widespread.       Ore color separator, the lucky star of the rise of China's mining industry. Ore color separator is the use of photoelectric principle through the color distinction of the raw ore sorting large mining technology equipment, its application not only solves the lack of operator technology and quality problems, but also greatly improve the yield of ore, thereby reducing the cost of ore, can be said to the above problem is solved.     In short, the introduction of ore color separator and the continuous development of mining production technology is quite important for the development of Chinese ore. It can not only solve the dilemma of domestic ore, but also open up a new chapter of rapid development of Chinese mining industry. Make domestic ore for imported ore to produce a strong impact, get rid of its dependence, and really achieve a new revolution of domestic ore, out of China, to the world! In this regard, ore color separator should not spare pressure, continue to develop and produce more possibilities in the field, fully support the development of domestic ore, and become the booster of a new chapter in the development of China's mining industry.

What are the advantages of Mingde photoelectric intelligent ore color sorter? Although the emergence of photoelectric color sorter has brought great changes to some industries, but with the changing production needs of color separation requirements are becoming more and more stringent. The traditional photoelectric color sorter can only meet the color separation of ore materials with large color difference, such as quartzite, talc, wollastonite and other materials color sorter can not make effective distinction. Ltd. after years of experience in color separation, launched AI artificial intelligence ore color sorter. Intelligent ore color sorting machine for ore texture characteristics, color, shape composite sorting, to solve the color sorting machine can not color sorting problems. Such as talc color sorting, needle dimensional wollastonite color sorting and other ore color sorting machine. Mingde photoelectric intelligent color sorter has what advantages? 1. In the field of sorting, the artificial intelligence means of neural network is introduced, which solves the problem that the color sorter can only sort based on simple criteria.   2. It can improve the sorting effect through learning.   3. One machine is multi-purpose, highly adaptable, shorten the sorting process to the greatest extent, reduce the user's investment in equipment, and improve the sorting efficiency.   4. intelligent ore color sorter for ore texture characteristics, color, shape composite sorting, to solve the problem of color sorting machine color can not. Such as talc color sorting, needle dimensional wollastonite color sorting and other ore color sorting machine.   5. Highly intelligent, remote debugging, intelligent monitoring, remote service, remote software upgrade.   6. Wide range of sorting materials, can be applied to ore, nuts, mixed grains and cereals, Chinese herbs, dehydrated food and other materials sorting    

Coal mine belt conveyor sorting robot foreign object identification and positioning system As an important equipment for coal mine production, the safe operation of coal mine belt conveyor is an important foundation to ensure the normal production of coal mine. However, in the process of coal production and transportation, coal mine conveyor belts can be affected by foreign objects mined out by comprehensive mining or comprehensive excavation, etc., which can lead to serious accidents such as torn belt and broken belt. Traditional foreign object detection methods such as manual detection, radar detection and metal detectors are inefficient, costly, difficult to deploy and maintain, and have safety hazards. With the continuous development of machine vision technology, domestic and foreign institutions and scholars have conducted a lot of research on the application of machine vision technology in coal mine belt conveyor condition monitoring and target detection. Although machine vision has a certain theoretical basis in coal mine belt conveyor target detection and identification, the current coal mine belt conveyor sorting robot target identification is mainly for gangue identification, and there is less research on foreign object target identification that causes belt penetration, tearing, etc., and there is less research on precise positioning of target foreign objects.   Hereby, MingDe designed a coal mine belt conveyor sorting robot foreign object identification and positioning system, which can identify and locate different types and shapes of foreign objects on the conveyor belt.   Through a multi angle, multi-dimensional stereo high-precision camera, the intelligent robot sorter quickly scans the ore on the conveyor belt. The self-developed CRM-CNN foreign object recognition algorithm accurately locates the 3D position of the debris, controls the robot to grab  the foreign object, and puts it into the foreign object collection box. Product characteristics   1: Based on deep learning technology ,combined with a large database of ore foreign objects, The intelligent robot sorter has a high foreign object recognition rate. 2: Using multi-angle and multi-dimensional industrial stereo camera and geometric 3D recognition algorithm, the intelligent robot sorting machine can accurately measure and position the depth, direction and position of foreign objects. 3: Highly flexible control, for the newly emerged foreign objects can be added at any time. 4: Specially developed high protection levels robot arm, faster and more flexible, can effectively adapt to various conveying speeds and harsh industrial environments 5: Highly intelligent, unattended and optional remote monitoring   Experimental Verification  The reliability of the coal mine belt conveyor sorting robot foreign object recognition and localization system and its algorithm is verified by the experimental prototype with rod-shaped foreign objects as the experimental object. The experimental results of the system prototype show that the foreign object recognition rate of the coal mine belt conveyor sorting robot foreign object recognition and positioning system is not affected by the size, material and color, etc., and it can realize the acquisition, processing, feature extraction, recognition and position positioning of the target foreign object image of the conveyor belt, and the recognition rate is more than 99.5%, and the average error of target foreign object position positioning is about 1%.  

How to sort feldspar Feldspar is a group of minerals consisting of aluminosilicates of potassium, sodium, calcium and barium. The main chemical composition is SiO2, Al2O3, CaO, K2O, Na2O. 　　Feldspar group minerals are the most important rock-forming minerals in nature, accounting for about 50%~60~ of the mineral composition of the earth's crust. Felsic minerals are widely produced in various genetic types of rocks, and are the main rock-forming minerals of magmatic and metamorphic rocks.   　　As feldspar is a rock-forming mineral, it is difficult to separate from other minerals in most cases, so the only feldspar deposits of industrial significance are pegmatite deposits with huge crystals that are easy to separate. I. Types of feldspar According to its chemical composition and crystallization characteristics, it can be divided into two subgroups: potassium sodium feldspar and plagioclase feldspar subgroup. 　　(1) Potassium-sodium feldspar subgroup 　　It is composed of potassium feldspar molecules and sodium feldspar molecules. The natural output of potassium feldspar are mixed with sodium feldspar, so often called potassium feldspar, all belong to potassium sodium feldspar, common potassium sodium feldspar species. 　　Turbidite, (K, Na)[AlSi3O8], contains sodium feldspar 　　molecules up to 50%, K: Na = 1: 1 　　Orthoclase, (K, Na)[AlSi3O8], containing sodium feldspar 　　molecules up to 30%, K:Na = 2:1 　　Microplagioclase, (K, Na)[AlSi3O8], containing sodium feldspar 　  molecules up to 20%, K: Na = 4: 1 　　(2) Plagioclase subgroup 　　It consists of sodium feldspar molecules and calcium feldspar molecules, which can be mixed in any ratio to form a continuous series of plagioclase analogues. It can be divided into six types: sodium feldspar, more feldspar, medium feldspar, labradorite, peperite, calcium feldspar, etc. Among them. 　　Sodium feldspar, containing 0-10% of calcium feldspar molecules, is produced in pegmatites, fine crystal rocks and schist. 　　Calcium feldspar, containing calcium feldspar molecules 90 ~ 100%, produced in gabbro and related rocks. 　　