Picking system and picking method

Abstract

The sorting system of one aspect of the present disclosure includes: a sorting device that performs a sorting process for each kind of material in a plurality of recycling processes, the sorting process sorting a mixture containing a plurality of kinds of materials; a computing device that includes an acquisition unit that acquires information, a computing unit that computes a sorting condition of the sorting device based on the acquired information, and a transmission unit that transmits the computed sorting condition to the sorting device; and an information storage system that stores a plurality of recycling history information corresponding to the plurality of recycling processes, the acquisition unit acquires information associated with a mixture to be sorted from the sorting device, the acquisition unit acquires the recycling history information associated with the mixture to be sorted from the information storage system, and the computing unit computes the sorting condition based on the information associated with the mixture to be sorted acquired from the sorting device and the recycling history information associated with the mixture to be sorted acquired from the information storage system.

Description

Technical Field

[0001] This disclosure relates to picking systems and picking methods. Background Technology

[0002] Conventionally, techniques for selecting mixtures containing multiple types of objects for each type of object are known. In such techniques, for example, there are electrostatic picking devices that charge the multiple types of objects contained in the mixture and then select specific objects for reuse through electrostatic picking. As techniques related to reuse processes such as electrostatic picking, the techniques described in Patent Documents 1 and 2 below are known, for example.

[0003] Patent Document 1's recyclable metal counting system is a circular economy system that tracks the total amount of metal-containing materials collected by dismantling companies for each part, the recycling companies, dismantling companies, steel and iron manufacturers, aluminum smelters, and non-ferrous metal smelters processing parts to recover recyclable metals, and returning them to the manufacturing company. The system clearly displays the total amount of metal-containing materials, the various types of recyclable metals recovered, and their quantities. This recyclable metal counting system includes a mainframe computer and multiple client computers for each company. The mainframe computer stores the expected amount of metal-containing materials collected by dismantling companies for each part, the expected amount brought in by each company, and the expected amount of recyclable metals processed and recovered by each company, based on the total amount of metal-containing materials. The multiple client computers for each company update the expected amounts stored on the mainframe computer that are relevant to their own business operations.

[0004] The recycling device of Patent Document 2 includes: an input unit for reading first product information attached to the device; a storage unit for storing a device recycling and dismantling information database containing information for comparison, dismantling, and classification of the device corresponding to the first product information attached to the device; and a comparison unit for comparing the first product information of the device obtained by the input unit with the device corresponding to the device recycling and dismantling information database.

[0005] Existing technical documents

[0006] Patent documents

[0007] Patent Document 1: Japanese Patent Application Publication No. 2011-164792

[0008] Patent Document 2: Japanese Patent Application Publication No. 2002-197147 Summary of the Invention

[0009] In the aforementioned electrostatic picking device, sufficient data needs to be collected to achieve high-precision picking. However, when a customer has just introduced the electrostatic picking device or when there are significant changes in the materials used in the electrostatic picking device, time is spent on data collection, resulting in a decrease in picking efficiency during the period until sufficient data is collected.

[0010] The technologies described in the aforementioned patent documents 1 and 2 do not describe the use of information obtained in other processes in the picking process, thus failing to improve the accuracy of the picking process.

[0011] This disclosure was made in view of the following circumstances, and its object is to provide a picking system and picking method that can improve the accuracy of picking mixtures by using information from the recycling process in the picking process.

[0012] This disclosure was made to solve the aforementioned problems. One solution of this disclosure provides a picking system comprising: a picking device for performing a picking process in which picking of a mixture of multiple types of materials for each type of material in a plurality of reuse processes; a computing device comprising an information acquisition unit, a computing unit for calculating picking conditions of the picking device based on the information acquired by the acquisition unit, and a sending unit for sending the picking conditions calculated by the computing unit to the picking device; and an information storage system for storing a plurality of reuse history information corresponding to the plurality of reuse processes, wherein the acquisition unit acquires information associated with the mixture to be picked from the picking device, acquires the reuse history information associated with the mixture to be picked from the information storage system, and the computing unit calculates the picking conditions based on the information associated with the mixture to be picked acquired from the picking device and the reuse history information associated with the mixture to be picked acquired from the information storage system.

[0013] Another aspect of this disclosure provides a picking method, comprising: a step of a computing device obtaining information associated with a picking object mixture comprising multiple types of materials from a picking device; a step of the computing device obtaining the reuse history information associated with the picking object mixture from an information storage system storing reuse history information corresponding to multiple reuse processes; a step of the computing device calculating picking conditions based on the information associated with the picking object mixture obtained from the picking device and the reuse history information associated with the picking object mixture obtained from the information storage system; a step of the computing device sending information representing the picking conditions to the picking device; and a step of the picking device picking the picking object mixture comprising multiple types of materials for each type of material.

[0014] According to this disclosure, the accuracy of picking mixtures can be improved. Attached Figure Description

[0015] Figure 1 This is a block diagram illustrating an example of the electrostatic sorting system 1 in the first embodiment.

[0016] Figure 2This is a block diagram illustrating an example of the information storage system 500 in the first embodiment.

[0017] Figure 3 This is a block diagram showing an example of the learning unit 308 in the first embodiment.

[0018] Figure 4 This is a diagram showing an example of the electrostatic sorting device 100 in the first embodiment.

[0019] Figure 5 This is a block diagram illustrating an example of the functional structure of the first arithmetic unit 204 in the first embodiment.

[0020] Figure 6 This is a timing diagram illustrating an example of the operation of the electrostatic sorting system 1 in the first embodiment.

[0021] Figure 7 This is a flowchart illustrating an example of the processing of the edge server device 200 and the computing device 300 in the first embodiment.

[0022] Figure 8 This is a diagram showing an example of a gravity-type sorting device 600 in the second embodiment.

[0023] Figure 9 This is a diagram showing an example of the optical picking device 700 in the third embodiment. Detailed Implementation

[0024] The picking system and picking method of the present invention will be described below with reference to the accompanying drawings.

[0025] (First Embodiment)

[0026] Figure 1 This is a block diagram illustrating an example of the electrostatic picking system 1 in the first embodiment. The electrostatic picking system 1 includes, for example, multiple electrostatic picking devices 100A, 100B, 100C, ..., multiple edge server devices 200A, 200B, 200C, ..., a computing unit 300, a database device 400, and an information storage system 500. In the following description, when referring to the multiple electrostatic picking devices collectively, it will be referred to as "electrostatic picking device 100," and when referring to the multiple edge server devices collectively, it will be referred to as "edge server device 200." Furthermore, the electrostatic picking system 1 of this embodiment includes the edge server device 200, but is not limited thereto. The electrostatic picking system 1 may not include the edge server device 200 and may connect the electrostatic picking device 100 and the computing unit 300 via a communication network NW.

[0027] The electrostatic sorting device 100, edge server device 200, computing device 300, database device 400, and information storage system 500 have communication interfaces (not shown) such as NICs (Network Interface Cards) or wireless communication modules for connecting to communication networks NW such as the Internet. The communication network NW may include, for example, general networks such as the Internet, as well as proprietary networks such as local 5G or WiFi (registered trademarks).

