Information processing device, information processing method, program, incineration management system, and operation method of incineration management system

Abstract

To stably burn waste by appropriately acquiring information of a moisture percentage of the waste.SOLUTION: An information processing device is provided with a control part for managing waste stored in a storage pit configured to be able to store waste and waste after being carried out of the storage pit and before being burned in an incinerator. The information processing device includes: a storage part for storing imaging information acquired from an imaging part for imaging the waste stored in the storage pit as an input parameter; and a control part for inputting the input parameter read out from the storage part to a moisture percentage determination model, and outputting, as an output parameter, a moisture percentage corresponding to an address in the storage pit in the waste stored in the storage pit. The moisture percentage determination model is a learning model generated in advance by at least one of machine learning and a statistical method with the imaging information of the waste stored in the storage pit as an input parameter for learning and with the moisture percentage of the waste after being carried out and before being burned as an output parameter for learning.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, a program, an incineration management system, and an operation method of an incineration management system. [Background technology]

[0002] Waste of various types, such as household waste, pruning branches, and sludge, is brought into a waste incineration facility where waste is incinerated. The brought-in waste is temporarily stored in a storage pit called a garbage pit, where it is mixed to homogenize it to a specific property. After the mixing process, the waste is loaded into an incinerator using a crane or the like and incinerated.

[0003] In recent years, there has been a demand for more stable incineration of waste than in the past. As one of the efforts to achieve stable incineration, technology is being developed to analyze the properties of waste piled up in storage pits.

[0004] Patent Document 1 discloses a technology related to a device for estimating the quality of waste, which estimates the quality of waste by inputting image information of waste in a storage pit into a teacher model constructed by learning using teacher data consisting of values ​​indicating the characteristics of waste identified based on the operation history of a waste treatment plant and labels classifying the quality of waste by an operator based on image information of waste in the storage pit, and controls combustion in an incinerator based on the estimation result. That is, Patent Document 1 discloses a technology in which the teacher data is collected from at least one of images of the storage pit and values ​​indicating the characteristics of waste identified based on the operation history of the waste treatment plant, and labels classifying the quality of waste by an operator based on image data of waste in a garbage pit.

[0005] In addition to the technology described in Patent Document 1, there is also research being conducted that focuses on the moisture content of waste and attempts to utilize the moisture content for combustion control in incinerators. For example, Patent Document 2 discloses a technology related to an incinerator structure that provides a moisture meter at the bottom end of the legs of a hopper, measures the moisture content of the waste immediately before it is supplied to the incinerator, and attempts to estimate the nature of the waste based on the measured moisture content. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2019-207099 A [Patent Document 2] JP 2020-085279 A Summary of the Invention [Problem to be solved by the invention]

[0007] However, in the technology disclosed in the above-mentioned Patent Document 1, the value indicating the characteristics of the waste identified based on the operation history of the waste treatment plant used as the teacher data is a value estimated based on data with a time delay obtained by burning the waste. Furthermore, the labels may vary depending on the worker who creates the teacher data. In other words, the technology described in Patent Document 1 has a problem in that the characteristics of the waste before burning are not actually measured, making it difficult to accurately control the burning.

[0008] Furthermore, in the technology disclosed in Patent Document 2, a moisture meter is installed at the lowest end of the legs of a hopper installed immediately before the incinerator to measure the moisture content of the waste, which poses the problem that if a sudden change occurs in the combustion, it becomes difficult to respond to the change in combustion.

[0009] Therefore, there was a need to develop technology that could properly obtain information on the moisture content of waste and burn the waste stably.

[0010] The present invention has been made in consideration of the above circumstances, and its object is to provide an information processing device, an information processing method, a program, an incineration management system, and an operating method for an incineration management system that can appropriately acquire information on the moisture content of waste and stably burn the waste. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems and achieve the object, an information processing device according to one embodiment of the present invention is an information processing device that includes a control unit that manages waste stored in a storage pit configured to store waste, and waste after it has been removed from the storage pit and before it is incinerated in an incinerator, and includes a memory unit that stores imaging information obtained from an imaging unit that images the waste stored in the storage pit as input parameters, and a control unit that inputs the input parameters read from the memory unit into a moisture content determination model and outputs a moisture content of the waste stored in the storage pit corresponding to an address in the storage pit as an output parameter, and the moisture content determination model is a learning model that has been generated in advance by at least one of machine learning and statistical methods, with the imaging information of the waste stored in the storage pit as a learning input parameter and the moisture content of the waste after it has been removed and before it is incinerated as a learning output parameter.

[0012] In an information processing device according to one aspect of the present invention, in the above invention, the control unit determines the moisture percentage for each address, and controls the stirring of the waste in the storage pit based on the moisture percentage.