2. Chemical composition of feldspar 　　The feldspar species used in the glass and ceramic industry are mainly microplagioclase, orthoclase and sodium feldspar. 　　The theoretical chemical composition of potassium feldspar is: K2O 16.90%, Al2O3 18.40%, SiO2, 64.70%. 　　(Among them: orthoclase often contains sodium feldspar, more up to 30%; microcline feldspar is a more pure potassium feldspar, but it contains sodium feldspar, more up to 20%) 　　The theoretical chemical composition of sodium feldspar is: Na2O 11.80%, Al2O3 19.50%, SiO2, 68.70% 　　But the pure sodium feldspar is rare, the content of Na2O is often lower than the theoretical value, and contains K2O, CaO, etc. Second, the physical and chemical properties of feldspar Feldspar family minerals are generally white, gray, light flesh red, glassy luster, decomposition development, hardness of 6 ~ 6.5, density of 2.5 ~ 2.7g/cm3. 　　As an important industrial raw material, the melting point, melting interval and viscosity of feldspar have important application significance. 　　1. Melting point and melt interval 　　The melting point of potassium feldspar is 1290℃, sodium feldspar is 1215℃, and 　　Calcium feldspar is 1552℃, and barium feldspar is 1715℃. 　　The wide melting interval is also one of the excellent process properties of feldspar, and the melting interval varies with the content of feldspar components. Potassium microplagioclase feldspar in 1160 ~ 1180 ℃ is liquid, to 1210 ~ 1280 ℃ before completely molten. 　　2. Melting liquid viscosity 　　When feldspar is melted, the melt viscosity depends on the mineral composition, chemical composition and melting temperature of the ore. 　　At the same temperature potassium feldspar melt than sodium feldspar melt viscosity, and as the temperature increases, sodium feldspar melt quickly becomes a small viscosity easy to dilute the fluid, so that the ceramic blank deformation. 　　Because of the potassium feldspar melting point is not high, melting interval time is long, molten liquid viscosity and other advantages, so in the industrial use of more than other feldspar more widely   　　3. Chemical stability 　　Potassium feldspar glass and soda feldspar glass have a high degree of chemical stability, except for high concentrations of sulfuric acid and hydrofluoric acid, not subject to corrosion by any other acids, alkalis. 　　4. Fluctuating properties 　　The feldspar melt has a fluxing effect on other substances, and its fluxing ability is related to the temperature and the type of feldspar. Sodium feldspar melt has a greater fusion effect on quartz than potassium feldspar melt. 　5. Easy to grind and grindability 　　The feldspar has good grinding and grindability due to the development of decomposition. The use of feldspar Feldspar is mainly used in glass and ceramic industry. The amount of feldspar in the glass industry accounts for 50%~60% of feldspar, the amount of feldspar in the ceramic industry accounts for 30%, and the rest is used in filler and other sectors. 　　1.Glass melting agent 　　Feldspar is one of the components of the glass mixture. It is mainly used to increase the alumina content in the glass ingredients, reduce the melting temperature and increase the alkali content in glass production, in order to reduce the amount of alkali. 　　Feldspar melt into glass process is relatively slow, crystallization ability is small, can prevent the formation of glass in the process of precipitation of crystals and damage to the product. Adjust the viscosity of the glass. 　　Generally used as a mixture of glass, such as potassium feldspar and sodium feldspar. Feldspar can also be used as glass fiber raw materials. 　　2. Ceramic blank raw materials 　　Before firing as a barren raw material to reduce the drying shrinkage deformation of the blank, improve drying performance, shorten the drying time. 　　When firing as a fusing agent to reduce the firing temperature, to promote the fusion of quartz and kaolin, accelerate the formation of mullite, so that the dense body and reduce the void, improve its mechanical strength and dielectric properties, improve the light transmission of the body. The amount of admixture is generally around 20%. 　　3. Ceramic glaze 　　Glaze is mainly composed of feldspar, quartz and clay raw materials, of which the content of feldspar up to 10% ~ 35%. In the ceramic industry (blank and glaze) is mainly with potassium feldspar. 　　Due to the feldspar fusion agent effect, so that the glaze melt fully. Feldspar glaze luster is good, smooth and transparent glaze. 　　4. Enamel raw materials 　　With feldspar and other mineral raw materials blended into enamel. The amount of feldspar blending is usually 20% to 30%. 　5.abrasive 　　The production of grinding wheels used as ceramic cements components, the content of 　　28%～30% 　　6. Other 　　Feldspar with high potassium content can be used as raw material for extracting potassium fertilizer. 　　It is one of the raw materials for the production of white cement. 　　Paper, refractory materials, machinery manufacturing, coatings, welding rods, etc. as filler D. Sorting of feldspar According to the different types and nature of feldspar deposits, and the use of different beneficiation methods. In the past, it was generally crushed and ground after hand sorting, and then magnetic separation was used to remove iron minerals. Such sorting method has been criticized by the majority of feldspar sorting enterprises due to the high cost, low efficiency and the inability to be used on a large scale. In recent years, as the quality of feldspar ore decreases, the quality of the product requirements continue to improve, as well as the development of comprehensive mine recovery, the introduction of increasingly sophisticated technology of color separation equipment for sorting operations, so as to remove quartz, mica, iron and titanium and other associated minerals. After years of development, our company (Hefei Mingde Photoelectric Technology Co., Ltd.) has developed ore color sorting machine for ore sorting, which is also applied to feldspar sorting operation on a large scale. For feldspar sorting, we use the photoelectric principle to identify and analyze the color of feldspar, so as to carry out sorting. This way of sorting improves the quality and productivity of the product and realizes the value of the ore itself. After research and the use of the majority of mining enterprises to prove that the sorting of feldspar and the entire ore industry will use the ore color sorting machine to produce, which is the progress of the industry, but also the progress of the times, but also the trend of the development of ore. Take the road of fine processing, deep processing has come, the use of automated equipment sorting is imminent. Mingde photoelectric welcome all mining enterprises to visit our company.

MingDe Material removal robot robotic manipulator  Robot Arm Foreign Object Removal Robot Material removal robot   A robot is an intelligent machine that can work semi-autonomously or fully autonomously. Robots can be programmed and automatically controlled to perform tasks such as working or moving. Product Principle Through a multi angle, multi-dimensional stereo high-precision camera, the intelligent robot sorter quickly scans the ore on the conveyor belt. The self-developed CRM-CNN foreign object recognition algorithm accurately locates the 3D position of the debris, controls the robot to grab  the foreign object, and puts it into the foreign object collection box. Mingde Product characteristics 1: Based on deep learning technology ,combined with a large database of ore foreign objects, The intelligent robot sorter has a high foreign object recognition rate. 2: Using multi-angle and multi-dimensional industrial stereo camera and geometric 3D recognition algorithm, the intelligent robot sorting machine can accurately measure and position the depth, direction and position of foreign objects. 3: Highly flexible control, for the newly emerged foreign objects can be added at any time. 4: Specially developed high protection levels robot arm, faster and more flexible, can effectively adapt to various conveying speeds and harsh industrial environments   5: Highly intelligent, unattended and optional remote monitoring   Application areas It is mainly used for ore sorting, sorting of anchor rods, steel brazier, rags, wood, iron parts, waste filling pipeline and other debris in the process of ore production and transportation, replacing manual hand sorting, laying the foundation for reducing personnel labor, lowering equipment failure rate, reducing staff and increasing efficiency.        