[0028] Furthermore, in this embodiment, an electrostatic picking device 100 that uses the difference in charge of each category of resin (plastic) contained in the mixture to perform picking will be described as a picking device, but it is not limited to this. The electrostatic picking system 1 of this embodiment can be a picking device that picks mixtures containing multiple types of materials for each type of material. For example, the electrostatic picking system 1 of this embodiment can be applied to a picking device that picks a specific object from a mixture containing metal or glass, and it can also be applied to a picking device that picks a specific object from a mixture containing food.

[0029] The electrostatic picking device 100 performs a picking process in multiple recycling steps, specifically picking a mixture of resin flakes (hereinafter also referred to as raw materials) containing multiple types of resin flakes for each type of resin. The picking objects of the electrostatic picking device 100 are reusable materials. These reusable materials include various plastic flakes with different electrical properties. The electrostatic picking device 100 includes, for example, a detection unit 102, a picking unit 104, a receiving unit 106, and a sending unit 108. The detection unit 102 detects sensing data. This sensing data represents the state of the mixture. The picking unit 104 performs the action of picking plastic flakes from the mixture. The receiving unit 106 receives control commands from the edge server device 200. The sending unit 108 sends the sensing data to the edge server device 200.

[0030] The edge server device 200 is, for example, an information processing device capable of communicating with the electrostatic sorting device 100 and the computing device 300. The edge server device 200 is, for example, installed corresponding to the electrostatic sorting device 100 located in a factory. The edge server device 200 and the electrostatic sorting device 100 are connected, for example, via a communication line such as a LAN line installed within the factory.

[0031] The edge server device 200 includes, for example, an acquisition unit 202, a first arithmetic unit 204, a receiving unit 206, a transmitting unit 208, and a control unit 210. The first arithmetic unit 204 and the control unit 210 are functional units implemented by a processor such as a CPU (Central Processing Unit) executing a program stored in a program memory. The acquisition unit 202 is connected to the electrostatic sorting device 100 via a LAN line, for example. The acquisition unit 202 acquires mixture information, operating status information, and sorting result information. The mixture information is information related to the mixture in the electrostatic sorting device 100. The mixture information may include, for example, at least one of the following: material type, composition, and particle size. The operating status information indicates the operating status of the electrostatic sorting device 100. The operating status information may include, for example, the voltage value used to generate the electrostatic field, the position of the separator, etc. The sorting result information indicates the sorting result of the electrostatic sorting device 100. The sorting result information may include, for example, the purity and recovery amount of the sorted resin.

[0032] The first calculation unit 204 performs a first calculation process on at least one of the mixture information, operation status information, and picking result information acquired by the acquisition unit 202. The first calculation process may, for example, be the process of extracting at least a portion of the mixture information, operation status information, and picking result information. The first calculation process may, for example, be the process of reducing the amount of data sent to the calculation device 300 by organizing the mixture information, operation status information, and picking result information. The transmission unit 208 transmits the information after the first calculation process performed by the first calculation unit 204.

[0033] The receiving unit 206 receives picking conditions from the arithmetic unit 300. The control unit 210 controls the electrostatic picking device 100 based on the picking conditions received by the receiving unit 206. The picking conditions are information that serves as target values ​​for causing the electrostatic picking device 100 to operate. Specifically, the picking conditions are target values ​​for the voltage applied to the electric field generating unit 130, target values ​​for the position of the separator plates, etc.

[0034] The computing device 300 is, for example, an information processing device that functions as a server to process requests from edge server devices 200 and send processing results to edge server devices 200. The computing device 300 is, for example, one device corresponding to multiple edge server devices 200.

[0035] The arithmetic unit 300 includes, for example, an acquisition unit 302, a second arithmetic unit 304, a learned model 306, a learning unit 308, and a transmission unit 310. The second arithmetic unit 304 and the learning unit 308 are functional units implemented by a processor such as a CPU executing a program stored in a program memory.

[0036] The acquisition unit 302 acquires information associated with the mixture of objects to be picked from the electrostatic picking device 100 and retrieves reuse history information associated with the mixture of objects to be picked from the information storage system 500. The mixture of objects to be picked is a mixture of objects to be picked supplied to the electrostatic picking device 100. The second calculation unit 304 calculates picking conditions based on the information associated with the mixture of objects to be picked acquired from the electrostatic picking device 100 and the reuse history information associated with the mixture of objects to be picked acquired from the information storage system 500. The learned model 306 is a machine learning model used in the second calculation process. The learning unit 308 performs a process to enable the learned model 306 to learn. The sending unit 310 sends the picking conditions calculated by the second calculation unit 304 to the electrostatic picking device 100 via the edge server device 200. Furthermore, the second calculation unit 304 can calculate the picking conditions without using the learned model 306 by using calculation processing of formulas and calculation processing of rule bases.

[0037] Database device 400 is an information processing device equipped with a data storage device and a processor for constructing a database (DB). Database device 400 stores, for example, a user database 410, a picking device database 412, and a learned model 414. User database 410 is, for example, a database that maps user information (such as users, business types, and products of the electrostatic picking device 100) to the learned model 414. Picking device database 412 is a database that maps mixture information, operation status information (sensing data), and picking result information of each electrostatic picking device 100. Learned model 414 contains information representing learning data, processing parameters used to determine the machine learning model, and other information used to determine the learned model. Learned model 414 can be constructed according to each type of resin sheet, each composition, and each particle size. Learned model 414 can be constructed according to each user, each industry, and each product.

[0038] The electrostatic picking system 1 in this embodiment includes multiple electrostatic picking devices 100A, 100B, and 100C, multiple edge server devices 200A, 200B, and 200C corresponding to each of the multiple electrostatic picking devices 100A, 100B, and 100C, and a computing unit 300 corresponding to each of the multiple edge server devices 200A, 200B, and 200C. The second computing unit 304 can calculate the picking conditions of one electrostatic picking device 100 based on multiple picking conditions sent by the multiple edge server devices 200A, 200B, and 200C. The electrostatic picking system 1 is not limited to this; for example, there may be only one electrostatic picking device 100, one edge server device 200, and one computing unit 300.

[0039] The information storage system 500 includes an information processing device that stores multiple historical reuse information corresponding to multiple reuse processes. These multiple reuse processes include, for example, a picking process, a raw material manufacturing process, a product manufacturing process, a usage process, a recycling process, a sorting process, and a crushing process. The multiple reuse processes include a picking process performed by the electrostatic picking device 100, an upstream process preceding the picking process, and a downstream process following the picking process. An upstream process is, for example, a crushing process. A downstream process is, for example, a raw material manufacturing process.