[0013] In one embodiment of the information processing device of the present invention, in the above invention, the control unit acquires combustion information indicating the combustion state of the waste in the incinerator, and performs control to change the stirring method for stirring the waste in the storage pit based on the combustion information and the derived moisture content of the waste in the storage pit.

[0014] In an information processing device according to one embodiment of the present invention, in this configuration, the control unit determines the moisture content for each address, and when the mixing method is a moisture standard mode, selects from among the multiple addresses in the storage pit an address to hold the waste and an address to dump the waste based on the moisture content, so that waste from addresses whose moisture content is equal to or greater than a predetermined set value and waste from addresses whose moisture content is less than a predetermined set value are mixed together.

[0015] In an information processing device according to one embodiment of the present invention, in this configuration, the control unit determines the moisture percentage for each address, and when the mixing method is the combustion standard mode, selects a supply moisture percentage to be supplied to the incinerator based on the combustion information, and controls the mixing of the waste by selecting an address to hold the waste and an address to release the waste from among multiple addresses in the storage pit based on the moisture percentage so that the moisture percentage approximately matches the supply moisture percentage.

[0016] An information processing method according to one embodiment of the present invention is an information processing method executed by a control unit that manages waste stored in a storage pit configured to store waste, and waste after it has been removed from the storage pit and before it is incinerated in an incinerator, and includes storing imaging information obtained from an imaging unit that images the waste stored in the storage pit as input parameters in a memory unit, inputting the input parameters read from the memory unit into a moisture content determination model, and outputting a moisture content of the waste stored in the storage pit corresponding to an address within the storage pit as an output parameter, and the moisture content determination model is a learning model that has been generated in advance by at least one of machine learning and statistical methods, with the imaging information of the waste stored in the storage pit as a learning input parameter and the moisture content of the waste after it has been removed and before it is incinerated as a learning output parameter.

[0017] A program according to one embodiment of the present invention has an information processing device having a control unit that manages waste stored in a storage pit configured to store waste, and waste after it has been removed from the storage pit and before it is incinerated in an incinerator, and the control unit generates a moisture content determination model using at least one of machine learning and statistical techniques, with imaging information of the waste stored in the storage pit as input parameters for learning, and the moisture percentage of the waste after it has been removed and before it is incinerated as an output parameter for learning, and stores the model in a memory unit, stores imaging information obtained from an imaging unit that images the waste stored in the storage pit as input parameters, inputs the input parameters read from the memory unit into the moisture content determination model read into the memory unit, and outputs the moisture percentage of the waste stored in the storage pit corresponding to the address in the storage pit as an output parameter.

[0018] An incineration management system according to one embodiment of the present invention comprises an information processing device according to the above invention, a storage pit for storing waste, an imaging unit capable of imaging the waste stored in the storage pit, a gripping unit capable of stirring the waste by gripping and releasing it under the control of the information processing device, and a moisture content measuring unit capable of measuring the moisture content of the waste after it has been removed and before it is incinerated.

[0019] The incineration management system according to one aspect of the present invention is the above-mentioned invention, further comprising an incinerator capable of incinerating the waste after the waste is removed.

[0020] A method for operating an incineration management system according to one aspect of the present invention is a method for operating an incineration management system including: a storage pit configured to be able to store waste and equipped with an imaging unit capable of imaging the waste; an information processing device having a control unit that manages the waste stored in the storage pit and the waste after it has been removed from the storage pit and before it is incinerated in an incinerator and controls the gripping unit that moves the waste stored in the storage pit by gripping and releasing it; and a memory unit that stores imaging information of the waste in the storage pit obtained from the imaging unit; and an incinerator capable of incinerating the waste after it has been removed, wherein the control unit controls the imaging information read out from the memory unit. The moisture content determination model is input as an input parameter, and the moisture content of the waste stored in the storage pit corresponding to the address within the storage pit is output as an output parameter, combustion information indicating the combustion state of the waste is obtained from the incinerator, and control is performed to change the stirring method for stirring the waste in the storage pit based on the combustion information and the derived moisture content of the waste in the storage pit, and the moisture content determination model is a learning model that has been generated in advance by at least one of machine learning and statistical methods, with imaging information of the waste stored in the storage pit as a learning input parameter and the moisture content of the waste after removal and before incineration as a learning output parameter. Effect of the Invention

[0021] The information processing device, information processing method, program, incineration management system, and operating method of the incineration management system according to the present invention make it possible to properly acquire information on the moisture content of waste and to combust the waste stably. [Brief description of the drawings]

[0022] [Figure 1] FIG. 1 is a block diagram showing an incineration management system according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating a waste treatment facility according to one embodiment of the present invention. [Diagram 3] FIG. 3 is a block diagram illustrating a management device according to an embodiment of the present invention. [Figure 4] FIG. 4 is a plan view of a storage pit according to one embodiment of the present invention. [Diagram 5] FIG. 5 is a flowchart for explaining a method for operating a waste storage facility controlled by a management device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In all the drawings of the embodiment, the same or corresponding parts are denoted by the same reference numerals. Furthermore, the present invention is not limited to the embodiment described below.