How do developed countries treat construction waste? Application areas It is mainly used in the sorting of ore, anchor rods, steel brazier, rags, wood, iron parts, waste filling pipes and other miscellaneous sorting in the process of ore production and transportation, replacing manual hand sorting, laying the foundation for reducing personnel labor, lowering equipment failure rate, reducing staff and increasing efficiency. Introduction   Construction waste is the waste generated in the process of building, maintenance and demolition, including waste concrete blocks, asphalt concrete blocks, mortar and concrete scattered during the construction process, broken brick slag, metal, bamboo and wood, waste from decoration, various packaging materials, etc., mainly solid waste.   For a long time, the reuse of construction waste in China has not attracted much attention, and it is usually transported to the suburbs or rural areas without any treatment and disposed of by means of open piles or landfills. The recycling of construction waste has become a major problem for urban construction. At the same time, there are still many materials in construction waste that can be recycled, and the immature construction waste treatment methods in China have invariably caused great waste of resources. The utilization rate of construction waste recycling in developed countries has reached 60% to 90%, or even 100%, while China is still less than 5%. Today, we share with you the experience of several developed countries in dealing with construction waste.   Japan   Japan regards construction waste as a "construction by-product" and attaches great importance to its reuse as a renewable resource. For example, port facilities and other infrastructure parts for renovation projects can use recycled stone to replace a considerable amount of natural quarry gravel material. Japan's leading policy on construction waste is to not transport construction waste from construction sites as much as possible and to reuse construction waste as much as possible. "Construction by-products" are divided into three categories: waste that cannot be used as raw materials, building materials that can be reused as raw materials (e.g. concrete blocks, wood), and building materials that can be reused directly (e.g. slag, metal). It can be seen that in the Japanese concept, the residual materials produced in the construction process are not all waste.   In Japan, there are more than 20 types of "construction by-products", and different laws apply to the disposal of different types of by-products. For example, weeds are treated as general waste, wood and construction sludge are treated as construction waste, metals are treated as industrial waste, asbestos, fluorescent light transformers and other toxic and hazardous substances are treated as specially managed industrial waste, and construction debris is not classified as waste.   Reducing construction site waste generation and reusing it as much as possible are the main principles of construction waste disposal in Japan. According to the "Outline for the Promotion of Appropriate Treatment of Construction By-products", construction project contractors and builders are obligated to reduce the generation of construction by-products during the construction process, and building material suppliers and building designers are obligated to produce and use building materials that can be recycled. Construction byproducts that can be reused should be reused as much as possible; construction byproducts that cannot be reused should be recycled as much as possible; and byproducts that cannot be recycled should be recycled by combustion to achieve heat recovery as much as possible.   In the past, Japan required the construction slag generated by piling and other construction slag to be transported out of the construction site, and then repurchased the slag when it was completed and backfilled, resulting in two transports and payments. In recent years, Japan has adopted a management approach to backfilling construction debris in place. The construction party can keep the slag excavated from piling in place at the construction site and then dispose of the remaining slag when it is backfilled to reduce the production of slag as much as possible.   Japan has a strict process to manage the production, sorting and disposal of construction waste. Construction teams are required to submit a detailed plan for the estimation, sorting and reuse, and final disposal of the waste that may be generated by the project to the construction company's headquarters, and to keep a report of the results for five years. If a company generates more than 1,000 tons of industrial waste in the previous year, it must submit a plan for waste reduction to the local prefectural governor by June 30 of that year.   A survey by the Ministry of Land, Infrastructure, Transport and Tourism showed that by the end of 2012, the re-resourceability of construction waste in Japan reached 96%, with the re-resourceability rate of concrete reaching 99.3%.   Germany   Germany's major cities suffered massive bombing in World War II, with Berlin and Dresden suffering over 80% damage to their buildings. Reconstruction requires a large amount of building materials, but Germany is a hundred years old, unable to produce. In such a situation, most of the construction waste was recycled, except for a few unmanageable building rubble that was dumped. The experience of reconstruction has fostered a renewed understanding in Germany that once put to good use, waste is also a resource.   Germany was the first country in the world to introduce legislation for a circular economy. After the introduction of the "Blue Angel" program in 1978, it enacted regulations such as the Waste Disposal Act, and in 1994 the Circular Economy and Waste Removal Act (amended in 1998), which has had a wide impact on the world. of recycled aggregates, and 175,000 housing units have been built with these recycled aggregates. At the same time, Germany levies a disposal fee of 500 euros per ton for unprocessed construction waste. The world's largest construction waste treatment plant is located in Germany, which can produce 1,200 tons of recycled construction waste per hour. There are about 200 construction waste disposal companies in Germany, with an annual turnover of 2 billion euros.   The "Georgswerder Energy Hill" on the banks of the Elbe in Hamburg is a classic example of waste recycling. Amidst the greenery, the white wind turbine blades slowly turn. Who would have thought that decades ago it was a dump for World War II bombing rubble and since then has been used for industrial waste and municipal garbage.   Wind power generator on the "Garbage Hill" on the Elbe River in Hamburg The garbage hill covers an area of 45hm2, the highest point being 40m above the ground, and was used for the reconstruction of Germany after World War II, when some of the construction waste could not be disposed of. Afterwards, Germany's industrialization took off and the scale of landfills became larger and larger. Until 1979, it was discovered that many companies had secretly buried a large amount of toxic chemical waste. The waste seeped into the ground and posed a threat to the safety of drinking water. A "rescue operation" was started.   The function of the "garbage mountain" Since the 1980s, the government has covered the mountain with a plastic waterproof film, laid a layer of soil up to 3m thick, and planted vegetation. In 2011, an 8,000m2 photovoltaic system was installed on the mountain, and a higher-powered wind turbine replaced the old motor. The electricity generated by both can meet the year-round needs of 4,000 households. The heat carried by the waste liquid generated by the garbage is also collected to heat the offices. In addition, a 1,000m long promenade was built at the top of the hill, which became the newest place for people to see the panoramic view of Hamburg. The garbage hill has become Hamburg's energy mound and a landscape park for the citizens. Panoramic view of the "Garbage Hill" According to German law, each responsible party in the construction waste production chain has to contribute to waste reduction and recycling. Manufacturers of construction materials must design their products to be more environmentally friendly and recycling-friendly. For example, they must produce panels in different lengths to avoid future re-cutting. Building contractors (including engineers and architects) must incorporate waste recycling into their construction plans. For example, use more recyclable building materials, etc. The responsibility of home demolition contractors is most critical. They are required by law to conduct demolition in a manner that facilitates the recycling of construction waste. In a highly competitive market, demolition contractors often obtain contracts from homeowners at very low or even zero prices. They then profit by breaking down, recycling and selling construction waste. This policy arrangement forces construction contractors and demolition operators to prevent contamination of construction materials to the greatest extent possible, as this not only results in reduced earnings for them, but also future payments for landfills or incineration.   Currently, using XRT intelligent sorting machine,Germany is one of the countries that does the best job of recycling construction waste, with a recycling rate of nearly 90%.   Singapore Data from Singapore's National Environment Agency shows that the country generated a total of 1,269,700 tons of construction waste in the year 2014, of which 1,260,000 tons were recycled, a recycling rate of 99%. About 8km south from Singapore's main island sits the world's first landfill developed from the sea, officially known as the Semakau Landfill, which consists of two small islands, Semakau Island and Sikyong Island, connected to each other and enclosed by the sea. In the 1960s and 1970s, Singapore relied on landfills around the island to dispose of solid waste, but by the late 1970s, limited land space forced the government to take steps to reduce waste generation and increase recycling rates. Singapore's "Semakau Landfill" Due to the economic value and market demand for construction waste such as steel bars, wood and concrete, waste collectors would sort the waste on site at the construction site by optical sorting machine and send it to the plant for recycling at a profit; if the waste was sent directly to the incineration plant or the Semakau landfill, the waste collectors would instead have to pay the corresponding waste disposal fees. Singapore's "Semakau Landfill"   As construction waste such as steel bars, wood and concrete have economic value and market demand, waste collectors will sort the waste on site at construction sites by garbage sorting equipment and later send it to factories for recycling and profit; if the waste is sent directly to incineration plants or the Semakau landfill, waste collectors will instead have to pay the corresponding waste disposal fees.   For construction waste recycling plants, the Environment Agency of Singapore also supports them by leasing out land, and these plants account for 80% to 90% of all construction waste recycled in Singapore. To maximize the recycling of construction waste, the Singapore government has introduced the Construction Demolition Code of Conduct, a set of procedural guidelines to help construction demolition contractors better plan the demolition process.   Government initiatives related to Singapore's focus on reducing waste generation at the source include the Green and Elegant Builder Program and the Green Building Mark Program. The former is a certification program launched in 2009 that rates building practitioners on a variety of aspects ranging from staff management, dust and noise control to public safety. The latter, which began in 2005, is a certification specifically for buildings in tropical regions that assesses the negative environmental impact of buildings and rewards their sustainability performance, assessing five areas: energy efficiency, water conservation, environmental protection, indoor environmental quality and other green features and innovations.   United States   The relevant U.S. law stipulates that any enterprise that causes industrial waste in its production must dispose of it properly by itself and must not dump it without authorization. This limits the amount of construction waste generated at the source, prompting enterprises to consciously seek ways to resource utilization of construction waste. For example, the American Home Builders Association promotes the "Resource Conservation House", whose walls are built with recycled tires and aluminum alloy scraps, most of the steel used in the roof frame is recycled from construction sites, and the panels used are made of sawdust and shredded wood plus 20% polyethylene.   South Korea   The Korean government enacted the Waste Recycling Promotion Act in 2003, which specifies the obligations of the government, dischargers and construction waste disposers and the requirements for capital, size, facilities, equipment and technical capacity of construction waste disposal companies. More importantly, it stipulates the scope and quantity of construction waste recycling products that construction projects are obligated to use and specifies what penalties will be imposed for failure to use construction waste recycling products as required. It is understood that there are currently 373 construction waste disposal enterprises in Korea.   Austria   Austria charges a high disposal fee for construction waste, thus increasing the cost of resource consumption. In addition, almost all enterprises that generate construction waste have purchased construction waste mobile processing equipment, and there are about 130 units (sets) in the country.   The Netherlands   In the Netherlands, 70% of construction waste can already be recycled, but the Dutch government wants to increase this percentage to 90%. Therefore, they have developed a series of regulations to establish a quality control system that restricts the dumping and disposal of construction waste and enforces recycling operations. An important by-product of construction waste recycling in the Netherlands is screened sand. Since sand is easily contaminated, its reuse is limited. In response to this, the Netherlands has adopted a sand recycling network where the picking branch is responsible for efficient sand screening, i.e. sorting according to its contamination level: storing clean sand and cleaning contaminated sand. In order to adapt to the new situation, MingDe XRF intelligent sorting machine, fully capable of fully sorting construction waste, municipal waste after incineration sorting, can effectively sort out the useful part, enhance the utilization of resources and reduce the volume of landfill.    