[0040] Figure 2 This is a block diagram illustrating an example of the information storage system 500 in the first embodiment. The information storage system 500 includes multiple recycling operator devices (510A, 510B, 510C, D, 510E, 510F) corresponding to multiple recycling processes, and an information management device 520 for managing recycling history information obtained from the multiple recycling operator devices. The recycling operator devices are, for example, at least one of a crushing operator device 510A, a raw material manufacturer device 510B, a product manufacturer device 510C, a user device 510D, a recycling operator device 510E, and a sorting operator device 510F. Furthermore, when the crushing operator device 510A, the raw material manufacturer device 510B, the product manufacturer device 510C, the user device 510D, the recycling operator device 510E, and the sorting operator device 510F are collectively referred to as "recycling operator device 510".

[0041] The reuse operator's device 510 includes, for example, an acquisition unit 512, an information processing unit 514, and a transmission unit 516. The acquisition unit 512 and the transmission unit 516 are, for example, communication circuits that communicate with other devices via a communication network NW. The information processing unit 514 is, for example, a functional unit implemented by a CPU executing a program.

[0042] The acquisition unit 512 obtains recycling history information related to reusable materials from the information management device 520. For example, the computing device 300 (the picking operator) obtains recycling history information related to mixed plastics from upstream processes in the picking process. Recycling history information may be, for example, information indicating the correspondence between information identifying reusable materials from upstream processes and information identifying reusable materials processed through recycling processes. Recycling history information may include detailed information about the reusable materials. Detailed information about the reusable materials may include, for example, at least one of the following: composition of the plastic sheet, particle size, color, additives, surface finish, surface contamination level, or information related to the number of times it has been reused.

[0043] Information Processing Department 514 creates and reuses historical information.

[0044] The sending unit 516 will reuse the historical information and send it to the information management device 520.

[0045] Blockchain technology is used to link recycled materials. Blockchain technology is a mechanism that uses multiple recycling operators' devices 510 to distribute and store the same data. Recycling operator devices 510 and information management devices 520 can, for example, include information related to raw materials using the same single plastic, information related to products or parts, and information related to mixtures in the same recycling history information. Each recycling operator can confirm the association between single plastics, raw materials, products or parts, recyclables, sorted materials, and shredded materials by referring to the recycling history information.

[0046] The information management device 520 includes an acquisition unit 522, an information processing unit 524, and a transmission unit 526. The acquisition unit 522 and the transmission unit 526 are, for example, communication circuits that communicate with other devices via a communication network NW. The information processing unit 524 is, for example, a functional unit implemented by a CPU executing a program.

[0047] The acquisition department 522 obtains historical recycling information from the recycling operator's equipment 510.

[0048] The information processing unit 524 manages the association information of multiple recycling history information. For example, the information processing unit 524 generates association information that corresponds to multiple recycling history information obtained from multiple recycling operator devices 510. The association information is, for example, the identification information of a single plastic selected by the identification processing device 300. A code is affixed to the raw materials, products, or parts selected by the identification processing device 300 to read the identification information of the single plastic selected by the identification processing device 300. Thus, the information processing unit 524 can, for example, store multiple recycling history information by associating single plastics, raw materials, products or parts, users, recycled products or parts, classified products or parts, and shredded mixed plastics.

[0049] The sending unit 526 sends recycling history information according to the request received from the recycling operator's device 510. For example, the sending unit 526 sends recycling history information corresponding to the mixture of picked objects to the computing device 300 according to the request from the computing device 300.

[0050] Crushing operator device 510A is an information processing device used by crushing operators. Raw material manufacturer device 510B is an information processing device used by raw material manufacturers. Product manufacturer device 510C is an information processing device used by product manufacturers. User device 510D is an information processing device used by users. Recycling operator device 510E is an information processing device used by recycling operators. Sorting operator device 510F is an information processing device used by sorting operators. The computing device 300 is a picking operator device in the information storage system 500. Each recycling operator device 510 stores recycling history information related to the recycling process corresponding to this device in the information management device 520.

[0051] Reuse processes include, for example, crushing, sorting, raw material manufacturing, product manufacturing, use, recycling, and sorting.

[0052] The picking process is a process in which a mixture is sorted into multiple types of plastic sheets using an electrostatic picking device 100. The picking process sorts the mixed plastics into individual plastics. The individual plastics are recycled products that are reused through the picking process.

[0053] The raw material manufacturing process is the process by which raw material manufacturers produce raw materials from single plastics that have undergone a sorting process.

[0054] The product manufacturing process is the process by which product manufacturers produce products and parts based on raw materials that have undergone the raw material manufacturing process.

[0055] The usage process is the process by which a user uses a product or part that has undergone the product manufacturing process.

[0056] The recycling process is the process by which recycling companies collect products or parts that have undergone the usage process.

[0057] The sorting process is the process by which sorting operators sort products or parts that have undergone recycling processes.

[0058] The crushing process is the process by which crushing operators crush products or parts that have undergone sorting processes into a mixture.

[0059] In this way, the recyclable products processed through each of the multiple recycling processes are recycled. The crushing plant 510A, the raw material manufacturing plant 510B, the product manufacturing plant 510C, the user plant 510D, the recycling plant 510E, and the sorting plant 510F send information associated with each process as recycling history information to the information management plant 520. The information management plant 520 stores multiple recycling history information corresponding to the multiple recycling processes.

[0060] Information storage system 500 includes, for example, a crushing operator device 510A, a raw material manufacturer device 510B, a product manufacturer device 510C, a user device 510D, a recycling operator device 510E, a sorting operator device 510F, and an information management device 520. The computing device 300 can be the sorting operator device within the information storage system 500. The information management device 520 is connected to the computing device 300, the crushing operator device 510A, the raw material manufacturer device 510B, the product manufacturer device 510C, the user device 510D, the recycling operator device 510E, and the sorting operator device 510F via a communication network NW.

[0061] Figure 3 This is a block diagram illustrating an example of the learning unit 308 in the first embodiment. The learning unit 308 acquires sensing data, operation status information, picking result information, processing results of the first arithmetic processing, picking conditions calculated by the second arithmetic unit 304, and reuse history information from multiple edge server devices 200A, 200B, and 200C as learning data. The reuse history information can be at least one of the reuse history information acquired from the shredder device 510A, the raw material manufacturer device 510B, the product manufacturer device 510C, the user device 510D, the recycling device 510E, or the sorting device 510F.

[0062] The learning unit 308 inputs the learning data into the learned model 306 and updates the processing parameters of the learned model 306 based on the output from the learned model 306. These processing parameters are, for example, filters (also called weights or biases) contained in the neural network, but are not limited to these. The machine learning techniques used in the learned model 306 can be commonly used deep learning or random forests, etc.

[0063] Figure 4 This is a diagram showing an example of the electrostatic sorting device 100 in the first embodiment. The electrostatic sorting device 100 includes, for example, an input-side detection unit 110A, a recycling-side detection unit 110B, a flashlight 120, a vibrating feeder 122, an electric field generator 130, a recycling bin 140, and a control device 150.