[0024] (Incineration Management System) Fig. 1 shows an incineration management system 1 to which an information processing device according to this embodiment is applied. Fig. 2 shows a waste treatment facility 3 according to this embodiment.

[0025] As shown in Fig. 1, the incineration management system 1 includes a management device 10, a waste storage facility 20, and a waste incineration facility 30, which are capable of communicating with each other via a network 2. A waste treatment facility 3 includes at least the waste storage facility 20 and the waste incineration facility 30. The management device 10 may be provided outside the waste treatment facility 3 and capable of communicating with the waste treatment facility 3 via the network 2, or may be a part of the waste treatment facility 3. Furthermore, the management device 10 may be provided inside the waste storage facility 20 or the waste incineration facility 30, and the installation location is not limited.

[0026] The network 2 is configured by appropriately combining wired communication and wireless communication, and is composed of communication networks such as the Internet network and mobile phone network. The network 2 is composed of one or more combinations of, for example, dedicated lines, public communication networks such as the Internet, for example, LANs (Local Area Networks), WANs (Wide Area Networks), telephone communication networks such as mobile phones, public lines, VPNs (Virtual Private Networks), etc. The management device 10, the waste storage facility 20, and the waste incineration facility 30 are connected via the network 2.

[0027] (Waste storage facility) The waste storage facility 20 as a waste storage unit includes a control unit 21, a communication unit 22, an imaging unit 23, a sensor unit 24, a gripper unit 25, and a storage pit 26. The gripper unit 25 is movably provided in the storage pit 26, and the imaging unit 23 and the sensor unit 24 are also provided. The gripper unit 25 is controlled by the control unit 21 based on a control signal transmitted from the control unit 11 of the management device 10. The control unit 21 may control the imaging unit 23 and the sensor unit 24 based on a control signal transmitted from the control unit 11. The control unit 11 of the management device 10 may directly control the imaging unit 23, the sensor unit 24, and the gripper unit 25.

[0028] Specifically, the control unit 21 includes a processor having hardware such as a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and an FPGA (Field-Programmable Gate Array), and a main storage unit such as a RAM (Random Access Memory) and a ROM (Read Only Memory) (none of which are shown). The control unit 21 controls the imaging unit 23, the sensor unit 24, and the grip unit 25 based on control signals input from the management device 10 via the communication units 13 and 22 in accordance with various programs stored in the main storage unit such as the RAM and ROM.

[0029] The communication unit 22 is, for example, a LAN interface board, a wired communication circuit for wired communication, or a wireless communication circuit for wireless communication. The LAN interface board, the wired communication circuit, and the wireless communication circuit are connected to the network 2. The communication unit 22 as a transmitting unit and a receiving unit is connected to the network 2 and communicates with the management device 10.

[0030] The imaging unit 23 has, for example, imaging cameras 231 and 232. The imaging unit 23 is configured to be able to image the waste 40 in the storage pit 26, particularly the surface, by the imaging cameras 231 and 232. The imaging unit 23 may further be configured to be able to image the waste 40 gripped by the bucket 253, for example, by imaging the bucket 253 of the gripping unit 25. The imaging unit 23 images the state of the waste 40 in the storage pit 26, i.e., the state of the waste 40, and transmits the captured image data as imaging information to the management device 10 via the communication unit 22. The imaging unit 23 may transmit the captured image data of the waste 40 as imaging information to the management device 10 via the communication unit 22.

[0031] The sensor unit 24 is configured to include at least one moisture meter 241. Various types can be adopted for the moisture meter 241 as a gripping moisture content measuring unit that measures the moisture content of the waste 40 gripped in the bucket 253. Examples of the type of the moisture meter 241 include a transmission type microwave intensity type, a contact type microwave intensity type, a contact type electrostatic capacitance type, a transmission type electrostatic capacitance type, and an infrared intensity type.

[0032] The transmission type microwave intensity method is a method in which an emitted microwave is transmitted through the waste and the moisture content of the waste is measured from the attenuation rate of the received microwave, etc. With the transmission type microwave intensity method, a wide range of waste 40 can be measured, so it is suitable for measuring waste 40 with an uneven distribution of moisture content, such as waste 40 in the chute section 336 described below.