Design of a 3D vision-guided robot depalletizing system for multi-gauge materials Material Removal Robot Robot Arm Robotic Manipulator Abstract: In industrial manufacturing and logistics, material depalletization by robots is one of the common applications. Material depalletization is a scenario where goods of different gauges (i.e., goods of different sizes, weights, or textures) are loaded on pallets for delivery. Earlier robot depalletization was only applicable to the unloading of single goods and required the goods to be arranged in a fixed order, and the robot did not have perception capability; the vision-guided robot depalletization system described in this paper is equipped with real-time environment perception capability to guide the grasping action, thus solving the problems of variable sizes of objects to be unloaded and irregular placement of multi-gauge material depalletization systems.   Keywords: 3D vision recognition, robot, hybrid palletizing, object positioning, depalletizing algorithm   In industrial manufacturing and logistics, various industrial robots can be used to optimize the flow of goods, and one of the common applications is the depalletization of materials. "Robotic depalletization" usually refers to the process of sequential unloading of materials from pallets using robotic arms and can be used to replace simple but heavy manual labor. In logistics, there are scenarios where goods of different gauges (i.e., different sizes, weights, or textures) are delivered in boxes, as shown in Figure 1. However, early robotic depalletizing systems were mainly controlled manually to complete robot gripping, which was only applicable to the unloading of a single cargo and required the cargo to be arranged in a fixed order, and the robot did not have the perception capability to react to external changes. However, multi-gauge material depalletization systems require robots to have real-time environmental awareness to guide the gripping action because the objects to be unloaded are variable in size and irregularly placed. With the development of various optical sensors, computer vision technology has been gradually introduced into robot grasping tasks to improve the robot's ability to acquire external information. A vision-guided robot depalletizing system usually contains five modules, which are vision information acquisition module, object localization and analysis module, grasping position calculation module, hand-eye coordinate conversion module, and motion planning module, as shown in Figure 2. Among them, the first three modules are the main part of the vision system, responsible for acquiring and processing visual information and providing object poses. The last two modules are mainly used to provide control information to the robot and complete the grasping function. In the following, we will introduce each module, common methods and implementation cases. I. Vision information acquisition module The role of the vision information acquisition module is to capture visual information and provide input for subsequent steps. At present, the commonly used visual inputs include 2D RGB images, 3D point cloud images and combined 2D and 3D RGB-D images. Among them, vision-assisted robotic arm grasping based on 2D RGB images is currently a mature solution in industry, which transforms the robot grasping problem into the problem of doing object target detection or image segmentation on RGB images. However, 2D vision lacks absolute scale information of objects and can only be used under specific conditions, such as scenarios with fixed pallets and known material sizes. For scenarios where the material gauge is unknown, the vision module is required to provide the robot with accurate absolute size information of the object to be grasped, so only 3D point cloud images or RGB-D images with a combination of 2D and 3D can be used. Compared with RGB information, RGB-D information contains spatial distance information from the camera to the object; compared with 3D point cloud images, RGB-D information contains rich color texture information. Therefore, RGB-D images can be used as the visual information input of the multi-gauge material depalletizing system. Object positioning and analysis module The object positioning and analysis module receives the data input from the vision information acquisition module, analyzes the materials present in the scene, and obtains key information such as their position and pose, and then inputs this key information into the grasping pose calculation module. Generally speaking, the material localization problem in robotic depalletizing system can be transformed into a target detection or image segmentation problem in the vision field. The RGB-D vision-based robot grasping solution can first perform 2D target detection or 2D image segmentation on the RGB image for the material, and then fuse the depth map to output the absolute size of the object and the grasping pose; or directly do target detection or segmentation on the 3D point cloud map. The following will be a brief introduction to the related work. 1.2D target detection The input of 2D target detection is the RGB image of the scene, and the output is the class and position of the object in the image, and the position is given in the form of border or center. The methods for target detection can be divided into traditional methods and deep learning based methods. Traditional target detection methods generally use a sliding window to traverse the entire image, with each window becoming a candidate region. For each candidate region, features are first extracted using SIFT, HOG and other methods, and then a classifier is trained to classify the extracted features. For example, the classical DPM algorithm uses SVM to classify the modified HOG features to achieve the effect of target detection. The traditional method has two obvious drawbacks: firstly, it is very time-consuming to traverse the whole image with a sliding window, making the algorithm's time complexity high and difficult to apply to large-scale or real-time scenarios; secondly, the features used often need to be designed manually, making such algorithms more experience-dependent and less robust. 2. Two-dimensional image segmentation Image segmentation can be regarded as a pixel-level image classification task. Depending on the meaning of the segmentation result, image segmentation can be divided into semantic segmentation and instance segmentation. Semantic segmentation classifies each pixel in an image into a corresponding category, while instance segmentation not only performs pixel-level classification, but also differentiates different instances on the basis of specific categories. Relative to the bounding box of target detection, instance segmentation can be accurate to the edges of objects; relative to semantic segmentation, instance segmentation needs to label different individuals of similar objects on the graph. In depalletizing applications, we need to extract the edges of materials precisely to calculate the grasping position, so we need to use instance segmentation techniques. The existing image segmentation techniques can be divided into traditional methods and deep learning based methods.   Most of the traditional image segmentation methods are based on the similarity or mutation of gray values in an image to determine whether pixels belong to the same class. The commonly used methods include graph theory-based methods, clustering-based methods and edge detection-based methods.   Deep learning-based methods have substantially improved the accuracy of 2D image segmentation compared to traditional methods. Typical deep neural network frameworks, such as AlexNet, VGGNet, GoogleNet, etc., add a fully connected layer at the end of the network for feature integration, followed by softmax to determine the category of the whole image. To solve the image segmentation problem, the FCN framework replaces these fully-connected layers with deconvolution layers, making the output of the network from a one-dimensional probability into a matrix with the same resolution as the input, which is the pioneering work of applying deep learning to semantic segmentation. 3. 3D target detection 3D target detection enables robots to accurately predict and plan their behavior and paths by directly computing the 3D position of objects to avoid collisions and violations. 3D target detection is divided into monocular camera, binocular camera, multiocular camera, line surface LIDAR scan, depth camera and infrared camera target detection according to the type of sensor. In general, stereo/multi-vision systems consisting of multi-vision cameras or LiDAR enable more accurate 3D point cloud measurements, where multi-view-based methods can use parallax from images of different views to obtain depth maps; point cloud-based methods obtain target information from point clouds. In comparison, since the depth data of points can be measured directly, the point cloud-based 3D target detection is essentially a 3D point delineation problem and is therefore more intuitive and accurate.   Third, the capture pose calculation module The gripping posture calculation module uses the position posture information of the target object output from the second module to calculate the gripping posture of the robot. Since there are often multiple graspable targets in a multi-gauge material depalletizing system, this module should solve the two problems of "which one to grasp" and "how to grasp". The first step is to solve the "which" problem. The goal of this problem is to select the best crawl target among many crawl targets, and the "best" here often needs to be defined by the actual requirements. Specifically, we can quantify some indicators that have an impact on the crawling judgment according to the actual situation, and then prioritize these indicators. The second step is to solve the problem of "how to catch". We can choose to analyze and calculate the grasping posture by mechanical analysis, or we can first classify the object by learning method, and then select the grasping point according to the classification, or directly regress the grasping posture.   