[0064] The flashlight 120 is supplied with a raw material (mixture) comprising multiple types of plastic sheets that are to be selected. For example, a predetermined amount of raw material is fed into the flashlight 120 per unit time. The raw material in this embodiment may include, for example, plastic sheets containing a mixture of multiple materials obtained by crushing the frames of household appliances such as air conditioners, refrigerators, and washing machines in a shredding process. The plastic sheets in this embodiment are ABS (Acrylonitrile Butadiene Styrene) sheets and PS (Polystyrene) sheets, which have different electrical properties. The plastic sheets are, for example, 5mm square.

[0065] The charged cylinder 120 agitates the raw material by rotation. Plastic sheets A and B, contained within the raw material, become charged through friction within the charged cylinder 120 due to rotational agitation. The charged plastic sheets A and B each possess a polarity and charge quantity corresponding to the triboelectric series. Specifically, plastic sheet A (e.g., ABS) is positively charged, and plastic sheet B (e.g., PS) is negatively charged.

[0066] The charged plastic sheets A and B, energized by the flashlight 120, are discharged into the vibrating feeder 122. The positively charged plastic sheet A and the negatively charged plastic sheet B are attracted to each other by electrostatic force. The vibrating feeder 122 causes the plastic sheets A and B to vibrate up and down while simultaneously pressing them against the electric field generating section 130, causing them to separate and fall from the front end of the vibrating feeder 122. A portion of the plastic sheets A and B from the vibrating feeder 122 is extracted and fed into the separation and feeding device 124.

[0067] An electric field generating unit 130 generates an electrostatic field. The electric field generating unit 130 includes, for example, a pair of electrodes 131 and 132, and a DC power supply 133. Each of the electrodes 131 and 132 is formed, for example, in a plate shape. The electrodes 131 and 132 are arranged facing each other in a manner that clamps the paths of falling plastic sheets A and B. A grounding voltage is applied to electrode 131. The DC power supply 133 generates an electrostatic field between the electrodes 131 and 132 by applying a DC voltage between them. The DC voltage between electrodes 131 and 132 is adjusted by a control signal output from the control device 150.

[0068] Plastic sheets A and B falling from the vibrating feeder 122 are attracted to either of a pair of electrodes 131 and 132 simultaneously by electrostatic forces corresponding to their charged states, such as polarity and charge. That is, plastic sheets A and B fall at different positions depending on their charged state. In this embodiment, plastic sheet A, being positively charged, is attracted to electrode 131 and falls. Plastic sheet B, being negatively charged, is attracted to electrode 132 and falls.

[0069] The fallen plastic sheets A and B are collected into recycling bin 140. Recycling bin 140 collects plastic sheets A and B. Recycling bin 140 includes, for example, dividers 141 and 142 and a divider drive device 143. Dividers 141 and 142 are movable, for example, in the direction of the arrow in the figure. By moving dividers 141 and 142, the width of the three areas formed by dividers 141 and 142 is adjusted. Divider drive device 143 includes a drive mechanism such as a motor. Divider drive device 143 supplies driving force to dividers 141 and 142 according to a control signal supplied from control device 150.

[0070] Furthermore, the movement of partition plates 141 and 142 can be performed manually. In this embodiment, partition plates 141 and 142 are described as partitions dividing areas containing materials that are sensed by electrostatic force, but their shape is not limited to that of the plates and can be other shapes.

[0071] Plastic sheets A and B, falling from the vibrating feeder 122, are recovered according to their charged state into any one of the three areas formed by separators 141 and 142. Positively charged plastic sheet A is primarily recovered into the area on the side of separator 141. Negatively charged plastic sheet B is primarily recovered into the area on the side of separator 142. Insufficiently charged plastic sheets are recovered into the area between separators 141 and 142. The plastic sheets recovered into each area are transported by a conveyor (not shown) and housed in individual containers. The electrostatic sorting device 100 can also return the plastic sheets recovered into the area between separators 141 and 142 to the supply unit 220 for re-sorting.

[0072] The input-side detection unit 110A includes, for example, a plastic type identification sensor 111, a charge sensor 112, a plastic type identification sensor 113, and a quality sensor 114.

[0073] The plastic type identification sensor 111 detects sensing data used to identify the type of plastic sheet inserted into the flashlight 120. The plastic type identification sensor 111 can be, for example, an optical sensor that captures an image of the mixture. The optical sensor can be, for example, a near-infrared camera, a visible light camera, or a device that generates image information. Alternatively, the plastic type identification sensor 111 can also be a sensor for detecting particle size or foreign matter.

[0074] The charge sensor 112 is, for example, a pass-through Faraday cage type sensor. The charge sensor 112 drops a charged plastic sheet from above and detects the electrostatic induction voltage waveform (sensing data) generated when the plastic sheet falls. The electrostatic induction voltage waveform is analyzed by the first arithmetic unit 204 and converted into a charge. The plastic type identification sensor 113 is, for example, an optical sensor that captures images of a mixture. The plastic type identification sensor 113 detects sensing data used to identify the type of plastic in the plastic sheet after it has passed through the charge sensor 112. The mass sensor 114 detects sensing data used to determine the mass of the plastic sheet after it has passed through the charge sensor 112.

[0075] The recycling-side detection unit 110B includes, for example, a plastic type identification sensor 115 and a mass sensor 116. The plastic type identification sensor 115 is, for example, an optical sensor that captures images of the mixture. The plastic type identification sensor 115 detects sensing data to identify the type of plastic sheet in each of the three areas formed by the separators 141 and 142 that are recycled into the recycling bin 140. The mass sensor 116 detects sensing data to determine the mass of each of the plastic sheets recycled into the three areas formed by the separators 141 and 142.

[0076] The control device 150 performs control and various calculations on the various parts of the electrostatic sorting device 100. In response to acquiring sensing data detected by the input-side detection unit 110A and the recycling-side detection unit 110B, the control device 150 immediately sends at least one of the acquired sensing data to the edge server device 200. According to the control commands received from the edge server device 200, the control device 150 controls the operation of at least one of the following: the flashlight 120, the vibratory feeder 122, the electric field generator 130, and the recycling bin 140.

[0077] Figure 5 This is a block diagram illustrating an example of the functional structure of the first arithmetic unit 204 in the first embodiment.

[0078] As described above, the electrostatic sorting device 100 includes an input-side detection unit 110A, a recycling-side detection unit 110B, and a control unit 150. The input-side detection unit 110A detects near-infrared images or visual images, charge information, and quality information as sensing data, for example. The control unit 150 outputs the sensing data to a first processing unit 204. The first processing unit 204 includes, for example, an image processing unit 204a and data conversion units 204b and 204c for processing the sensing data detected by the input-side detection unit 110A.

[0079] The electrostatic sorting device 100 includes a plastic type identification sensor 111, which is an optical sensor that captures an image signal of the mixture being sorted, as described above, to generate information about the mixture. The image processing unit 204a of the first arithmetic unit 204 generates mixing ratio data representing the mixing ratio of the mixture based on the image signal generated by the plastic type identification sensor 111. For example, the image processing unit 204a can analyze the infrared image signal detected by the plastic type identification sensor 111 and convert it into mixing ratio data representing the mixing ratio of plastic flakes in the mixture.