[0033] The moisture meter 241 that employs the transmission type microwave intensity method is a so-called transmission type microwave intensity moisture meter, and has a microwave transmitter, a receiver, and a moisture percentage calculator connected to the receiver. The transmitter is installed on one of the claws of the bucket 253, and the receiver is installed on the other claw of the bucket 253. Since microwaves have the property of being absorbed by moisture, the microwaves emitted from the transmitter are attenuated by the moisture in the waste as they pass through the waste before reaching the receiver. The transmission type microwave intensity moisture meter can measure the moisture percentage contained in the waste 40 based on the attenuation rate of the microwaves.

[0034] Specifically, the transmission type microwave intensity moisture meter determines the intensity ratio (or intensity difference) of the microwave intensity transmitted by the transmitter to the microwave intensity received by the receiver, i.e., the voltage ratio (or voltage difference) of the voltage received by the receiver to the voltage transmitted by the transmitter, as the attenuation rate (or amount of attenuation) when the microwave transmits through the waste. The moisture percentage calculator holds in advance the correlation between the microwave attenuation rate and the moisture percentage of the waste as a relational database, and by referring to this relational database, calculates the moisture percentage of the waste from the attenuation rate actually determined as a voltage ratio. The moisture percentage calculator is connected to the control unit 21, and the moisture percentage of the waste 40 calculated by the moisture percentage calculator is output to the control unit 21 as the measurement value of the moisture meter 241.

[0035] The moisture meter 241 employing the contact-type capacitance method is a so-called contact-type capacitance moisture meter, and includes a capacitance meter of the contact-type capacitance method and a moisture percentage calculator connected to the capacitance meter. The capacitance meter measures the capacitance of the waste 40 that contacts the claws of the bucket 253. The moisture percentage calculator can derive the moisture percentage corresponding to the measurement value of the capacitance measured by the capacitance meter from the correlation between the capacitance and moisture percentage of the waste that is stored in advance. Specifically, the moisture percentage calculator stores a relational database in which the correlation between the capacitance of the waste and the moisture percentage of the waste is measured in advance, and inputs the measurement value of the capacitance of the waste 40 transmitted from the capacitance meter into the relationship between the capacitance and moisture percentage in the relational database, and outputs the measured moisture percentage of the waste 40 as the measurement value of the moisture meter 241. The moisture percentage calculator is connected to the control unit 21, and the moisture percentage of the waste 40 calculated by the moisture percentage calculator is output to the control unit 21 as the measurement value of the moisture meter 241.

[0036] In this embodiment, the moisture meter 241 includes, for example, a capacitance meter (not shown) that measures capacitance. The capacitance meter has a detection element, for example, an electrode, that comes into contact with the waste 40 gripped by the bucket 253. The moisture meter 241 is provided on the openable and closable claws of the bucket 253, but it is sufficient that at least a detection element is provided. In this case, the moisture meter 241 is provided separately from the bucket 253.

[0037] The sensor unit 24 may include multiple distance measuring sensors, such as LiDAR (Laser Imaging Detection and Ranging). In addition to the distance measuring sensor, the sensor unit 24 may also have a laser sensor, an infrared sensor, or a combination of these sensors. Here, the distance measuring sensor can measure the distance from the installation position to the surface of the waste 40, and can measure the distance to the waste 40 in association with two-dimensional position information (x, y) along the horizontal plane of the storage pit 26. The sensor unit 24 transmits the measured distance to the management device 10 via the communication unit 22.

[0038] The storage pit 26 is a pit capable of temporarily storing the waste 40. The waste 40 in the storage pit 26 is gripped by the gripping part 25, and is supplied to the incinerator 33 of the waste incineration facility 30, where it is incinerated.

[0039] The gripping unit 25 grips and moves the waste 40 stored in the storage pit 26. The bucket 253 serving as an opening / closing unit is capable of gripping the waste 40. The crane 252 serving as a moving unit is configured to be movable by connecting the bucket 253. The crane 252 is provided with a load meter 251. The load meter 251 is configured to be able to measure the weight of the waste 40 gripped by the bucket 253. Data on the weight of the waste 40 measured by the load meter 251 (load data) is transmitted to the management device 10 via the communication unit 22.

[0040] Parameters related to the work operation of the crane 252 provided above the storage pit 26 in the two dimensions (x, y) of the horizontal plane in the storage pit 26 are sequentially transmitted from the gripper 25 to the management device 10 via the control unit 21 and the communication unit 22. Examples of parameters related to the work operation of the crane 252 in the two dimensions (x, y) include, but are not limited to, a current position, a moving path, a moving range, and a moving speed. Similarly, parameters related to the work operation of the bucket 253 in the three dimensions (x, y, z) are sequentially transmitted from the gripper 25 to the management device 10 via the control unit 21 and the communication unit 22. Examples of parameters related to the work operation of the bucket 253 in the three dimensions (x, y, z) include, but are not limited to, a current position, an opening time, a closing time, a number of opening and closing times, and an opening amount.

