Setting assistance system, setting assistance method, setting assistance device, and program

Abstract

A setting assistance system according to one aspect of the present disclosure comprises: an acquisition unit which acquires mixture information pertaining to a mixture, which includes a plurality of types of objects, from a first sorting device that sorts the mixture for each type of the objects, and operation condition information about the first sorting device; a reception unit which receives mixture information about the mixture that is sorted by a second sorting device; a similarity calculation unit which calculates similarity between the mixture information received by the reception unit and the mixture information acquired by the acquisition unit; and a setting unit which sets an operation condition to the second sorting device on the basis of the similarity calculated by the similarity calculation unit and the operation condition information about the first sorting device.

Description

Setting support system, setting support method, setting support device, and program

[0001] The present disclosure relates to a setting support system, a setting support method, a setting support device, and a program.

[0002] Conventionally, there has been known a technique for separating a mixture containing multiple types of objects into individual types of objects. For example, one such separation technique is an electrostatic separation device that electrically charges multiple types of objects contained in the mixture and separates specific objects as objects to be recycled by electrostatic separation. For example, the electrostatic separation device described in Patent Document 1 listed below is known as a technique for improving the accuracy of electrostatic separation.

[0003] The electrostatic separation device in Patent Document 1 agitates a group of plastic pieces containing a mixture of multiple types of plastic pieces, charges each plastic piece, drops the charged plastic pieces into an electric field, and collects the dropped plastic pieces in a collection box. The collection box is provided with a movable partition plate within the collection box. This electrostatic separation device samples a portion of the group of plastic pieces to measure the composition ratio of the multiple types of plastic pieces, analyzes the trend in the composition ratio based on the composition ratio measured previously and the composition ratio measured this time, and controls the position of the partition plate based on the analysis results.

[0004] International Publication No. 2021-166178

[0005] When sorting objects, there is a trade-off between the recovery rate of a specific object and the purity of that object. For example, it is necessary to maximize the recovery rate while satisfying the user's desired purity. The recovery rate and purity results vary depending on the state of the mixture fed into the sorting device. The state of the mixture is indicated by, for example, the type of object to be sorted, the amount fed, the mixing ratio (composition ratio), and the particle size. The state of the mixture varies depending on the product to be recycled, pre-processing, season, input lot, etc.

[0006] For example, when a new sorting device is introduced, it is not possible to set appropriate sorting conditions for the device because the relationship between the state of the mixture and the recovery results is unknown. Furthermore, even after the introduction of the sorting device, fluctuations in the state of the mixture can change the recovery results, making it difficult to stabilize the recovery results.

[0007] The present disclosure has been made in consideration of these circumstances, and aims to provide a setting support system, a setting support method, a setting support device, and a program that can assist in appropriately setting operating conditions for a sorting device.

[0008] The present disclosure has been made to solve the above-mentioned problems, and one aspect of the present disclosure is a setting support system including: an acquisition unit that acquires mixture information regarding a mixture containing multiple types of objects in a first sorting device that sorts the mixture by type of object and operating condition information of the first sorting device; a reception unit that accepts mixture information of a mixture to be sorted by a second sorting device; a similarity calculation unit that calculates a similarity between the mixture information accepted by the reception unit and the mixture information acquired by the acquisition unit; and a setting unit that sets operating conditions for the second sorting device based on the similarity calculated by the similarity calculation unit and the operating condition information of the first sorting device.

[0009] Another aspect of the present disclosure is a setting support method including the steps of: acquiring mixture information about a mixture in a first sorting device that sorts a mixture containing multiple types of objects by type of object, and operating condition information about the first sorting device; accepting mixture information about a mixture to be sorted by a second sorting device; calculating a similarity between the mixture information about the mixture to be sorted by the second sorting device and the mixture information about the mixture in the first sorting device; and setting operating conditions for the second sorting device based on the similarity and the operating condition information of the first sorting device.

[0010] Another aspect of the present disclosure is a setting support device including: a similarity calculation unit that calculates a similarity between mixture information regarding a mixture in a first sorting device that sorts a mixture containing multiple types of objects by type of the objects, operating condition information of the first sorting device, and mixture information of a mixture to be sorted by a second sorting device; and a setting unit that sets operating conditions for the second sorting device based on the similarity calculated by the similarity calculation unit and the operating condition information of the first sorting device.

[0011] Another aspect of the present disclosure is a program that causes a computer of a setting assistance device to execute the steps of: calculating a similarity between mixture information regarding a mixture in a first sorting device that sorts a mixture containing multiple types of objects by type of object, operating condition information of the first sorting device, and mixture information of a mixture to be sorted by a second sorting device; and setting operating conditions for the second sorting device based on the similarity and the operating condition information of the first sorting device.

[0012] According to the present disclosure, it is possible to assist in setting appropriate operating conditions for a sorting device.

[0013] 1 is a block diagram showing an example of a setting support system in a first embodiment. FIG. 2 is a flowchart showing an example of an operating procedure of the setting support system in the first embodiment. FIG. 3 is a sequence diagram showing an example of processing of the setting support system in the first embodiment. FIG. 4 is a diagram showing an example of an electrostatic separation device in the first embodiment. FIG. 5 is a block diagram showing an example of the functional configuration of the electrostatic separation device in the first embodiment. FIG. 6 is a diagram showing an example of the operation of a similarity calculation unit in the first embodiment. FIG. 7 is a diagram showing an example of the operation of the similarity calculation unit in the first embodiment. FIG. 8 is an explanatory diagram showing an example of the operation of selecting a trained model by the setting support system in the first embodiment to control the electrostatic separation device. FIG. 9 is an explanatory diagram showing an example of the operation of selecting a trained model by the setting support system in the first embodiment to control the electrostatic separation device. FIG. 10 is a block diagram showing an example of a setting support system in a second embodiment. FIG. 11 is a sequence diagram showing an example of processing of the setting support system in the second embodiment.