[0080] The electrostatic sorting device 100 includes a plastic type identification sensor 111, which is an optical sensor that captures images of the mixture of sorted objects as described above to generate image signals as information about the mixture. The image processing unit 204a of the first arithmetic unit 204 can extract a portion of the image signal generated by the plastic type identification sensor 111, and the transmission unit 108 can transmit a portion of the image signal to the arithmetic unit 300.

[0081] Furthermore, the first processing unit 204 can parse the visual image signal and convert it into particle size data and foreign matter data of the mixture. In addition, the first processing unit 204 can, for example, convert the particle waveform signal detected by the particle sensor (not shown) for the mixture supplied to the flashlight 120 into the fluid flow rate and output particle size / foreign matter data.

[0082] The electrostatic sorting device 100 includes a charge sensor 112 that generates a charge signal that varies according to the charge of the mixture being sorted, serving as information about the mixture. The electrostatic sorting device 100 is an electrostatic sorting device that adjusts the magnitude of the voltage applied to electrodes 131, 132 (electrode pairs) that provide electrostatic force to the charged mixture, or the position of partitions 141, 142 that divide the area containing the mixture sensed by electrostatic force. The data conversion unit 204b of the first arithmetic unit 204 analyzes the charge signal generated by the charge sensor 112 and generates charge data representing the charge of the mixture. The data conversion unit 204b receives an electrostatic induction voltage waveform from the plastic type identification sensor 113 and converts it into charge data. The data conversion unit 204c receives a mass signal from the mass sensor 114 and converts it into mass data.

[0083] The electrostatic sorting device 100 may include a flow sensor that generates a flow signal as mixture information, which varies according to the flow rate of the mixture being sorted. The flow sensor may be, for example, a sensor that detects the amount of raw material fed into the flashlight 120. It may be a sensor integrated with the plastic type identification sensor 111, or it may be a flow sensor installed independently of 111. The data conversion unit 204d of the first arithmetic unit 204 can analyze the flow signal generated by the flow sensor, generate flow information indicating the flow rate of the mixture, and send it to the arithmetic unit 300.

[0084] The control device 150 outputs operation status information, such as separator position information, voltage information, MID cycle information, flashlight information, and feeder speed information, representing the operation status of the electrostatic picking device 100, to the data processing unit 204e. The separator position information indicates the positions of separators 141 and 142. The voltage information indicates the voltage between electrodes 131 and 132. The MID cycle information indicates the amount of plastic sheet circulating from the area between separators 141 and 142 to the flashlight 120. The flashlight information indicates the rotational speed of the flashlight 120. The feeder speed information indicates the vibration speed of the vibratory feeder 122. The data processing unit 204e performs predetermined data processing, for example, associating the separator position information, voltage information, MID cycle information, flashlight information, and feeder speed information at each time interval, and sends the resulting operation status data to the computing unit 30.

[0085] The first processing unit 204 includes, for example, an image processing unit 204f and a data conversion unit 204g for processing the sensing data detected by the recycling side detection unit 110B. The image processing unit 204f analyzes the infrared image detected by the plastic type identification sensor 115 and converts it into composition ratio data representing the composition ratio of the mixture. The data conversion unit 204g receives a mass signal from the mass sensor 116 and converts it into mass data.

[0086] Figure 6 This is a timing diagram illustrating an example of the operation of the electrostatic sorting system 1 in the first embodiment.

[0087] In the information storage system 500, reuse history information is sent from the reuse operator device 510 to the information management device 520, and the information management device 520 accumulates the reuse history information (step S100). In addition, in this embodiment, the reuse history information is accumulated in the information management device 520, but it is not limited to this; it can be accumulated in each of the reuse operator devices 510.

[0088] The raw material information (mixture to be picked) supplied to the electrostatic picking device 100 is read (step S102). The raw material information may be, for example, identification information assigned to the raw material in an upstream process of the picking process. The raw material information is sent from the electrostatic picking device 100 to the computing device 300 via the edge server device 200. The computing device 300 sends a request for reuse history information corresponding to the raw material information to the information management device 520. The information management device 520 extracts the reuse history information corresponding to the request and sends it to the computing device 300 (step S104). Thus, the computing device 300 obtains the reuse history information associated with the mixture to be picked.

[0089] After the supply of raw materials for the object to be picked begins (step S106), the electrostatic picking device 100 acquires sensing data detected by the plastic type identification sensor 111 and sends it to the edge server device 200 as information associated with the mixture of objects to be picked (step S108).

[0090] The first arithmetic unit 204 uses the sensing data acquired by the acquisition unit 202 to perform the first arithmetic process (step S110). The transmission unit 208 sends the processing result of the first arithmetic process to the arithmetic device 300 (step S112).

[0091] In response to acquiring sensing data from each of the plastic type identification sensor 111, charge sensor 112, plastic type identification sensor 113, and mass sensor 114, the control device 150 immediately sends each sensing data to the edge server device 200. In response to receiving the multiple sensing data, the first processing unit 204 immediately performs a first processing operation on each of the multiple sensing data, and sends multiple processing results corresponding to each of the multiple sensing data to the processing device 300.

[0092] The acquisition unit 302 of the arithmetic unit 300 receives the processing result of the first arithmetic process, and the second arithmetic unit 304 performs the second arithmetic process based on the processing result of the first arithmetic process and the reuse history information associated with the mixture of picking objects obtained from the information storage system 500, thereby calculating the picking conditions (step S114). The sending unit 310 sends the information representing the picking conditions as the processing result of the second arithmetic process to the edge server device 200 (step S116).

[0093] The second calculation unit 304 senses the mixing ratio of raw materials, the picking results, and the charge-to-mass ratio distribution of plastic sheets based on the processing results of the first calculation and the reuse of historical information, thereby performing high-performance automatic picking. The charge-to-mass ratio is a physical value obtained by dividing the charge of a plastic sheet charged by friction by its mass. Due to variations in the shape of each plastic sheet and differences in the friction mode during triboelectric charging, even for the same type of plastic sheet, the charge-to-mass ratio is not a constant value and exhibits a distribution. There is a correlation between the charge-to-mass ratio distribution and the distribution of the falling position of the plastic sheet after passing between electrodes 131 and 132. Therefore, the second calculation unit 304 calculates the charge-to-mass ratio distribution and predicts the optimal positions of the separators 141 and 142 based on the charge-to-mass ratio distribution.

[0094] The electrostatic picking device 100 extracts a portion of the mixed plastic that has just been rubbed and charged by the charged tube 120, and feeds it piece by piece into the plastic type identification sensor 113 via the separating feed device 124. Then, the sensing data obtained by the plastic type identification sensor 113 (optical sensor) and the mass sensor 114 are sent to the computing device 300 via the edge server device 200. The computing device 300 predicts the distribution of the falling positions of the plastic pieces based on the charge-to-mass ratio distribution, determines the optimal positions (picking conditions) of the separators 141 and 142, and remotely controls the separating drive device 143.