[0041] As shown in FIG. 2, the storage pit 26 in the waste storage facility 20 is provided outside the incinerator 33. A gripping unit 25 is provided above the storage pit 26. A crane 252 constituting the gripping unit 25 is provided above the storage pit 26 equipped with the input hopper 331 of the incinerator 33. The crane 252 is provided with a wire rope 254 that can be wound up and down and a crab 255 equipped with a traverse device, which are arranged so as to be able to travel along a crane girder 256. The crane 252 is configured to be able to raise and lower a bucket 253 capable of gripping a predetermined amount of waste 40 by winding up and lowering the wire rope 254 on the crab 255 arranged so as to be able to traverse on the crane girder 256. The bucket 253 is configured to be able to freely rise and fall between the top of the storage pit 26 and inside the storage pit 26. The bucket 253 grasps the waste 40 in the storage pit 26, rises, and moves laterally to a position above the input hopper 331, after which the bucket 253 drops and inputs the waste 40 into the input hopper 331. Thus, the gripping section 25 is configured to be able to grasp and lift the waste 40 from within the storage pit 26 and input it into the input hopper 331.

[0042] In this embodiment, the bucket 253 is provided with the moisture meter 241 described above. At least one moisture meter 241 is provided for each bucket 253. The moisture meter 241 detects the capacitance of the waste 40 by contacting the detection element with the waste 40 held by the bucket 253, and can measure the moisture content. That is, the moisture meter 241 can measure the moisture content by deriving the corresponding moisture content from the relationship between the capacitance value of the waste and the moisture content value stored in advance from the capacitance value measured by the capacitance meter. Note that the management device 10 may obtain the capacitance measured by the moisture meter 241 and derive the moisture content. The management device 10 may store in the storage unit 12 a moisture content relational database that is generated by measuring the relationship between the capacitance of the waste 40 and the moisture content of the waste 40 in advance, and derive the moisture content of the waste 40 by comparing the measurement value of the capacitance of the waste sent from the detection element with the relationship between the capacitance and the moisture content in the relational database.

[0043] (Waste incineration facility) As shown in FIG. 1, the waste incineration facility 30 as a waste incineration unit includes a combustion control device (ACC) 31, a sensor unit 32, and an incinerator 33. The combustion control device 31 controls the combustion air volume, the cooling air volume, the waste feeder feed speed, and the grate feed speed as the operation quantities of the respective operation terminals based on the setting of a predetermined operation quantity reference value. The sensor unit 32 includes a moisture meter 321 as a moisture percentage measurement unit. The sensor unit 32 further includes, for example, various thermometers, various pressure gauges, various concentration meters, and various exhaust gas flow meters provided at various locations. The incinerator 33 as a waste incinerator includes a waste inlet (inlet hopper 331) for inputting waste 40, a furnace in which the waste 40 is combusted, and a boiler (not shown). The internal state of the incinerator 33 and various physical quantities such as pressure and speed in facilities related to the incinerator 33, specifically, in a power generation facility for generating electricity, are output as sensor information measured by the sensor unit 32. The sensor information output from the sensor unit 32 is supplied as parameters to the combustion control device 31. The combustion control device 31 controls the combustion in the incinerator 33 based on the input parameters.

[0044] As shown in Fig. 2, the incinerator 33 according to this embodiment is, for example, a grate-type waste incinerator. The incinerator 33 is not limited to a grate-type waste incinerator, and may be another type of waste incinerator, such as a gasification melting furnace. The incinerator 33 includes a waste incinerator, a waste gasification melting furnace, and the like.

[0045] The incinerator 33 is configured such that combustion air is introduced from a plurality of air supply systems 333 connected to the bottom of the grate 332, and the waste 40 on the grate 332 is incinerated in a combustion chamber 334 formed above the grate 332. The grate 332 moves the waste 40 toward the right in FIG. 2 in the combustion chamber 334 by a feed speed controlled by reciprocating movement. A dust feeder 335 such as a pusher is provided above the left end of the grate 332 in FIG. 2. The dust feeder 335 sends the waste 40 onto the grate 332. A chute section 336 is provided above the dust feeder 335, extending upward. An input hopper 331 is provided at the top end of the chute section 336 as a waste input port. The amount of air blown in the air supply system 333, the feed speed of the grate 332, and the discharge speed of the dust feeder 335 are variable.

[0046] In this embodiment, the chute section 336 of the incinerator 33 in the waste incineration facility 30 is provided with a moisture meter 321 that measures the moisture content of the waste 40 before incineration in the chute section 336. The moisture meter 321 has a configuration similar to that of the moisture meter 241 described above. The moisture meter 321 has a detection element, and at least the detection element is attached to the inner surface of the side wall of the chute section 336. In FIG. 2, the detection element of the moisture meter 321 is provided in a lower layer area below half the height of the chute section 336, but it may be provided in an upper layer area above half. The detection element can detect the moisture content of the high-pressure, high-density waste 40 present in the chute section 336. The detected moisture content is transmitted to the management device 10 via the control unit 21.