[0014] A setting support system, a setting support method, a setting support device, and a program to which the present invention is applied will be described below with reference to the drawings.

[0015] FIG. 1 is a block diagram showing an example of a setting support system 1 according to a first embodiment. The setting support system 1 includes a setting support device 100, a plurality of electrostatic separation devices 200A and 200B, a database device 300, a learning device 400, and a control device 410. The setting support device 100, the electrostatic separation devices 200A and 200B, the learning device 400, and the control device 410 each have a communication interface (not shown) such as a network interface card (NIC) or a wireless communication module for connecting to a network such as the Internet. The network may include, for example, a general-purpose network such as the Internet, and a private network such as local 5G or Wi-Fi (registered trademark).

[0016] In the embodiments, an electrostatic sorting device that uses static electricity to separate recyclable materials from a mixture containing multiple types of resins is described as a sorting device that separates a mixture containing multiple types of objects by object type, but the invention is not limited to this. The setting support system 1 of the embodiments can also be applied to a sorting device that separates specific objects from a mixture containing metals, or a sorting device that separates specific objects from a mixture containing food.

[0017] The electrostatic separation device 200A is, for example, a new electrostatic separation device that does not perform electrostatic separation, and the electrostatic separation device 200B is, for example, an existing electrostatic separation device that performs electrostatic separation. The setting support system 1 illustrates one electrostatic separation device 200A and multiple electrostatic separation devices 200B as shown in FIG. 1 , and describes setting the operating conditions of the new electrostatic separation device 200A based on the operating condition information of the existing electrostatic separation device 200B, but is not limited to this. The operating conditions of the other electrostatic separation device 200B may be set based on the operating condition information of the existing electrostatic separation device 200B. In the following description, the electrostatic separation devices 200A and 200B will be collectively referred to simply as "electrostatic separation device 200."

[0018] The electrostatic separation device 200 operates to separate materials to be recycled from a mixture containing multiple types of resins. The materials to be recycled include multiple types of pieces to be separated that have different electrostatic charge characteristics. The electrostatic separation device 200 includes a detection unit 202 that detects mixture information of the mixture input into the electrostatic separation device 200. The electrostatic separation device 200 transmits the mixture information and operating condition information to the setting support device 100. Note that the electrostatic separation device 200 can also be applied to recycling equipment that separates materials to be recycled from raw materials using other methods, such as a specific gravity method, rather than an electrostatic method.

[0019] The mixture information is information about the mixture in the electrostatic separation device 200. For example, the mixture information is information including at least one of the type, composition, and particle size of the object.

[0020] The operating condition information is information indicating the operating conditions of the electrostatic separation device 200. The operating condition information may be the magnitude of the voltage applied to the electrode pair that applies an electrostatic force to the charged object, or the position of the partition that divides the area that contains the object guided by the electrostatic force. The operating condition information may be information based on a trained model that is trained using mixture information, operating conditions, and collection results as training data and is trained to output operating conditions that result in optimal collection results when mixture information is input. A trained model is created for each mixture information. The operating condition information may be information based on a single trained model that combines multiple trained models based on similarity. The information based on the trained model may be, for example, information for setting the trained model or information output from the trained model. The information for setting the trained model is information for allowing the control device 410 of one electrostatic separation device 200 to use the trained model of another electrostatic separation device 200.

[0021] The learning device 400 acquires mixture information, operating condition information, and recovery results from the electrostatic separation device 200B as learning data. The recovery results may include, for example, the recovery rate of a specific object and the purity of the specific object. The recovery results may include the environmental load of the electrostatic separation device 200, the operating cost of the electrostatic separation device 200, and the operating speed of the electrostatic separation device 200. The learning device 400 constructs a trained model 402 that outputs operating conditions that result in optimal recovery results when mixture information is input. The learning device 400 constructs the trained model 402 by updating processing parameters such as filters (also known as weights or biases) included in a neural network. The machine learning method used in the trained model 402 may be commonly used deep learning or random forest, etc.

[0022] The control device 410 is an information processing device that controls the electrostatic separation device 200 using the trained model 402. The control device 410 inputs the mixture information detected by the detection unit 202 to the trained model 402, and controls the electrostatic separation device 200 based on the operating condition information output from the trained model 402.

[0023] The database device 300 is a device including a data storage device, a processor for constructing a database (DB), and the like. The database device 300 stores, for example, a user database 310 and information related to a trained model 320. The user database 310 is a database that associates, for example, user information that identifies a user or the electrostatic separation device 200, mixture information, and a trained model 320. The trained model 320 corresponds to the trained model 402 constructed by the training device 400. The trained model 320 is stored in the database device 300 for each mixture. The trained model 320 may be stored in the database device 300 by type, composition, or particle size of the resin pieces.

[0024] The database device 300 acquires mixture information, information for setting a trained model, and the like from the electrostatic separation device 200, the learning device 400, or the control device 410 via a network. The database device 300 acquires user information based on the network or user operations. The database device 300 thereby creates and updates a user database 310. The database device 300 also acquires information for setting a trained model 402 from the learning device 400 and stores a trained model 320 corresponding to the mixture information. The database device 300 may be equipped with a learning device, acquire mixture information, operating condition information, and recovery results of the electrostatic separation device 200, and construct a trained model using the acquired information.