[0095] The receiving unit 206 of the edge server device 200 receives information indicating picking conditions from the computing unit 300, and the sending unit 208 sends the information indicating picking conditions as a control command to the electrostatic picking device 100 (step S118).

[0096] The receiving unit 106 of the electrostatic picking device 100 receives a control command, and the control device 150 controls the picking unit 104, which includes a flashlight 120, a vibrating feeder 122, an electric field generator 130, and a recycling bin 140, so that the electrostatic picking device 100 picks a single plastic (step S120). The recycling-side detection unit 110B detects sensing data, and the control device 150 sends the sensing data obtained from the recycling-side detection unit 110B to the edge server device 200 (step S122).

[0097] The first computing unit 204 performs a first computing process using the sensing data sent by the edge server device 200 (step S124), and sends the processing result of the first computing process to the computing device 300 (step S126).

[0098] The acquisition unit 302 acquires information associated with the mixture of objects to be picked and picking result information indicating the picking results of the picking device, obtained from the edge server device 200. The sending unit 310 sends the information associated with the mixture of objects to be picked and the picking result information acquired by the acquisition unit 302 to the information storage system 500 (step S128). The information management device 520 updates the reuse history information of the picking process based on the information obtained from the computing device 300 (step S130).

[0099] The electrostatic picking device 100 acquires sensing data via a plastic type identification sensor 115 and a quality sensor 116 for plastic sheets that are collected into the three areas formed by separators 141 and 142 after picking. The second calculation unit 304 calculates the picking result for each of the three areas formed by separators 141 and 142. The second calculation unit 304 compares the predicted value of the distribution of the plastic sheet's drop position with the measured value of the distribution of the plastic sheet's drop position, and then calculates the optimal position (picking condition) of separators 141 and 142. Thus, the second calculation unit 304 improves the accuracy of determining the position of separators 141 and 142 based on material variations.

[0100] The acquisition unit 302 can obtain product information using the mixture of objects to be picked from the information storage system 500 based on the information associated with the mixture of objects to be picked obtained from the electrostatic picking device 100 (step S104). The product information includes the manufacturer, model, and which part of the product is the source of the mixture of objects to be picked. The product information can be historical recycling information disclosed by the recycling operator device 510E, the sorting operator device 510F, and the shredding operator device 510A. The second calculation unit 304 can calculate picking conditions based on the information associated with the mixture of objects to be picked obtained from the electrostatic picking device 100 and the product information obtained from the information storage system 500 (step S114). For example, the state of the mixture of objects to be picked, such as additives, surface treatment, and surface contamination, may vary depending on the product. Therefore, the second calculation unit 304 can calculate appropriate picking conditions corresponding to the product. The calculation device 300 can calculate appropriate picking conditions based on the product by constructing a learned model 306 for each product.

[0101] The acquisition unit can obtain detailed information about the plastic sheets contained in the mixture of objects to be picked from the information storage system 500 based on the information associated with the mixture of objects to be picked obtained from the picking device (step S104). The detailed information includes at least one of the following: composition, particle size, color, additives, surface treatment, surface contamination, or reuse count of the plastic sheets contained in the mixture of objects to be picked. The second calculation unit 304 can calculate the picking conditions based on the information associated with the mixture of objects to be picked obtained from the electrostatic picking device 100 and the detailed information obtained from the information storage system 500 (step S114). The second calculation unit 304 can simulate the predicted value of the picking result based on the composition and particle size of the plastic sheets, and calculate the picking conditions based on the predicted value. In addition, by pre-storing the detailed information of the plastic sheets and the picking results, the calculation device 300 can infer the cause of picking defects caused by abnormal charging, or calculate picking conditions to prevent picking defects from occurring. In addition, after the picking process, in cases where a single plastic is sold or a quality defect occurs in a downstream process, the electrostatic picking system 1 can infer the cause of the quality defect or calculate picking conditions to prevent quality defects from occurring in the first place.

[0102] The electrostatic picking system 1 includes multiple electrostatic picking devices 100. The acquisition unit 302 can acquire picking process information (step S104) from the information storage system 500, which is related to a different picking process than the picking performed by the electrostatic picking device 100A, and is included in the recycling process of the mixture of picking objects. The second calculation unit 304 calculates picking conditions based on the information related to the mixture of picking objects acquired from the electrostatic picking device 100A and the picking process information acquired from the information storage system 500. Therefore, when the electrostatic picking device 100A performs a picking process, the calculation device 300 can calculate the picking conditions of the electrostatic picking device 100A using sensing data acquired from the electrostatic picking device 100A, as well as sensing data, operation status information, and picking result information acquired from other electrostatic picking devices 100B, etc. (step S114). In addition, the computing device 300 can use sensing data, operation status information and picking result information obtained from the electrostatic picking device 100A other than the electrostatic picking device 100A to enable the learned model 306 used to calculate the picking conditions of the electrostatic picking device 100A to learn, and calculate the picking conditions of the electrostatic picking device 100A based on the output of the learned model 306 (step S114).

[0103] The acquisition unit 302 can obtain post-process information associated with the process following the picking process from the information storage system based on the information associated with the mixture of picking objects obtained from the electrostatic picking device 100 (step S104). The post-process information is reuse history information such as raw material manufacturing process after the picking process. For example, the post-process information may be information indicating the final use of a single plastic sold in the raw material manufacturing process. The information indicating the use may be, for example, information indicating whether the single plastic picked through the picking process is material reuse, chemical reuse, or heat recovery. The second calculation unit 304 can calculate the picking conditions based on the information associated with the mixture of picking objects obtained from the electrostatic picking device 100 and the post-process information obtained from the information storage system 500 (step S114).

[0104] The acquisition unit 302 can obtain recycling quantity information, representing the recycling quantity of each type of resin recycled in the plurality of recycling processes, from the information storage system 500 based on the information associated with the mixture to be picked obtained from the electrostatic picking device 100 (step S104). The second calculation unit 304 can calculate the picking conditions based on the information associated with the mixture to be picked obtained from the electrostatic picking device 100 and the recycling quantity information obtained from the information storage system 500 (step S114). Thus, the electrostatic picking system 1 can calculate the picking conditions taking into account the recycling quantity of each material, product recycling, etc.

[0105] Figure 7 This is a flowchart illustrating an example of the processing of the edge server device 200 and the computing device 300 in the first embodiment.

[0106] The edge server device 200 determines whether the electrostatic picking device 100 is in operation (step S200). If the electrostatic picking device 100 is in operation (step S200: "Yes"), the edge server device 200 performs a reference... Figure 6 The processing described (step S202).