[0047] (Management device) Fig. 3 shows details of the management device 10 in Fig. 1. The management device 10 as an information processing device shown in Fig. 3 includes a control unit 11, a storage unit 12, a communication unit 13, and an input / output unit 14. The control unit 11 and the communication unit 13 are physically similar to the above-mentioned control unit 21 and communication unit 22, respectively.

[0048] The storage unit 12 is composed of a storage medium selected from a volatile memory such as a RAM, a non-volatile memory such as a ROM, an erasable programmable ROM (EPROM), a hard disk drive (HDD, Hard Disk Drive), and a removable medium. The removable medium is, for example, a universal serial bus (USB) memory, or a disk recording medium such as a compact disc (CD), a digital versatile disc (DVD), or a Blu-ray (registered trademark) disc (BD). The storage unit 12 may also be composed of a computer-readable recording medium such as an externally mountable memory card.

[0049] The storage unit 12 can store an operating system (OS), various programs, various tables, various databases, and the like for executing the operations of the management device 10. These various programs can also be recorded on computer-readable recording media such as hard disks, flash memories, CD-ROMs, DVD-ROMs, and flexible disks and widely distributed. The control unit 11 loads the programs stored in the storage unit 12 into the working area of ​​the main storage unit, executes them, and controls each component through the execution of the programs, thereby achieving functions that meet a predetermined purpose.

[0050] The storage unit 12 stores a moisture content determination model 121, type information 122, deposition information 123, imaging information 124, and moisture percentage information 125. The type information 122, deposition information 123, imaging information 124, and moisture percentage information 125 are all stored in the storage unit 12 as a database in a searchable manner.

[0051] The moisture content determination model 121 is composed of a learning model in which the image information 124 acquired from the imaging unit 23 that images the waste 40 in the storage pit 26 is used as an input parameter, and moisture information representing the moisture percentage ρ for each address (x, y) of the waste 40 is used as an output parameter. The learning model is also referred to as a trained model or simply a model. Furthermore, the moisture percentage ρ is typically a continuous numerical value, but may also be a discontinuous numerical value, rank, level, etc.

[0052] In this embodiment, the moisture content determination model 121 uses the imaging information 124 obtained and annotated from the imaging unit 23 that images the waste 40 in the storage pit 26 as a learning input parameter. The imaging information 124 includes information such as the time when the image was captured or the elapsed time. The moisture content determination model 121 uses information on the measured value of the moisture content ρ in the chute unit 336 (moisture content information 125) as a learning output parameter. The moisture content information 125 includes information such as the measured time or the elapsed time. In other words, the imaging information 124, which is an input parameter for learning, and the moisture content information 125, which is an output parameter for learning, are information that can be synchronized with each other by time, elapsed time, etc. In other words, the time it takes for the waste 40 input into the input hopper 331 to reach the installation position of the moisture meter 321 can also be incorporated into the moisture content determination model 121. The moisture content determination model 121 is generated by machine learning such as supervised learning, using input and output data sets as teacher data from imaging information 124 along a time series as an input parameter for learning and moisture percentage information 125 along a time series as an output parameter for learning. Note that the machine learning can employ various types of machine learning such as deep learning using a neural network. As a result, when imaging information 124 captured by imaging cameras 231, 232 is input as an input parameter, the moisture content determination model 121 can output moisture percentage information 125 of the moisture percentage ρ for each address (x, y) of the input waste 40 as an output parameter.

[0053] The type information 122 includes information on the type of waste 40 in the storage pit 26. The type information 122 includes, for example, information on the type determined by assuming that the waste 40 carried in is one type when the waste collection vehicle carries the waste 40 into the storage pit 26, but is not necessarily limited to this. The type information 122 includes type information for each predetermined position defined by three-dimensional coordinates (x, y, z) in the storage pit 26 after the waste 40 is transferred by the gripper 25. In other words, the type information 122 includes information on the type of waste 40 determined in association with the three-dimensional position coordinates (x, y, z) of the surface of the waste 40 when the surface shape of the waste 40 is measured.

[0054] The deposition information 123 includes information on the moisture percentage ρ of the waste 40 stored in the storage pit 26. The moisture percentage ρ is also referred to as the moisture content or moisture concentration. The deposition information 123 includes measurement information on the moisture percentage ρ of the waste 40 associated with a specific position (x, y) such as a two-dimensional coordinate in the storage pit 26, i.e., an address.