[0025] The database device 300 may store the mixture information and the operating condition information for the electrostatic separation device 200B in association with each other. The database device 300 transmits the mixture information and the operating condition information for the electrostatic separation device 200B to the setting support device 100 in response to a request from the setting support device 100.

[0026] The setting support device 100 is an information processing device in which a processor such as a CPU (Central Processing Unit) executes a program stored in a program memory. The setting support device 100 includes, for example, an acquisition unit 102, a reception unit 104, a similarity calculation unit 106, and a setting unit 108. The acquisition unit 102, the reception unit 104, the similarity calculation unit 106, and the setting unit 108 are functional units realized by the processor executing the program.

[0027] The acquiring unit 102 acquires mixture information about the mixture in the electrostatic separation device 200B that separates a mixture containing multiple types of objects into individual object types, and operating condition information about the electrostatic separation device 200B. The accepting unit 104 accepts the mixture information about the mixture to be separated by the electrostatic separation device 200A.

[0028] The similarity calculation unit 106 calculates the similarity between the mixture information received by the reception unit 104 and the mixture information acquired by the acquisition unit 102. The setting unit 108 sets operating conditions for the electrostatic separation device 200A based on the similarity calculated by the similarity calculation unit 106 and the operating condition information of the electrostatic separation device 200B.

[0029] The setting unit 108 may, for example, set the operating condition information of the electrostatic separation device 200B in the electrostatic separation device 200A by transmitting the operating condition information of the electrostatic separation device 200B to the electrostatic separation device 200A. The setting unit 108 may, for example, set the operating condition information of the electrostatic separation device 200B in the electrostatic separation device 200A by transmitting the operating condition information of the electrostatic separation device 200B to a terminal of a person who sets up the electrostatic separation device 200A. The setting unit 108 may, for example, display the operating condition information of the electrostatic separation device 200B on a terminal of a person who sets up the electrostatic separation device 200A.

[0030] In the first embodiment, the acquisition unit 102, the reception unit 104, the similarity calculation unit 106, the setting unit 108, the detection unit 202, the learning device 400, and the control device 410 are distributed as shown in FIG. 1 , but this is not limiting and they may be distributed or consolidated in other forms. The acquisition unit 102, the reception unit 104, the similarity calculation unit 106, and the setting unit 108 may be distributed across multiple information processing devices. For example, the similarity calculation unit 106 and the setting unit 108 may be provided in one setting assistance device 100, and the acquisition unit 102 and the reception unit 104 may be provided in a device other than the setting assistance device 100.

[0031] FIG. 2 is a flowchart showing an example of the operation procedure of the setting support system 1 according to the first embodiment. First, the acquisition unit 102 acquires mixture information for the existing electrostatic separation device 200B (step S100). Next, the acquisition unit 102 acquires operating condition information for the existing electrostatic separation device 200B (step S102). The acquisition unit 102 acquires the mixture information for the electrostatic separation device 200B and stores it in the user database 310. The acquisition unit 102 acquires a trained model 402 as operating condition information, associates it with the mixture information for the electrostatic separation device 200B, and stores it as a trained model 320. Next, the reception unit 104 accepts the mixture information for the new electrostatic separation device 200A (step S104). Next, the similarity calculation unit 106 calculates the similarity between the mixture information for the electrostatic separation device 200A accepted by the reception unit 104 and the mixture information for the electrostatic separation device 200B acquired by the acquisition unit 102 (step S106).

[0032] Next, the setting unit 108 selects the optimal trained model 320 from the trained models 320 based on the similarity calculated by the similarity calculation unit 106 (step S108). The setting unit 108 determines the operating conditions of the electrostatic separation device 200A using the selected trained model 320 (step S110). The setting unit 108 sets the determined operating conditions to the new electrostatic separation device 200A (step S112).

[0033] 3 is a sequence diagram showing an example of processing by the setting support system 1 in the first embodiment. The existing electrostatic separation device 200B transmits mixture information and information S1 for setting the trained model 402 to the setting support device 100. The acquisition unit 102 receives the information S1 related to the mixture information and the trained model 402 from the electrostatic separation device 200B and transmits an information registration request S2 to the database device 300. As a result, the database device 300 updates the user database 310 based on the information S1 related to the mixture information and the trained model 402 and stores the trained model 320. The information related to the trained model 402 is information for setting the trained model 402, but is not limited to this and may be operating conditions output from the trained model 402.

[0034] When setting operating conditions for a new electrostatic separation device 200A, the electrostatic separation device 200A transmits mixture information S3 to the setting support device 100, and the receiving unit 104 receives the mixture information S3. The similarity calculation unit 106 calculates the similarity and transmits a request S4 for information on the trained model 320 selected based on the similarity. The database device 300 transmits information S5 on the selected trained model to the setting support device 100 in accordance with the request S4. The setting unit 108 transmits operating condition information S6 to the electrostatic separation device 200A based on the information S5 on the trained model transmitted from the database device 300. The setting unit 108 may create operating condition information for the new electrostatic separation device 200A using the trained model and transmit the information to the electrostatic separation device 200A. The setting unit 108 may transmit information for setting the selected trained model to the electrostatic separation device 200A.