Fourth, the hand-eye coordinate conversion module With the third module, we have obtained a feasible gripping pose. However, the gripping pose is based on the pose in the camera coordinate system, and the gripping pose needs to be converted to the robot coordinate system before motion planning can be performed. In depalletizing systems, hand-eye calibration is usually used to solve this problem. Depending on the camera fixation position, the hand-eye calibration method can be divided into two cases. One is that the camera is fixed on the robot arm and the camera moves together with the arm, called Eye-in-hand, as shown in Figure 3. In this relationship, the position relationship between the robot base and the calibration plate remains constant during the two movements of the robot arm, and the solved quantity is the position relationship between the camera and the robot end coordinate system. The other type of camera is fixed on a separate stand, called Eye-to-hand, as shown in Figure 4. In this case, the attitude relationship between the end of the robot and the calibration plate remains the same during the two movements of the arm, and the solution is the attitude relationship between the camera and the coordinate system of the robot base. Both cases are eventually transformed into a solution problem with AX=XB, and the equation can be transformed into a linear equation using Lie group and Lie algebra to solve for the rotation and translation quantities, respectively. Fifth. Motion planning module This module mainly considers the kinematics, dynamics, mechanical analysis, and motion planning of the robot to plan a feasible motion path that does not collide with the environment. By multiplying the grasping pose in the camera coordinate system obtained by the grasping pose calculation module with the conversion matrix calibrated by the hand-eye coordinate conversion module, we can get the grasping pose in the robot arm coordinate system. Based on this posture, the motion planning can be carried out and the robot arm can be guided to complete the depalletizing task. Therefore, the input of the motion planning module is the starting and target positions of the robot arm, and the output is the motion path of the robot arm.   The complete motion planning algorithm can be split into the following three steps. Step 1: Inverse kinematic solving. In order to avoid problems such as singularities, robotic arm motion planning is generally performed under joint space. Therefore, we should first perform the inverse kinematic solution based on the input poses to obtain the joint values corresponding to the poses. Step 2: Path planning. With the path planning algorithm, we can get the motion path of the robotic arm. The goal of this step is twofold: one is obstacle avoidance, to ensure that the robotic arm does not collide with other objects in the scene during its movement; the second is to improve the operation speed in order to increase the operation efficiency of the system. By planning a reasonable motion path, the running time of a single grasp of the robotic arm can be made shorter, thus improving efficiency. Step 3: Time interpolation. Although we can already get a feasible motion path through path planning, however, this path is composed of one location point after another. When the robot arm is running along this path, it needs to keep acceleration and deceleration, so it will have an impact on the running speed. For this reason, we need to perform temporal interpolation to obtain the velocity, acceleration, and time information for each point on the path as the robot arm moves to that point. In this way, the robot arm can run continuously and smoothly, thus improving efficiency.   Sixth. Implementation Example Based on the above research, a complete vision system consisting of 3D depth camera, lighting system, computer, and vision processing software can be used in the piece box material identification scenario to obtain some special information about real objects, and the information obtained through this system can be used to accomplish some special tasks, such as obtaining the box position through the vision system, which can guide the robot to grasp and obtain the box quantity information as a calibration for the task. The main components of this system, as shown in Figure 5. The 3D camera and light system are mainly used for photo imaging, where the 3D camera can obtain depth data within a certain range. And the digital image imaging is related to the illumination system. The computer, on the other hand, includes general-purpose computing and storage devices for saving images, processing images through specialized vision software, and also for network communication with other systems. The image display facilitates the operator to operate the vision processing software and monitor the system operation. Large-capacity storage is used for permanent or temporary storage of images or other data. Specialized vision software, on the other hand, includes digital image processing, image data analysis, and some special functions.   Generally speaking, a 3D depth camera has a frame rate of 1 to 30 fps, RGB image resolution of 640×480, 1280×960, special 1920×1080, 2592×1944, and a depth range of about 500mm to about 5000mm.   And depending on the price, there are different precision and range. Here is an example of a brand of 3D camera with parameters as shown in Figure 6 and accuracy as shown in Figure 7. With the 3D camera, you can get RGB images and depth images of special scenes, and according to the processing and analysis of these images (see Figure 8), you can get some information about the position, number, and information of objects in the scene. The rectangular box in Figure 9 is the box grabbing position map identified after processing. The order of upper left, lower left, upper right and lower right is "2, 3, 3, 2" respectively, that is, the robot hand will grasp two boxes on the left, three boxes on the left, three boxes on the right and two boxes on the right according to the position information given by the image recognition system. Seventh. Summary In this paper, we have introduced the framework and common methods of 3D vision-guided multi-gauge material robot depalletizing system, and defined several basic modules that the framework needs to have, namely, vision information acquisition module, object localization and analysis module, grasping position calculation module, hand-eye coordinate conversion module, and motion planning module, and explained the main tasks and common methods of each module. In practical applications, different methods can be used to implement these modules as needed without affecting the functions of other modules and the system as a whole.    

Robotics and automation: New robots improve material handling efficiency The advent of robotics means that warehouse workers can spend less time on material handling and more time digging in and serving customers. It also creates opportunities for workers to manage and "train" new equipment. For years, warehouses have been quietly incorporating robots into their material handling operations, but over the past 18 months, the trend has moved into high gear due to a surge in e-commerce demand and a general labor shortage during the new crown epidemic.   This demand is not expected to stabilize anytime soon. The surge in automation could have a ripple effect on the workforce, namely rewriting the job descriptions of many workers in manufacturing, logistics and retail. Not only will robots speed up operations, especially in areas with severe labor shortages, they will also create new jobs for workers who can manage, maintain and "train" automated equipment, suppliers say.   How are robots changing jobs in the warehouse material handling industry? How are people "training" robots to do certain jobs in DC? Currently, most robots are programmed by people who either write the software code or physically guide the robot's arm into the right position. But the next generation of robotics increasingly relies on artificial intelligence (AI) to guide the direction, giving workers complete freedom to perform other DC tasks. How is robotics changing the material handling process in the mine? In the process of mining and transportation, ores are often mixed with wood, steel nails, rags, plastic parts, waste filling pipes and other sundries. These sundries has seriously affected the safety and effectiveness of equipment in the transportation, crushing, grinding and beneficiation. In the past, manual sorting was usually used to remove it, but there are serious safety and occupational health risks in manual sorting, as well as problems such as incomplete manual sorting. The robot for mining can effectively solve the above problems. Through a multi angle, multi-dimensional stereo high-precision camera, the intelligent material removal robot quickly scans the ore on the conveyor The self-developed CRM-CNN foreign object recognition algorithm accurately locates the 3D position of the debris, controls the robot to grab the The self-developed CRM-CNN foreign object recognition algorithm accurately locates the 3D position of the debris, controls the robot to grab the foreign object, and puts it into the foreign object collection box.  