[0055] The deposition information 123 may include mapping data including point cloud data representing the shape of the surface of the waste 40. The deposition information may also include information regarding the level of the waste 40 stored in the storage pit 26. The deposition information 123 may include measurement information of a height level (waste level z) associated with the two-dimensional coordinates (x, y) of the waste 40 in the storage pit 26. In other words, the deposition information may include information of the three-dimensional coordinates of the waste 40 in the storage pit 26.

[0056] Furthermore, the deposition information 123 may include information generated in association with the type information 122 of the determined waste 40. That is, the deposition information 123 may include three-dimensional map information generated in association with the type information 122 captured by the imaging unit 23 and determined by the type determination unit 112, with three-dimensional coordinates (x, y, z) based on the deposition information 123 of the stored waste 40. This allows the control unit 11 to accumulate, as the deposition information 123, information on not only the surface of the waste 40 stored in the storage pit 26, but also information on the waste 40 buried below the surface.

[0057] In addition, when the waste 40 in the storage pit 26 is managed as multiple units divided into, for example, a predetermined rectangular prism shape, specifically, a cube shape of, for example, 500 mm square, the deposition information 123 may include historical information such as the volume, bulk density, weight, and number of days of storage of each unit.

[0058] The deposition information 123 may include work area information regarding the work area in the storage pit 26. The work area information includes information regarding areas such as the carry-in area (carry-in area), the transfer area (transfer area), and the throw-in area (throw-in area). The work area information may also include information on the crushing area (crushing area), which is an area where garbage is crushed by the bucket 253 or the like. This allows the control unit 11 to separate the deposition information for each of the carry-in area, the transfer area, the throw-in area, and the crushing area.

[0059] In this embodiment, the functions of a moisture amount derivation unit 111, a type determination unit 112, a pile information generation unit 113, a crane control unit 114, a combustion acquisition unit 115, and a learning unit 116 are executed by executing various programs loaded by the control unit 11. The various programs also include a program for realizing an artificial intelligence or a learned model capable of realizing the processing according to this embodiment.

[0060] The moisture amount derivation unit 111 as a moisture amount determination unit can set an area for the waste 40 stored in the storage pit 26. Specifically, for example, the horizontal size of the bucket 253 can be used as a reference to divide the area on a plane parallel to the bottom surface of the storage pit 26 and set an address (x, y). It is also possible to set areas such as a carry-in area, a transfer area, and a throw-in area based on the address (x, y). The carry-in area 261 is an area where garbage is carried in from a garbage collection vehicle (packer vehicle), and may be divided by a partition wall. The transfer area is a transfer area where the waste 40 is transferred from the carry-in area by a crane 252 provided in the storage pit 26. The throw-in area is an throw-in area where the waste 40 to be thrown into the incinerator 33 is stored.

[0061] The moisture content derivation unit 111 can calculate or determine the moisture content ρ of the waste 40 in the chute unit 336 of the waste incineration facility 30 and the change in the moisture content ρ over time, based on the imaging information provided from the imaging cameras 231, 232 of the imaging unit 23. That is, the moisture content derivation unit 111 reads out the moisture content determination model 121 from the storage unit 12. The moisture content derivation unit 111 inputs the imaging information 124 of the waste 40 in the storage pit 26, which is continuously or intermittently acquired from the imaging unit 23 along a time series, as an input parameter to the moisture content determination model 121. The moisture content derivation unit 111 outputs the moisture content information 125, which is the moisture content ρ along a time series of the waste 40 in the chute unit 336, as an output parameter from the moisture content determination model 121.

[0062] In addition, the moisture content derivation unit 111 may calculate or determine the moisture content ρ of the waste 40 in the storage pit 26 of the waste storage facility 20 based on at least one, and preferably multiple, measurement values ​​supplied from the moisture meter 241 of the sensor unit 24.

[0063] When the moisture content deriving unit 111 is capable of measuring the moisture content ρ of the waste 40 in the storage pit 26, it may derive the moisture content ρ of the waste 40 for each rectangular region (section) partitioned at a predetermined interval in the x direction and at a predetermined interval in the y direction, as necessary. Moisture content information 125, which is the measured value of the moisture content ρ for each predetermined section based on the position coordinates (x, y), is stored in the memory unit 12.

[0064] The type determination unit 112 determines the type (also called classification) of the waste 40 carried into the carry-in area 261. Furthermore, when the waste 40 is mixed or reloaded by the crane 252, the type determination unit 112 determines the type and state of the waste 40 after mixing or reloading based on the imaging information supplied from the imaging cameras 231, 232. In this way, the type determination unit 112 generates type information 122. The type information 122 includes information on the type and state of the waste 40.