[0035] FIG. 4 illustrates an example of an electrostatic separation device 200 according to the first embodiment. The electrostatic separation device 200 includes, for example, a supply unit 220 that supplies a mixture 210 containing multiple types of plastic pieces A and B to be separated, a charging unit 230 that charges the plastic pieces A and B, an electric field generating unit 240 that generates an electrostatic field, and a collection box 250 that collects the plastic pieces A and B. A control device 410 that controls each component of the electrostatic separation device 200 and performs various calculations is connected to the electrostatic separation device 200. The mixture 210 in this embodiment includes plastic pieces of a mixture of multiple materials obtained by crushing the housings of home appliances such as air conditioners, refrigerators, and washing machines to be recycled. The plastic pieces A and B in this embodiment are ABS (Acrylonitrile Butadiene Styrene) pieces and PS (Polystyrene) pieces, each having different charging characteristics. The plastic pieces A and B are, for example, about 5 mm square. In Fig. 4, the ABS piece as the plastic piece A is shown in white, and the PS piece as the plastic piece B is shown in solid.

[0036] The supply unit 220 includes, for example, a hopper 221 and an input feeder 222. The hopper 221 stores plastic pieces A and B that have been dried by a dryer (not shown). The hopper 221 supplies a predetermined amount of plastic pieces A and B per unit time to the input feeder 222. The input feeder 222 inputs the plastic pieces A and B supplied from the hopper 221 into the charging unit 230.

[0037] The charging unit 230 includes, for example, a charging cylinder 231 and a vibrating feeder 232. The charging cylinder 231 agitates the plastic pieces A and B by rotating. The plastic pieces A and B rub ​​against each other within the charging cylinder 231 as they rotate and agitate, and become electrically charged. The electrically charged plastic pieces A and B each have a polarity and amount of charge according to their triboelectric series. Specifically, the plastic piece A is positively charged, and the plastic piece B is negatively charged.

[0038] The charged plastic pieces A and B are discharged into the vibrating feeder 232. The positively charged plastic piece A and the negatively charged plastic piece B are attracted to each other by electrostatic force. The vibrating feeder 232 vibrates the plastic pieces A and B up and down (in the Z direction) while pushing them forward (towards the +X direction), causing the plastic pieces A and B to fall from the tip of the vibrating feeder 232 while separating them.

[0039] The electric field generator 240 includes, for example, a pair of electrodes 241 and 242 and a DC power supply 243. Each of the pair of electrodes 241 and 242 is formed, for example, in a flat plate shape. The pair of electrodes 241 and 242 are arranged opposite each other in the X-axis direction, sandwiching the path along which plastic pieces A and B fall. A ground voltage GND is applied to the electrode 241. The DC power supply 243 applies a predetermined DC voltage between the electrodes 241 and 242, thereby generating an electrostatic field between the pair of electrodes 241 and 242. Plastic pieces A and B that fall from the vibrating feeder 232 are attracted to one of the pair of electrodes 241 and 242 by electrostatic force depending on their charge state, such as polarity and charge amount. In other words, plastic pieces A and B fall to different positions depending on their charge state. In this embodiment, plastic piece A is positively charged and therefore is attracted to and falls toward electrode 241. Since the plastic piece B is negatively charged, it is attracted to the electrode 242 and falls.

[0040] The dropped plastic pieces A and B are collected in collection box 250. Collection box 250 includes, for example, housing 251 and partition plates 252 and 253. Collection box 250 is formed with area 250A surrounded by housing 251 and partition plate 252, area 250B surrounded by housing 251 and partition plate 253, and area 250C sandwiched between partition plates 252 and 253.

[0041] The partition plates 252 and 253 are movable, for example, in the X direction in the figure. The partition plates 252 and 253 are connected, for example, to drive units 254 and 255. The drive units 254 and 255 are, for example, motors that generate driving forces to move the partition plates 252 and 253. The control device 410 supplies control signals to the drive units 254 and 255. The partition plates 252 and 253 move to adjust the sizes of the regions 250A, 250B, and 250C. The partition plates 252 and 253 may be moved manually based on the positions of the partition plates 252 and 253 calculated by the control device 410. In the embodiment, the partition plates 252 and 253 are described as partitions that separate the regions 250A, 250B, and 250C that contain objects guided by electrostatic force. However, the partition plates are not limited to being plate-shaped and may have other shapes.

[0042] Plastic pieces A and B dropped from vibrating feeder 232 are collected in either area 250A, 250B, or 250C depending on their charge state. Positively charged plastic pieces A are collected mainly in area 250A. Negatively charged plastic pieces B are collected mainly in area 250B. Plastic pieces that are not sufficiently charged are collected in area 250C. The plastic pieces collected in area 250A, area 250B, and area 250C are transported by a conveyor (not shown) and stored in separate containers.

[0043] The electrostatic separation device 200 may return the plastic pieces collected in the area 250C to the supply section 220 and perform separation again.

[0044] 5 is a block diagram showing an example of the functional configuration of the electrostatic separation device 200 according to the first embodiment. The electrostatic separation device 200 includes, for example, a data processing unit 261, an input amount conversion unit 262, a data processing unit 263, image processing units 264, 265, 266, and 267, an input plastic sensor unit 270, and a recovered plastic sensor unit 280. The input plastic sensor unit 270, the input amount conversion unit 262, the data processing unit 263, and the image processing units 264 and 265 correspond to the detection unit 202 described above.

[0045] The control device 410 outputs operating condition information, such as partition plate position information, voltage information, MID circulation information, charging cylinder information, and feeder speed information, to the data processing unit 261. The partition plate position information is information indicating the positions of the partition plates 252 and 253. The voltage information is information indicating the voltage between the electrodes 241 and 242, and the MID circulation information is information indicating the amount of plastic pieces circulated from the region 250C to the supply unit 220. The charging cylinder information is information indicating the rotation speed of the charging cylinder 231. The feeder speed information is information indicating the vibration speed of the vibrating feeder 232. The data processing unit 261 performs, for example, predetermined data processing and transmits operating condition data, which links the partition plate position information, voltage information, MID circulation information, charging cylinder information, and feeder speed information for each time, to the learning device 400.