Increase digitalization and intelligent construction, mining sites to achieve the mine into a "gold mine"   Copper smelting enterprises are producing red-hot, so how about the upstream mining situation? To see the reporter's report.     　　Located in the city of Dexing, Jiangxi Province, this copper mine is a very large open-pit copper mine, the reporter saw several electric shovels are loaded ore to the electric wheel, mining site director Xie Wenbo told reporters that after the beginning of the year, the mining site has been maintaining the momentum of high productivity and efficiency, a number of indicators are more than planned to complete.     　　Xie Wenbo, director of a copper mine in Dexing, Jiangxi: In January, the total amount of ore stripped from the mine was 10 million tons, the highest level in the past five years.     　　The reporter noticed that the staff in the central control room issued an automatic driving command, a few kilometers away from the electric wheel will immediately enter the driverless mode, the copper mine's electric wheel driverless project, is the key to the construction of digital mine.     　　Xie Wenbo, director of a copper mining site in Dexing, Jiangxi province, said that the mining vehicle with a capacity of more than 200 tons can complete the whole process of loading, transporting and unloading independently. The successful trial of this project will accumulate valuable experience for us to promote the unmanned driving of the whole process of mining and further enhance the intrinsic safety level of "less people, no one" in mines.     　　Yulong Copper Mine, another copper mine located in the Changdu region of the Tibet Autonomous Region, is the highest-grade open-pit copper mine in China. By the end of 2022, the retained copper metal reserves reached 5,751,600 tons. After the start of the year, the local temperature remains cold, and the company has made sufficient preparations to guarantee positive production mining in the open pit in winter.     　　Fan Wentao, chairman of a copper company in Tibet: After the Spring Festival, we took a series of winter production safety measures to strengthen the frequency of equipment overhaul and maintenance to ensure the normal supply of ore in extreme weather. Compared with the past, the "intelligent mine" construction is put into operation to effectively improve the mine supply index.     　　Ltd. Chairman Liang Yanbo: The company has a copper ore processing capacity of 22.3 million tons per year, and the mines are currently producing at full capacity.     　　The reporter also learned in the interview, copper prices, for some of the copper companies have their own mines in response to market price fluctuations will have more advantages.     　　A copper company in Qinghai general manager Zhou Xuan: 70% of our main source of raw materials is relying on its own mines, our production of stable operation has a great guarantee.     　　Fan Xiaohui, deputy general manager of the futures department of the trading division of a copper company in Jiangxi, China: the processing fees of the enterprise is a relatively limited source of profit, but with the rise in asset prices, the benefits that can be produced from the mine is the most core source of profit.   Translated with www.DeepL.com/Translator (free version)

    What is the working principle of color sorter?   1、Selected materials from the top of the hopper into the machine, through the vibration of the vibrator device, selected materials along the channel transmission, into the sorting chamber of the observation area, and from the sensor and the background plate between the passage.   2、Under the action of light source, according to the intensity of light and color change, the system produces output signal to drive the solenoid valve work to blow out the different color particles to the waste hopper, and the good selected materials continue to fall to the finished product hopper, so as to achieve the purpose of selection.

Application of Artificial Intelligence in Coal Mine Robotics   Abstract With the rapid development of artificial intelligence technology, its application in coal mines has become more and more extensive. In the process of coal mine production, the urgency of the demand for robot replacement has accelerated the industrial application of coal mine robots and the application of artificial intelligence technology in coal mine robots. The application of artificial intelligence technology in coal mine robots is analyzed and explored, the main research contents of artificial intelligence technology and its application in industry are introduced, the current situation of the application of artificial intelligence in coal mine production is analyzed, the concept of applying artificial intelligence technology to coal mine robots effectively is elaborated, and the prospect of the development of artificial intelligence in coal mine robots is prospected.   Keywords artificial intelligence, coal mine robot, intelligent perception, intelligent decision making, intelligent monitoring，material removal robot 0 Introduction The underground production and operation process of coal mine has the problems of many people going down the shaft, high risk of disaster, high accident rate, harsh operating environment and serious environmental pollution [1]. Facing the high-risk underground operations, coal mine robots become one of the important ways to achieve the goal of safe and efficient underground coal mine production. Coal mine robots can assist or replace people to complete some dangerous mining operations and achieve safe and efficient production in coal mines. In order to achieve "no one is safe", robots are the trend to replace miners in underground operations.   With the strategy of "Made in China 2025", "German Industry 4.0" and "American Industrial Internet", 5G communication, Internet of Things, big data, cloud computing and artificial intelligence The gradual maturity of technologies such as 5G communication, Internet of Things, big data, cloud computing and artificial intelligence has greatly promoted the transformation and upgrading of China's traditional manufacturing industry [2]. As an emerging science and technology, artificial intelligence can make computer technology more accurate, fast, and convenient to complete complex scientific calculations that the human brain is incapable of undertaking, and achieve partial replacement, extension, and enhancement of the human brain, thus creating intelligent machines that can complete complex and dangerous operations instead of humans [3].   Future coal mine production will develop toward unmanned, autonomous, intelligent, and efficient, in which artificial intelligence technology will play an irreplaceable role and diverse artificial intelligence technologies will be applied to coal mine robots [4]. Although the current application of artificial intelligence in the field of industrial coal mines is still in the fumbling period, however, with the increasingly widespread application of artificial intelligence technology in the field of coal mines, the construction of unmanned operation mines is inevitable [5].   1 The urgent problems in the coal industry China's coal industry has experienced more than 40 years of development, and the mining of coal mineral resources gradually tends to be intelligent, but there are still some bottlenecks that need to be solved.   1.1 Technology and equipment need to be upgraded Although the mining and transportation of coal in China have gone through the stages of digitalization, automation and informatization, the overall technical level and production equipment are still lower than those of developed countries [6].In 2019, the former State Administration of Coal Mine Safety Supervision proposed to accelerate the industrialization and application of coal mine robots for digging, coal mining, transportation, safety control, support and rescue. The current coal mine robot is no longer just completing simple repetitive operations, it can sense the surrounding environment and give real-time feedback to the outside world, but it does not yet have independent thinking, identification, reasoning, judgment and decision-making capabilities, and still needs human participation to complete some complex work tasks.   1.2 Serious safety hazards The coal industry is a high-risk industry, and there are various hazards in every step of production, water, fire, gas, coal dust, geological formations and other disasters are frequent, and the unknown complex underground environment seriously threatens the life safety of underground operators. Although the intelligent monitoring and early warning technology of coal mines based on the Internet of Things, big data and cloud computing has largely reduced the occurrence of accidents and guaranteed the safe production of coal mines, there are still many problems. The poor accuracy and sensitivity of sensors lead to incomplete and untimely collection of precursor information; monitoring systems are independent of each other and have a single function, and the integration and application integration depth of the cloud platform is not deep enough; monitoring system database security is weak; monitoring equipment lacks deep learning as well as self-adaptive capability [7].   1.3 Serious environmental pollution Coal mines produce coal dust during the mining process, and also produce harmful gases such as carbon monoxide and carbon dioxide to pollute the atmosphere [8]. At the same time, the production effluent from coal mining contains a large amount of heavy metals and acidic substances, which can easily seep into the soil or enter the groundwater to pollute the geology and water sources. Coal mining projects will encroach on a large amount of vegetation and agricultural land, and the land is prone to collapse after mining leading to the destruction of the surface layer [9].   2 The main research content of artificial intelligence 2.1 Pattern recognition Pattern recognition in artificial intelligence technology uses the powerful data collection, analysis and processing functions of advanced computer technology to simulate human perception and recognition of the external environment by setting the corresponding programs in advance. Intelligent robots incorporating pattern recognition can better simulate human sensory abilities, recognize characters, sounds, images, scenes and their fused information with high accuracy, and accurately perceive and model the surrounding environment through the acquisition of multi-source information [10].   