[0107] When the electrostatic picking device 100 stops (step S200: "No"), the edge server device 200 determines whether there is any unsent data (step S204). The unsent data is an image signal other than a portion of the image signal generated by the plastic type identification sensor 111 or the plastic type identification sensor 113. When the electrostatic picking device 100 is operating and transmitting the calculation result information obtained by the first calculation process performed by the first calculation unit 204, the unsent data can be source information used for calculating the calculation result information.

[0108] The first arithmetic unit 204 obtains unsent data from the electrostatic sorting device 100 (step S206). The first arithmetic unit 204 can obtain the source information of the first arithmetic process from the internal storage device. The sending unit 208 sends the obtained unsent data to the arithmetic device 300 (step S208).

[0109] The acquisition unit 302 of the computing unit 300 receives unsent data and sends it to the information management unit 520 (step S210). The unsent data includes information associated with the mixture of picking objects acquired by the acquisition unit 302 and picking result information indicating the picking result of the electrostatic picking device 100. Therefore, the sending unit 310 sends the information acquired by the acquisition unit 302 to the information storage system 500 when the electrostatic picking device 100 stops. The learning unit 308 uses the received unsent data to enable the learned model 306 to learn (step S212). The information management unit 520 accumulates the information associated with the mixture of picking objects acquired by the computing unit 300 and the picking result information indicating the picking result of the electrostatic picking device 100 as reuse history information (step S214).

[0110] As explained above, the electrostatic picking system 1 in this embodiment can acquire information related to the mixture to be picked from the electrostatic picking device 100 and recycling history information related to the mixture to be picked from the information storage system 500 via the acquisition unit 302 of the computing unit 300. The second computing unit 304 then calculates picking conditions based on the information related to the mixture to be picked acquired from the electrostatic picking device 100 and the recycling history information related to the mixture to be picked acquired from the information storage system 500. By using this electrostatic picking system 1, the picking process is performed based on the picking conditions calculated using information from the recycling process, thereby improving picking accuracy. In other words, the electrostatic picking system 1 allows information from processes other than the picking process in the recycling process to be used in the picking process to calculate optimal picking conditions, enabling remote control of the picking process.

[0111] In the recycling process, it is impossible to always recycle waste products in the same state, regardless of the industry. Therefore, the mixing ratio of the mixed plastic sheets (raw materials) obtained from crushing them will vary. For example, after the mixing ratio of the raw materials changes, the triboelectric charge of the plastics is affected, and the falling positions of the various plastics between the electrodes will change. Therefore, in order to maintain the purity and recycling volume of various plastics in the sorting process, operational techniques such as adjusting the voltage applied to electrodes 131 and 132 and the position of separators 141 and 142 are required. Without operational techniques corresponding to the changes in raw materials, the performance of the electrostatic sorting device 100 cannot be maximized, and the quality of recycled materials cannot be stabilized. Therefore, according to the electrostatic sorting system 1, the learned model 306 can learn information from other processes obtained from the information storage system 500 (crushing conditions of upstream processes, part information obtained during sorting, product information, etc.) and derive the optimal sorting conditions.

[0112] Furthermore, according to the electrostatic picking system 1, for example, there may be insufficient collection of sensing data, operational status information, and picking results regarding the electrostatic picking device 100, or a lack of data on picking processes for products in a particular industry. In such cases, high picking efficiency cannot be achieved until sufficient data is accumulated. However, according to the electrostatic picking system 1, by obtaining reuse history information from the information storage system 500, and acquiring data on upstream processes such as sorting and crushing processes (crushing particle size, proportion / material type / additive information of parts that become raw materials, quantity sent to the picking system, etc.), it is possible to predict the mixing ratio, composition, and charge-to-mass ratio of raw materials and control the electrostatic picking device 100.

[0113] (Second Implementation)

[0114] Figure 8 This is a diagram showing an example of a gravity-type sorting device 600 in the second embodiment.

[0115] The picking device in the first embodiment described above is an electrostatic picking device 100, but the picking device can be a gravity-based picking device 600. The gravity-based picking device 600 is a picking device that uses the difference in specific gravity of resin sheets contained in a mixture to be picked to perform picking. For example, the gravity-based picking device 600 picks plastic sheets contained in a mixture using a wet gravity picking method. The gravity-based picking device 600 includes a floating / sinking picking section 610 and a jigging picking section 620.

[0116] The float-and-sink sorting section 610 uses water as a medium to separate plastic sheets by causing lighter PP to float and heavier PS, ABS, and other plastics to settle. The plastic sheets floating on the water are recovered in the float-and-sink sorting section 610. The plastic sheets that have settled in the float-and-sink sorting section 610 are then transported to the jigging sorting section 620.

[0117] The jigging sorting unit 620 generates a shaking water flow by transmitting piston motion to the water, forming layers of each specific gravity. The jigging sorting unit 620 sorts materials such as light PS and ABS mixtures and heavy flame-retardant plastics, and recycles the light plastic sheets and heavy plastic sheets respectively.

[0118] The specific gravity picking device 600 is connected to the edge server device 200 and the computing device 300 as described above. The first computing unit 204 performs a first calculation on at least a portion of information in the specific gravity picking device 600, including mixture information related to the mixture, operating status information indicating the operating state of the specific gravity picking device 600, and picking result information indicating the picking result of the specific gravity picking device 600, and sends the processing result of the first calculation to the computing device 300. The computing device 300 performs a second calculation based on the processing result of the first calculation and information obtained from the specific gravity picking device 600, calculates the picking conditions of the specific gravity picking device 600, and sends the calculated picking conditions to the edge server device 200. The edge server device 200 controls the specific gravity picking device 600 according to the picking conditions received from the computing device 300.

[0119] (Third implementation)

[0120] Figure 9 This is a diagram showing an example of the optical picking device 700 in the third embodiment.

[0121] The picking device in the first embodiment described above is an electrostatic picking device 100, but the picking device can be an optical picking device 700. The optical picking device 700 is an optical picking device equipped with optical sensors (X-ray source 720, detector 722) that capture images of resin sheets containing a mixture of picking objects, and that performs picking based on the differences between the captured images of the resin sheets captured by the X-ray source 720 and the detector 722. The optical picking device 700, for example, includes a conveyor belt 710, an X-ray source 720, a detector 722, an air gun 730, collection units 740 and 742, and a control device 750.

[0122] In the optical sorting device 700, X-rays are irradiated from an X-ray source 720 onto a mixture conveyed by a conveyor belt 710, and the transmitted light is detected by a detector 722. This generates an X-ray transmission image signal of the mixture to be sorted. The X-ray transmission image signal is, for example, an image showing the shadow cast by bromine-containing plastics. The control device 750 sends the X-ray transmission image signal as sensing data to the edge server device 200. The first arithmetic unit 204 sends the processing result of performing a first arithmetic processing on the sensing data to the arithmetic unit 300. Based on the processing result of the first arithmetic processing and information obtained from the information storage system 500, the arithmetic unit 300 sends sorting conditions indicating the operation of the air gun unit 730 to the edge server device 200. The edge server device 200 controls the air gun unit 730 according to the sorting conditions received from the arithmetic unit 300. Thus, the optical sorting device 700, for example, sorts plastics containing bromine-based flame retardants to the recycling unit 742 and plastics without bromine-based flame retardants to the recycling unit 740.