[0046] The input plastic sensor unit 270 includes, for example, a particle sensor, a charge sensor, a near-infrared camera, and a visible light camera. The input plastic sensor unit 270 outputs particle passage information detected by the particle sensor for the mixture 210 input into the charging unit 230 to the input amount conversion unit 262. The input amount conversion unit 262 transmits input amount data indicating the input amount of the mixture 210 based on the particle passage information to the learning device 400. The input plastic sensor unit 270 outputs particle charge information detected by the charge sensor to the data processing unit 263. The data processing unit 263 transmits charge amount data indicating the charge amount of the mixture 210 based on the charge amount information to the learning device 400. The input plastic sensor unit 270 outputs a near-infrared image captured by the near-infrared camera to the image processing unit 264. The image processing unit 264 acquires the composition ratio based on the near-infrared image and transmits the composition ratio data of the mixture 210 to the learning device 400. Input plastic sensor unit 270 outputs a visible image captured by the visible light camera to image processing unit 265. Image processing unit 265 acquires information indicating the particle size and foreign matter contained in mixture 210 based on the visible image, and transmits the particle size and foreign matter data of mixture 210 to learning device 400.

[0047] The collected plastic sensor unit 280 includes, for example, a visible light camera, an optical sensor, and a near-infrared camera. The collected plastic sensor unit 280 outputs visible images of the plastic pieces collected in the collection box 250, detected by the visible light camera or the optical sensor, to the image processing unit 266. The image processing unit 266 acquires the collection rates for each of the plastic pieces A and B based on the visible images, and transmits the collection rate data to the learning device 400. The collected plastic sensor unit 280 outputs near-infrared images of the plastic pieces collected in the collection box 250, detected by the near-infrared camera, to the image processing unit 267. The image processing unit 267 acquires the purity based on the near-infrared images, and transmits the purity data of the mixture 210 to the learning device 400.

[0048] The learning device 400 uses mixture information including input amount data, charge amount data, composition ratio data, and particle size / foreign matter data, as well as operating condition data and recovery results including recovery rate data and purity data to train the learned model 402. This allows the learning device 400 to update the learned model.

[0049] When the detection unit 202 detects, based on the mixture information, that another mixture 210 has been added to the electrostatic separation device 200, the learning device 400 may train a trained model corresponding to the other mixture 210. This allows the learning device 400 to construct a trained model for each mixture 210. The control device 410 can switch the trained model based on the mixture information detected by the detection unit 202.

[0050] When the mixture information detected by the detection unit 202 differs from the mixture information corresponding to the trained model, the control device 410 may transmit the mixture information detected by the detection unit 202 to the setting assistance device 100. The similarity calculation unit 106 calculates the similarity based on the mixture information acquired from the control device 410, and the setting unit 108 selects the trained model with the highest similarity. The setting unit 108A can transmit information regarding the selected trained model to the control device 410. The control device 410 can switch to a trained model corresponding to the mixture information and control the electrostatic separation device 200. This allows the setting unit 108 to change the operating conditions set in the electrostatic separation device 200A based on the similarity.

[0051] FIG. 6 is a diagram illustrating an example of the operation of the similarity calculation unit 106 in the first embodiment. The similarity calculation unit 106 calculates the similarity between the mixture information of the new electrostatic separation device 200A and the mixture information of the existing electrostatic separation device 200B. The similarity is calculated using at least one of the following parameters that affect the type of plastic pieces and the separation efficiency: the mixing ratio, particle size, and input amount. For example, as shown in the user database 310 in FIG. 6, the similarity calculation unit 106 extracts devices B, E, and G (devices A to G) in which the type of plastic pieces contained in the mixture 210 of the new electrostatic separation device 200A matches the type of plastic pieces contained in the mixture 210 of the existing electrostatic separation device 200B.

[0052] 7 is a diagram showing an example of the operation of the similarity calculation unit 106 in the first embodiment. The similarity calculation unit 106 calculates the similarity between the average composition ratio, average particle size, and average input amount in the mixture information of the new electrostatic separation device 200A and the average composition ratio, average particle size, and average input amount in the mixture information of each of the devices B, E, and G. As a result, the similarity calculation unit 106 selects the device G that is most similar to the mixture information of the electrostatic separation device 200A from the mixture information of the devices B, E, and G. At this time, the similarity calculation unit 106 may calculate the similarity by calculating Euclidean distance or cosine similarity.

[0053] 8 is an explanatory diagram showing an example of an operation of selecting a trained model by the setting support system 1 according to the first embodiment to control the electrostatic separation device 200. Assume that the database device 300 stores mixture information and trained model A for device A, mixture information and trained model B for device B, and mixture information and trained model C for device C. The similarity calculation unit 106 calculates the similarity for each of device A, device B, and device C by comparing the composition, average composition ratio, composition variation, and average particle size of the mixture information for the new electrostatic separation device 200A with the composition, average composition ratio, composition variation, and average particle size of the mixture information stored in the database device 300. The similarity calculation unit 106 selects trained model B for device B because device B has the highest similarity among devices A, B, and C.