Machine vision in artificial intelligence technology, as one of the most important environmental perception modalities, simulates human visual capabilities to improve the robot's understanding of the downhole environment, operational processes, and feedback phenomena. Intelligent robots incorporating machine vision are, first, able to adapt well to the downhole operating environment and collaborate well with other artificial devices; second, able to capture more information about the external landscape and to understand and dig deeper into the content of images through stereo vision, visual inspection, and dynamic image analysis techniques; and third, able to judge the underground feedback phenomena of the operating process and feed back information about the robot's status to the motion control system [11].   2.2 Expert system Expert systems are technologies that model the knowledge and experience of human experts and are used to solve problems such as system decisions, processes, and failures. Through artificial intelligence techniques, knowledge systems are created for downhole systems that simulate humans to solve practical problems encountered during operations. Human experts can predict system failures, determine failure points and generate troubleshooting solutions based on the current state of the system, such as equipment displays and sounds, operational data parameters, and the state of the product, when solving real-world problems. Therefore, expert systems are commonly used for fault prediction, diagnosis and troubleshooting. In addition, in the manufacturing industry, expert systems are also used for production planning decisions, production process optimization, production coordination, and optimization of equipment parameters.   2.3 Machine Learning Machine learning in artificial intelligence technologies mimics human learning capabilities through model frameworks and algorithms to automatically extract intrinsic laws through training data, environmental information, and feedback to improve system performance and enhance environmental adaptation and robustness. Robots that incorporate machine learning have human-like law extraction and knowledge summarization capabilities to identify effective information from the large amount of information resources collected and learn to improve their own intelligence. Machine learning technology can effectively solve a series of problems in unexpected situations and largely reduce labor and production costs [12].   2.4 Distributed Artificial Intelligence Distributed artificial intelligence system coordinates the scheduling and control of heterogeneous multi-intelligent body systems by scientifically and rationally combining artificial intelligence and computer technology, so as to enhance the performance of the artificial intelligence system, improve the task execution capability, and increase the efficiency of the cooperative work of each independent system in the intelligent robot. When the intelligent robot encounters some unexpected situations, it can still guarantee each subsystem to carry out normal work. The current distributed artificial intelligence system is still in the initial stage of research and development, and the technical difficulty lies in how to coordinate the operation rules of different systems [13].   3 Status of application of artificial intelligence in coal mine robots 3.1 Application of artificial intelligence in the motion control of coal mine robots In order to ensure that coal mine robots can operate properly in complex underground environments, research scholars have applied artificial intelligence technologies such as expert systems and artificial neural networks to robot motion control methods, algorithms and collaborative operations. By simulating human expert thinking and knowledge level, coal mine robots can solve some complex multidimensional nonlinear problems, reduce the amount of operations for dynamical system analysis, parameter setting and data processing, and improve control efficiency and accuracy.   Wang Nian et al [14] researchers designed an intelligent mine robot based on embedded ucos and used GSM network to realize remote control of the device; Zhang Chuancai et al [15] researchers used BP neural network to establish a measurement method to determine the robot turning angle based on motor speed and running time, which can provide angle parameters for robot path planning; Wang Xuesong et al [16] researchers personnel approximated the kinetic uncertain parameters based on the improved Elman neural network and sent control commands for the coal mine robot servo system using a neuro-fuzzy controller; Song Xin et al [17] researchers applied neural networks in the field of robot control to accomplish actions such as robotic arm multi-joint coupling control, end trajectory planning, and hydraulic valve control.     3.2 Application of Artificial Intelligence in Intelligent Perception and Danger Prediction of Coal Mine Robot Mining inspection robots realize all-round perception of underground environment information by carrying various sensors, real-time monitoring of instrument and equipment failure, personnel safety and disaster information such as gas, coal dust, water and fire, and timely issuance of early warning to reduce the occurrence of coal mine accidents. For several technical difficulties such as inaccurate identification and untimely monitoring in complex underground environments, researchers use deep learning, pattern recognition, and expert system technologies to further enhance the robot's accurate identification and real-time monitoring of underground emergent hazards.   Researchers in Lu Wanjie et al [18] used deep learning algorithms based on convolutional neural networks to model and train coal mine equipment so that the underground inspection robot could accurately identify the type of coal mine equipment; researchers in Zhang Fan et al [19] proposed a mining image reconstruction method based on residual neural networks for the disturbing effects of underground noise on the visualized operating environment, which effectively improved the clarity of monitoring images and Nie Zhen et al [20] used a genetic algorithm based on BP neural network to build a tunnel gas environment intelligent detection system and obtain real-time data of gas concentration distribution on different tunnel sections in the path of coal mine inspection robots; Pan Yue et al [21] used BP neural network to establish a diagnostic model for fan faults and establish a mapping between fan fault types and fan rotor vibration frequency bands, thus realizing fan fault diagnosis. relationship, and then achieve fan fault diagnosis; Yan Junjie et al [22] researchers based on artificial neural network to establish a diagnostic model for coal mine machinery gear faults, using the input signal to train the neural network model, classify the output signal, and then determine the gear fault.   3.3 Application of artificial intelligence in autonomous positioning navigation and map construction for coal mine robots Achieving autonomous positioning and navigation in complex unstructured coal mine environments requires consideration of both the inability of GPS technology to be applied directly downhole and the need to overcome interference from external factors such as dust, temperature, humidity, noise, and airflow, which places higher demands on autonomous and accurate positioning and navigation technology for robots in restricted and closed environments downhole. Map construction, positioning navigation, path planning, and real-time obstacle avoidance of coal mine robots based on artificial intelligence technology have become hot spots for applied research.   Bai Yun [23] proposed variable structure fuzzy neural network and applied it to the environment sensing process of snake underground rescue robots, fusing multi-source sensor data to achieve obstacle recognition and environment modeling of snake robots in harsh environments; Fu Hua et al [24] researchers used artificial neural network model to model and dynamically describe the workspace of intelligent coal mine monitoring system, using neural network model for robot obstacle avoidance path planning; Zhang Yaofeng et al [25] researchers used Elman network-based compensation for ultrasonic sensor measurement error of underground robot, which greatly improved the accuracy of ultrasonic ranging and obstacle detection; Zhai Guodong et al [26] researchers summarized binocular vision technology in coal mine rescue robot to obtain accident scene information and achieve autonomous obstacle avoidance and path planning, including pattern classification and recognition, visual measurement and 3D reconstruction, combined measurement and localization, and visual servo control; Ma Hongwei et al [27] researchers constructed a depth camera-based machine vision system and proposed a depth vision-based navigation method, in which the robot is equipped with an RGB-D depth camera for data acquisition to achieve map creation and autonomous navigation.   4 Research on intelligent coal mine robots There are various kinds of artificial intelligence technologies, and the main research contents applied to the field of coal mine robots include multimodal fusion intelligent perception, knowledge learning and intelligent decision making, and intelligent control cooperative operation. Through perception, learning, decision making, and collaborative control, the intelligent development of coal mine robots is realized.   4.1 Multi-modal fusion intelligent perception The coal mine robot is equipped with various explosion-proof, high-precision and high-reliability sensors to build an intelligent perception system with multimodal fusion of vision, hearing, smell, touch, etc., to complete intelligent recognition and analysis, abnormal sound recognition, abnormal temperature monitoring, smoke detection, harmful gas concentration detection, autonomous obstacle avoidance, autonomous grasping and other operations.   (1) Research on machine vision recognition and visual detection technologies in coal mine application scenarios. Through image processing and understanding, the robot is able to, firstly, identify and monitor equipment digital meters, LCD screens, indicators, valves, etc.; secondly, detect pipeline liquid dripping, tape running and cracking; thirdly, carry out personnel intrusion, personnel on duty, personnel dressing detection; fourthly, identify and track foreign objects such as gangue, anchor rods, road logs, iron pipes, etc. that appear on the tape.   (2) Research on technologies such as robot hearing i.e. sound detection and recognition in coal mine application scenarios. Using high-sensitivity sound pickup sensor, high-speed DSP digital signal processor, combined with adaptive dynamic noise reduction processing technology, audio feature extraction and detection model algorithm recognition technology to identify abnormal sound in the mine.   (3) Research on intelligent recognition technology for robotic olfaction i.e. gas detection in coal mine application scenarios. Accurate detection of methane, hydrogen sulfide, carbon monoxide, oxygen and other gases in the environment and whether smoke exceeds the limit, timely detection of gas leaks and early warning of fires.   (4) Research on haptic technology for robots in coal mine application scenarios. Collect the temperature of objects such as motors, pumps, bearings, rollers, tapes, etc. through contact or non-contact and analyze the data; through force sensing equipment, real-time monitoring of contact force, gripping force, operating force, internal stress, and realize force sensing and safety control.   4.2 Knowledge learning and intelligent decision making In view of the current problems of incompatible coal mine robot system protocols and lack of information sharing and integration, we will deeply integrate coal mine robots with new generation information technology, build a generalized, standard and flexible system for mutual learning and knowledge sharing of coal mine robots, and break through the technical bottlenecks of coal mine robot scene understanding, safety detection, precise positioning, autonomous perception and efficient navigation. Realize cloud-based online services for common technologies of coal mine robots to solve the limitations of individual robots and improve the intelligent decision making of coal mine robots.   (1) Establish a learning and generalization framework that integrates the individual and the whole. At the individual level, a single robot integrates sensing, decision-making, control, collaboration, and human-robot interaction information during operation, and conducts incremental, real-time, online training through an artificial intelligence learning framework represented by neural networks to dynamically adjust the robot's operational state and achieve full-cycle optimal control and decision-making. At the overall level, multi-robots upload and distribute their learned knowledge among themselves through the new generation of information technology, so that when a robot faces a brand-new operational task, it can quickly familiarize itself with the operational characteristics with the knowledge results of other robots, reduce re-learning time, and enhance the overall system's task flexibility and adaptability.   (2) Establish an operation mode in which the robot body and the cloud are integrated. Breakthrough the traditional robot R&D and integration model, and realize a new robot R&D and integration route that integrates the local lightweight robot body with the high-performance data processing capability in the cloud with the help of "5G + cloud computing". The algorithms that require strong computing power, such as intelligent environment perception, pattern recognition, map construction, and autonomous navigation, are moved to the cloud, and the local robot uploads the data of on-board sensors and actuators to the cloud in real time, and optimizes the calculation of perception, modeling, and execution through the powerful data processing and computing power of the cloud. The result is sent to the local robot in real time, which reduces the computational burden of the local robot and shifts more hardware resources to the sensor and execution side to achieve a lightweight, streamlined, and high-performance operational robot design.       4.3 Intelligent control of cooperative operation Integrating deep learning and laser/visual SLAM technologies into coal mine robots, combined with intelligent sensing system of multimodal fusion, realizes the functions of autonomous movement, precise positioning, position adjustment, intelligent operation planning, autonomous operation and intelligent disaster sensing of coal mine robots in complex mine environment, and realizes intelligent collaborative control of detection, digging and support operation processes.   (1) Integrating neural network technology into the collaborative operation control and planning of multiple coal mine robots. Self-organization, self-grouping and self-coordination of mobile robots in mines to achieve integration of heterogeneous equipment. Through intelligent task decomposition, task assignment, and load balancing technologies, a swarm of robots in complex environments in mines is formed, and technologies such as autonomous navigation in underground space, multi-sensor state sensing, intelligent operation planning, and multi-robot collaborative control are applied to realize efficient collaborative operations among multiple robots in workface digging, drilling, extraction, transportation, and support.   (2) Extending the mode of human interaction with a single robot to human interaction with multiple robot groups, and realizing the intervention and collaboration of operators on robot groups. During the operation of coal mine robots, each heterogeneous robot with different functions forms a complex multi-robot collaborative swarm. At the same time, the multi-robot collaborative swarm needs to be able to collaborate with the operator in depth. Through AI technology, we can break through the simple "command-execution-display" mode of existing human-robot interaction technology, and integrate human intervention into the control cycle to realize a new mode of human-robot interaction with "human in the loop", and realize the "unmanned underground system group + unmanned underground system group + unmanned underground system group". underground unmanned system group + underground operator" mode of operation, to improve the overall system's operational efficiency, task flexibility and robustness.   In order to achieve the goal of smart coal mine, we will carry out research on "coal mine robot +", "coal mine robot + 5G" to realize comprehensive sensing and interconnection, full domain information sharing and multi-channel human-robot interaction; "coal mine robot + cloud computing "Coal mine robot + cloud computing" realizes the compatibility of lightweight and low-cost robot ontology and high-performance learning computing capability; "coal mine robot + big data" realizes dynamic prediction, information integration, and provides data basis for robot evolutionary learning; "coal mine robot + AI "Mine robot + AI" realizes intelligent autonomous perception, optimal analysis and decision making, and knowledge learning evolution, thus forming a complete intelligent system of three-dimensional perception, autonomous learning and cooperative control in the mine.   5 Future Outlook Artificial intelligence has been widely applied in the field of coal mine robotics, and more research results have been achieved. However, as an emerging frontier technology, artificial intelligence still has limitations.   (1) The current AI technology is mainly oriented to a single task, and a general AI framework that can face multiple tasks has not yet been realized. For example, the models trained for image recognition cannot be used for sound detection and recognition; the algorithm framework for recognizing a specific target object cannot be extended to the recognition of arbitrary target objects, and the data set needs to be constructed and retrained when a new classification target appears. This feature limits the application of AI in complex task scenarios.   (2) Artificial intelligence algorithms need to rely on a large amount of data, and operations such as data collection, processing, calibration and alignment need to be done manually, which is less efficient. How to use a smaller amount of data to achieve higher performance has become one of the current research hotspots of artificial intelligence methods.   (3) There are many types of coal mine robots, and there exist a large number of sensing devices, driving devices and actuating devices. The data formats of each device are diverse, and it is difficult to form a unified data interface, making the data between each system independent of each other. The incompatible data makes it difficult for the AI system to coordinate the robots in each part of the coal mine production process, and to obtain enough data to form a closed-loop unified plan for the entire production process.   (4) The environment in which coal mine robots operate is extremely dangerous, so current AI systems alone cannot guarantee a high level of safety and stability. How to integrate the AI system with the operator's manual intervention and integrate human intervention into the whole AI system's operation loop becomes one of the key elements to be addressed in the next step.   In the future, AI systems applied to coal mine robots will develop toward generalization, low overhead, unification, and human-machine collaboration, with the emergence of a general AI algorithm framework for multiple tasks that continuously learns and evolves online using small amounts of data and low-cost training methods, capable of integrating key data from all aspects of coal mine production for integrated computing and scheduling, and able to collaborate with each other and humans to achieve It is capable of collaborating with humans to achieve efficient, safe, and autonomous coal mine production.   6 Conclusion With the development of artificial intelligence technology, the coal mining industry will see a major change. With the efficient model building, parallel computing, and planning capabilities of AI, the intelligence and automation of coal mine robots will reach a new level, truly realizing the unmanned and safe requirements of coal mine production. At the same time, artificial intelligence will enable a significant increase in coal mine production efficiency and promote the safe, healthy and sustainable development of the coal mine industry.