[0123] Furthermore, various embodiments and modifications have been described, but they are only examples and are not limited to these. For example, any one of the embodiments and modifications, a part of the embodiments and a part of the modifications may be combined with one or more other embodiments and one or more other modifications to realize a solution of the present invention.

[0124] (Symbol Explanation)

[0125] 1: Electrostatic sorting system; 100, 100A, 100B, 100C: Electrostatic sorting devices; 102: Detection unit; 104: Sorting unit; 106: Receiving unit; 108: Sending unit; 110A: Input-side detection unit; 110B: Recycling-side detection unit; 111, 113, 115: Plastic type identification sensor; 112: Charge sensor; 114, 116: Mass sensor; 120: Flashlight; 122: Vibrating feeder; 124: Separation and input device; 130: Electric field generating unit; 131 132: Electrode; 133: DC power supply; 140: Recycling bin; 141, 142: Separator; 143: Drive unit; 150: Control unit; 200, 200A, 200B, 200C: Edge server unit; 202: Acquisition unit; 204: First arithmetic unit; 204a, 204f: Image processing unit; 204b, 204c, 204d, 204g: Data conversion unit; 204e: Data processing unit; 206: Receiving unit; 208: Transmitting unit; 210: Control unit; 220 300: Supply Department; 302: Computing Unit; 304: Acquisition Department; 306: Second Computing Unit; 308: Learned Model; 310: Learning Department; 310: Sending Department; 400: Database Unit; 410: User Database; 412: Picking Device Database; 414: Learned Model; 500: Information Storage System; 510: Recycling Operator Unit; 510A: Crushing Operator Unit; 510B: Raw Material Manufacturer Unit; 510C: Product Manufacturer Unit; 510D: User Unit; 510E: 510F: Sorting equipment; 512: Acquisition unit; 514: Information processing unit; 516: Dispatch unit; 520: Information management unit; 522: Acquisition unit; 524: Information processing unit; 526: Dispatch unit; 600: Gravity sorting device; 610: Float-sink sorting device; 620: Jigging sorting device; 700: Optical sorting device; 710: Conveyor belt unit; 720: X-ray source; 722: Detector; 730: Air gun unit; 740, 742: Recycling unit; 750: Control device.

Claims

1. A picking system, comprising: The sorting device performs sorting of materials for each type in multiple recycling processes, including sorting processes for mixtures of multiple types of materials; The computing device includes an acquisition unit for acquiring information, a computing unit for calculating picking conditions for the picking device based on the information acquired by the acquisition unit, and a sending unit for sending the picking conditions calculated by the computing unit to the picking device; and An information storage system stores multiple historical reuse information corresponding to the multiple reuse processes. The acquisition unit obtains information associated with the mixture to be picked from the picking device, and obtains the reuse history information associated with the mixture to be picked from the information storage system. The computing unit calculates the picking conditions based on information associated with the mixture to be picked obtained from the picking device and the reuse history information associated with the mixture to be picked obtained from the information storage system.

2. The picking system according to claim 1, wherein, The information storage system comprises: multiple recycling operator devices, corresponding to multiple recycling processes; and an information management device for managing the recycling history information obtained from the multiple recycling operator devices. Each of the recycling operator's devices stores historical recycling information related to the recycling process corresponding to that device in the information management device. The information management device manages the associated information of the multiple reuse historical information.

3. The picking system according to claim 1, wherein, The acquisition unit retrieves product information related to the mixture being picked from the information storage system based on information obtained from the picking device that is associated with the mixture being picked. The computing unit calculates the picking conditions based on information associated with the mixture of objects to be picked obtained from the picking device and product information obtained from the information storage system.

4. The picking system according to claim 1, wherein, The acquisition unit retrieves detailed information about the plastic sheets contained in the mixture of objects to be picked from the information storage system based on information associated with the mixture of objects to be picked obtained from the picking device. The computing unit calculates the picking conditions based on information associated with the mixture of objects to be picked obtained from the picking device and the detailed information obtained from the information storage system. The detailed information includes at least one of the following: composition, particle size, color, additives, surface finish, surface contamination level, or information related to the number of times the plastic sheet can be reused.

5. The picking system according to claim 1, wherein, Equipped with multiple of the aforementioned picking devices, The acquisition unit obtains from the information storage system picking process information associated with picking processes that are different from the picking processes performed by the picking device, which are included in the recycling process of the mixture of picked objects. The computing unit calculates the picking conditions based on information associated with the mixture of objects to be picked obtained from the picking device and the picking process information obtained from the information storage system.

6. The picking system according to claim 1, wherein, The acquiring unit retrieves, based on information associated with the mixture of objects to be picked obtained from the picking device, subsequent process information associated with the process following the picking process from the information storage system. The calculation unit calculates the picking conditions based on information associated with the mixture of objects to be picked obtained from the picking device and the subsequent process information obtained from the information storage system.

7. The picking system according to claim 1, wherein, The acquiring unit retrieves recycling quantity information from the information storage system, based on information associated with the mixture of sorted objects obtained from the sorting device, representing the recycling quantity of each type of material recycled in the plurality of recycling processes. The calculation unit calculates the picking conditions based on information associated with the mixture of objects to be picked obtained from the picking device and the cycle quantity information obtained from the information storage system.

8. The picking system according to claim 1, wherein, The acquiring unit acquires information associated with the mixture of objects to be picked from the picking device, as well as picking result information indicating the picking result of the picking device. The sending unit sends the information associated with the mixture of the picked objects, obtained by the acquiring unit, and the picking result information to the information storage system.

9. The picking system according to claim 1, wherein, When the picking device stops operating, the sending unit sends the information acquired by the acquiring unit to the information storage system.

10. The picking system according to claim 1, wherein, The picking device is an electrostatic picking device that uses the difference in charge of each category of resin contained in the mixture to perform picking.

11. The picking system according to claim 1, wherein, The sorting device is a gravity-based sorting device that uses the difference in specific gravity of each category of resin contained in the mixture to perform sorting.

12. The picking system according to claim 1, wherein, The picking device is an optical picking device that uses the differences in camera images of each category of resin contained in the mixture to be picked to perform the picking.

13. A picking method, comprising: The step of the computing device obtaining information from the picking device associated with a mixture of picking objects containing multiple types of materials; The step of the computing device obtaining the reuse history information associated with the sorted mixture from an information storage system that stores multiple reuse history information corresponding to multiple reuse processes; The step of the computing device calculating the picking conditions based on information associated with the mixture of objects to be picked obtained from the picking device and the reuse history information associated with the mixture of objects to be picked obtained from the information storage system; The step of the computing device sending information representing the picking conditions to the picking device; and The sorting device is used to sort mixtures of materials containing multiple types for each type of material.

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