[0054] The setting unit 108 acquires information about the selected trained model B from the database device 300, and inputs the mixture information of the new electrostatic separation device 200A into the trained model B. The setting unit 108 transmits the operating condition information, such as the partition plate position information, voltage information, charged cylinder information, and feeder speed information, output from the trained model B, to the new electrostatic separation device 200A. This allows the electrostatic separation device 200A to perform electrostatic separation in accordance with the partition plate position information, voltage information, charged cylinder information, and feeder speed information transmitted from the setting unit 108.

[0055] FIG. 9 is an explanatory diagram showing an example of an operation of selecting a trained model and controlling the electrostatic separation device 200 by the setting support system 1 according to the first embodiment. The setting unit 108A may select multiple trained models based on similarities, and the operating condition information may be information based on a single trained model obtained by combining the selected multiple trained models. The setting unit 108 combines two trained models, trained model B and trained model C, which have the highest similarities among the multiple similarities calculated by the similarity calculation unit 106, to create a single trained model D. The setting unit 108 inputs mixture information for the new electrostatic separation device 200A into the trained model D and transmits the operating condition information output from the trained model D to the new electrostatic separation device 200A. The setting unit 108 may transmit information to the electrostatic separation device 200A for setting a single trained model D obtained by combining trained model B and trained model C as the trained model 402 for the electrostatic separation device 200A.

[0056] If there is mixture information whose similarity to the mixture information of the electrostatic separation device 200A is equal to or greater than a threshold, the setting unit 108 may transmit information based on a trained model corresponding to the mixture information with the highest similarity to the electrostatic separation device 200A. If there is no mixture information whose similarity to the mixture information of the electrostatic separation device 200A is equal to or greater than a threshold, the setting unit 108 may create a trained model by combining multiple trained models. For example, if the average composition ratio in the mixture information of the electrostatic separation device 200A is 30-70%, and one trained model corresponds to mixture information with an average composition ratio of 30-50%, and another trained model corresponds to mixture information with an average composition ratio of 50-70%, a trained model corresponding to mixture information with an average composition ratio of 30-70% can be constructed by combining the two trained models. This allows the setting unit 108 to increase the similarity between the mixture information corresponding to the combined trained model and the mixture information of the electrostatic separation device 200A. As a result, the setting unit 108A can set the operating condition information output from the trained model with a high degree of similarity to the electrostatic separation device 200A, and can set the trained model with a high degree of similarity to the electrostatic separation device 200A.

[0057] As described above, the setting support system 1 according to the first embodiment can acquire mixture information and operating condition information for the electrostatic separation device 200B, accept mixture information for the mixture 210 to be separated by the electrostatic separation device 200A, and set operating conditions for the electrostatic separation device 200A based on the similarity between the mixture information for the electrostatic separation device 200A and the mixture information for the electrostatic separation device 200B and the operating condition information for the electrostatic separation device 200B. This allows the setting support system 1 to assist in appropriately setting operating conditions for the electrostatic separation device 200A. As a result, the setting support system 1 can determine operating conditions for a new electrostatic separation device 200A in a short period of time without the user having to acquire data indicating the relationship between the mixture information and the collection results.

[0058] Furthermore, the setting support system 1 can calculate the similarity between the mixture information detected by the detection unit 202 and the mixture information of the electrostatic separation device 200B, and change the operating conditions set in the electrostatic separation device 200A based on the similarity. As a result, the setting support system 1 can stabilize the recovery results even if the state of the mixture 210 changes after the electrostatic separation device 200 is installed.

[0059] The second embodiment will be described below. The second embodiment differs from the first embodiment in that the operating conditions of the electrostatic separation device 200A are set based on the requirements of the customer who owns the electrostatic separation device 200A, the similarity between the mixture information of the electrostatic separation device 200A and the mixture information of the electrostatic separation device 200B, and the operating condition information of the electrostatic separation device 200B. Note that the same parts as those in the first embodiment are denoted by the same reference numerals and will not be described again.

[0060] FIG. 10 is a block diagram illustrating an example of a configuration support system 1A according to the second embodiment. The configuration support system 1A according to the second embodiment includes a user terminal device 500. The user terminal device 500 is an information processing device, such as a personal computer, that performs various processes based on operations performed by a user as a customer. The user terminal device 500 transmits requests related to the electrostatic separation device 200A to the configuration support device 100A in response to user operations. The requests related to the electrostatic separation device 200A include, for example, requests to improve the recovery rate of a specific type of object in the mixture 210, requests to improve the purity of a specific type of object in the mixture 210, requests to reduce the environmental impact of the electrostatic separation device 200A, and requests to reduce the operating costs of the electrostatic separation device 200A. The environmental impact is a value representing the load on the environment caused by the operation of the electrostatic separation device 200A, such as CO2 emissions. The operating cost is, for example, the cost incurred by the operation of the electrostatic separation device 200A, such as the power consumption of the electrostatic separation device 200A.

[0061] In the setting support device 100A, the reception unit 104 receives a request from a customer who uses the electrostatic separation device 200A from the user terminal device 500. The setting unit 108A sets the operating conditions of the electrostatic separation device 200A based on the request received by the reception unit 104, the mixture information of the electrostatic separation device 200A, the similarity with the mixture information of the electrostatic separation device 200B acquired by the acquisition unit 102, and the operating condition information of the electrostatic separation device 200B. In this way, the setting unit 108A sets the operating conditions in accordance with the request, prioritizing at least one of the recovery rate, purity, environmental load, and operating cost.

[0062] For example, when the receiving unit 104 receives a request to prioritize purity, the setting unit 108A may select the trained model with the highest purity from among the trained models whose similarity is equal to or higher than a standard. The setting unit 108A can adjust the operating conditions in response to the request by inputting mixture information of the electrostatic separation device 200A into the selected trained model and setting operating conditions based on the output of the trained model for the electrostatic separation device 200A.

[0063] The setting assistance device 100A includes a billing processing unit 110 that generates billing information based on the setting of operating conditions that satisfy the requirements by the setting unit 108A. The billing processing unit 110 may generate the billing information based on a change in at least one of the recovery rate, purity, environmental load, and operating cost due to the operation of the electrostatic separation device 200A using the operating conditions that satisfy the requirements set by the setting unit 108A. The billing processing unit 110 may generate the billing information when the changed operating conditions are provided in response to the acceptance by the acceptance unit 104 of a change in the mixture information or a change in the request.

[0064] 11 is a sequence diagram showing an example of processing by the setting support system 1A in the second embodiment. The existing electrostatic separation device 200B transmits information S1 regarding the mixture information and the trained model 402 to the setting support device 100A. The acquisition unit 102 receives the information S1 regarding the mixture information and the trained model 402 from the electrostatic separation device 200B and transmits an information registration request S2 to the database device 300. As a result, the database device 300 updates the user database 310 based on the information S1 regarding the mixture information and the trained model 402 and stores the trained model 320.

[0065] When setting operating conditions for a new electrostatic separation device 200A, the electrostatic separation device 200A transmits mixture information S3 to the setting support device 100A, and the reception unit 104 receives the mixture information S3. The similarity calculation unit 106 calculates the similarity and transmits a request S4 for information on the trained model 320 selected based on the similarity. The database device 300 transmits information S5 on the selected trained model to the setting support device 100A in accordance with the request S4. The setting unit 108A transmits operating condition information S6 to the electrostatic separation device 200A based on the information S5 on the trained model transmitted from the database device 300. The setting unit 108A may create operating condition information for the new electrostatic separation device 200A using the trained model and transmit the information to the electrostatic separation device 200A. The setting unit 108A may transmit information for setting the selected trained model to the electrostatic separation device 200A.

[0066] The user terminal device 500 transmits a user request S7 regarding the electrostatic separation device 200A to the setting support device 100A based on a user operation. The reception unit 104 receives the user request S7. The user request S7 may be a change to the mixture information or a change to the request. The request change may be, for example, a change to the target values ​​for the recovery rate of a specific type of object in the mixture, the purity of a specific type of object in the mixture, the environmental load of the electrostatic separation device 200A, and the operating cost of the electrostatic separation device 200A, or a change to a prioritized operating condition among the recovery rate of a specific type of object in the mixture, the purity of a specific type of object in the mixture, the environmental load of the electrostatic separation device 200A, and the operating cost of the electrostatic separation device 200A. The setting unit 108A transmits a model request S8 to the database device 300 to acquire a trained model corresponding to the user request S7, and acquires information S9 regarding the trained model from the database device 300. At this time, the setting unit 108A acquires information S9 about a trained model that satisfies the user's requirements more than the trained model set by the information S5, among trained models whose similarity is equal to or higher than a standard. The setting unit 108A transmits the acquired information S9 as operating condition information S10 to the electrostatic separation device 200A. The billing processing unit 110 generates billing information S11 based on the setting of the operating conditions for the electrostatic separation device 200A by the setting unit 108A, and transmits the generated billing information to the user terminal device 500.

[0067] The acquisition unit 102 acquires a collection result S12 resulting from operation of the electrostatic separation device 200A based on the operating condition information S10. The billing processing unit 110 detects, based on the collection result S12, that at least one of the collection rate, purity, environmental load, and operating cost has changed due to operation of the electrostatic separation device 200A using operating conditions that satisfy the requirements set by the setting unit 108A. The billing processing unit 110 generates billing information based on the change in at least one of the collection rate, purity, environmental load, and operating cost due to operation of the electrostatic separation device 200A using operating conditions that satisfy the requirements set by the setting unit 108A, and transmits the billing information to the user terminal device 500.

[0068] As described above, the setting support system 1A according to the second embodiment can set operating conditions for the electrostatic separation device 200A based on the user's request, the similarity, and the operating condition information for the electrostatic separation device 200B. As a result, the setting support system 1A can support the user in setting appropriate operating conditions for the electrostatic separation device 200A in accordance with the user's request.

[0069] According to the setting support system 1A of the second embodiment, billing information can be generated based on the setting of operating conditions that satisfy the user's requirements for the electrostatic separation device 200A. According to the setting support system 1A of the second embodiment, billing information can be generated based on the collection results after the operating conditions based on the user's requirements are set for the electrostatic separation device 200A. As a result, according to the setting support system 1A, it is possible to build a business model in which users are charged by adjusting the collection rate, purity, environmental impact, operating costs, etc. of the electrostatic separation device 200A to satisfy the user's requirements.

[0070] Although each embodiment and variant have been described, these are merely examples and are not intended to limit the scope of the present invention. For example, one of the embodiments or variants, or a part of each embodiment or a part of each variant, may be combined with one or more other embodiments or one or more other variants to realize one aspect of the present invention.

[0071] 1, 1A... setting support system, 100, 100A... setting support device, 102... acquisition unit, 104... reception unit, 106... similarity calculation unit, 108, 108A... setting unit, 110... billing processing unit, 200, 200A, 200B... electrostatic separation device, 202... detection unit, 210... mixture, 220... supply unit, 221... hopper, 222... input feeder, 230... charging unit, 231... charging cylinder, 232... vibration feeder, 240... electric field generation unit, 241, 242... electrode, 243... direct current Power supply, 250... collection box, 251... housing unit, 252, 253... partition board, 254, 255... drive unit, 261, 263... data processing unit, 262... input amount conversion unit, 264, 265, 266, 267... image processing unit, 270... input plastic sensor unit, 280... collected plastic sensor unit, 300... database device, 310... user database, 320, 402... trained model, 400... learning device, 410... control device, 500... user terminal device

Claims

1. an acquisition unit that acquires mixture information related to a mixture including a plurality of types of objects in a first sorting device that sorts the mixture into groups of objects by type, and operating condition information of the first sorting device from a plurality of first sorting devices; a receiving unit that receives mixture information of the mixture to be sorted by the second sorting device; a similarity calculation unit that calculates a similarity between each of the mixture information accepted by the accepting unit and the mixture information acquired by the acquiring unit; a setting unit that selects operating condition information for setting operating conditions for the second sorting device from among the operating condition information of the plurality of first sorting devices based on the similarity calculated by the similarity calculation unit; and A setting support system comprising:

2. The setting support system according to claim 1 , wherein the mixture information includes at least one of a type, a composition, and a grain size of the object.

3. the sorting device is an electrostatic sorting device, The setting support system according to claim 1 , wherein the operating condition information is a magnitude of a voltage applied to an electrode pair that applies an electrostatic force to the charged object, or a position of a partition that divides an area that contains the object induced by the electrostatic force.

4. Each of the plurality of first sorting devices is trained on the mixture information, operating conditions, and recovery results as learning data, and performs sorting using a trained model trained to output operating conditions that result in optimal recovery results when mixture information is input, The trained model is created for each of the mixture information, The setting unit selects a trained model for setting operating conditions for the second sorting device from among a plurality of trained models of the plurality of first sorting devices. The setting support system according to claim 1 .

5. The setting support system described in claim 4, wherein the recovery results include a recovery rate of a specific object contained in the mixture and a purity of a specific object contained in the mixture.

6. The setting unit is Selecting a plurality of trained models based on the similarity; The setting support system of claim 4, further comprising: selecting operating condition information for setting operating conditions for the second selection device based on a single trained model obtained by combining the selected trained models.

7. a detection unit that detects mixture information of the mixture input to the second sorting device, the similarity calculation unit calculates a similarity between the mixture information detected by the detection unit and each of the mixture information acquired by the acquisition unit; the setting unit selects operating condition information for changing the operating conditions set in the second sorting device from the operating condition information of the plurality of first sorting devices based on the similarity calculated by the similarity calculation unit. The setting support system according to claim 1 .

8. The reception unit receives a request from a customer who owns the second sorting device, the setting unit selects operating condition information for setting operating conditions for the second sorting device from among the operating condition information of the plurality of first sorting devices based on a similarity between the request, each of the mixture information accepted by the accepting unit, and the mixture information acquired by the acquiring unit. The setting support system according to claim 1 .

9. The setting support system of claim 8, wherein the setting unit selects operating conditions that prioritize at least one of the recovery rate of a specific type of object in the mixture, the purity of a specific type of object in the mixture, the environmental load of the second sorting device, and the operating cost of the second sorting device based on the request.

10. 9. The setting support system according to claim 8, further comprising a billing processing unit that generates billing information based on the setting of the operating conditions that satisfy the request by the setting unit.

11. The setting support system described in claim 9, further comprising a billing processing unit that generates billing information based on a change in at least one of the recovery rate, the purity, the environmental load, and the operating cost due to the second sorting device operating under operating conditions that satisfy the requirements set by the setting unit.

12. The setting support system according to claim 8 , further comprising a billing processing unit that generates billing information when changed operating conditions are provided in response to a change in the mixture information or a change in the request being accepted by the accepting unit.

13. An acquisition unit that acquires mixture information regarding a mixture including a plurality of types of objects and a trained model that is created for each of the mixture information and outputs an operating condition when the mixture information is input; a reception unit that receives mixture information of the mixture to be sorted by the sorting device; a similarity calculation unit that calculates a similarity between the mixture information accepted by the acceptance unit and the mixture information acquired by the acquisition unit; A setting unit that selects a trained model for setting operating conditions of a selection device from among the plurality of trained models based on the similarity calculated by the similarity calculation unit; A setting support system comprising:

14. A step of acquiring mixture information related to a mixture including a plurality of types of objects in a first sorting device that sorts the mixture into groups of objects by type, and operating condition information of the first sorting device from a plurality of first sorting devices; receiving mixture information of the mixture to be sorted by the second sorting device; Calculating a similarity between the mixture information of the mixture sorted by the second sorting device and each of the mixture information regarding the mixture in the first sorting device; selecting operating condition information for setting operating conditions for the second sorting device from among the operating condition information of the plurality of first sorting devices based on the similarity; A setting assistance method comprising:

15. a similarity calculation unit that calculates a similarity between each of a plurality of mixture information related to a mixture in a plurality of first sorting devices that sort a mixture including a plurality of types of objects for each type of the objects and mixture information of a mixture sorted by a second sorting device; a setting unit that selects operating condition information for setting operating conditions for the second sorting device from among the operating condition information of the plurality of first sorting devices based on the similarity calculated by the similarity calculation unit; and A setting assistance device comprising:

16. The computer of the setting support device A step of calculating a similarity between each of a plurality of mixture information related to a mixture in a plurality of first sorting devices that sort a mixture including a plurality of types of objects for each type of the objects and mixture information of a mixture sorted by a second sorting device; selecting operating condition information for setting operating conditions for the second sorting device from among the operating condition information of the plurality of first sorting devices based on the similarity; A program to execute.

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