[0065] The deposition information generating unit 113 generates deposition information on the waste 40 at a predetermined position (x, y). That is, the deposition information generating unit 113 can derive the height of the waste 40 (waste level z) based on the distance to the predetermined position (x, y) measured by the sensor unit 24. That is, the deposition information generating unit 113 can map at least the surface of the waste 40 in the storage pit 26 with three-dimensional coordinates (x, y, z). This allows the deposition information generating unit 113 to instantly or intermittently derive the shape of the surface of the waste 40, and generate deposition information 123 including point cloud data in which the surface shape of the waste 40 is mapped. The deposition information 123 includes three-dimensional mapping information of the surface shape of the waste 40.

[0066] Specifically, when the sensor unit 24 includes, for example, LiDAR, the electromagnetic waves used are typically near-infrared or infrared. As a result, while cameras and the like have difficulty capturing images in dark places, LiDAR makes it possible to measure distances even in dark places. In addition, the dimensions of objects that can be detected by LiDAR are equal to or larger than the wavelength. The electromagnetic waves emitted from the LiDAR have a high light flux density, high coherence, and an extremely short wavelength, so that they are easily reflected even when irradiated on a small object. Therefore, the LiDAR emits electromagnetic waves toward the waste 40, and it becomes easier to measure the time from the time the electromagnetic waves are emitted to the time the electromagnetic waves reflected by the waste 40 are received. This allows the LiDAR to easily measure the distance to the surface or surface layer of the waste 40 stored in the storage pit 26 with high accuracy. In addition, since the distance is measured by the LiDAR or the like, the time response can be made to the order of msec, so that the measurement interval can be shortened and the distance from the LiDAR to the waste 40 can be measured almost continuously.

[0067] In addition, when the bucket 253 is moving in the storage pit 26, if there is only one distance measuring sensor such as LiDAR, the electromagnetic waves of one of the LiDARs may be blocked by the bucket 253 and not irradiated to the waste 40 at a predetermined position. In this case, by providing multiple distance measuring sensors, i.e., multiple LiDARs, as the sensor unit 24, it is possible to ensure that the electromagnetic waves emitted from at least one of the LiDARs can be irradiated to the waste 40 without being blocked by the bucket 253. Therefore, even when the bucket 253 is moving in the storage pit 26, the distance to the waste 40 can be measured from at least one of the multiple LiDARs, so that the possibility of measuring the moisture content ρ at a predetermined position is improved compared to the case where only one distance measuring sensor is used. In other words, even when the bucket 253 is moving, the height of the waste 40 in the storage pit 26 can be measured by the sensor unit 24 instantly with a higher probability.

[0068] As described above, the upper layer of the waste 40 can be measured by at least one of the multiple LiDARs. That is, as shown in FIG. 2, consider a case where two LiDARs (sensor unit 24) are provided near the imaging cameras 231 and 232, respectively. In this case, the waste level z at the predetermined position P(x, y) can be specified by the distance l1 from the LiDAR near the imaging camera 231 to the predetermined position P(x, y) and the angle θ1 with the horizontal. Similarly, the waste level z at the predetermined position P(x, y) can be specified by the distance l2 from the LiDAR near the imaging camera 232 to the predetermined position P(x, y) and the angle θ2 with the horizontal. By deriving the waste level z by the two LiDARs, the waste level z can be measured over all addresses (x, y) of the waste 40 in the storage pit 26. In addition to LiDAR, it is also possible to use the crane rope length of the bucket 253 to accumulate information on the waste level z of the waste 40 at a predetermined position (x, y) and derive pile information corresponding to the three-dimensional coordinates (x, y, z) of the waste 40. The method of deriving the waste level z is not necessarily limited to the above-mentioned method, and various methods can be adopted.

[0069] The deposition information generating unit 113 associates type information 122 of the waste 40 determined by the type determining unit 112 with three-dimensional coordinates (x, y, z) based on deposition information 123 of the waste 40 at a predetermined position obtained based on measurement by the sensor unit 24. This allows a three-dimensional map of the waste 40 to be generated continuously. The generated deposition information 123 is stored in the memory unit 12. The deposition information generating unit 113 may generate the deposition information continuously or discontinuously at predetermined time intervals.

[0070] The crane control unit 114, which serves as a gripping unit control unit, outputs instruction signals to drive the crane 252 on three axes to grip waste 40 at a desired position with the bucket 253, and instruction signals to scatter the gripped waste 40, and transmits these to the control unit 21. The control unit 21 controls the crane 252 and bucket 253 of the gripping unit 25 based on the received instruction signals. Note that the crane 252 may also be directly controlled by the crane control unit 114. The crane control unit 114 is capable of transferring waste based on the acquired pile information 123 and moisture percentage information 125.

[0071] The combustion acquisition unit 115 as a combustion state acquisition unit acquires the combustion state of the waste 40 inside the incinerator 33 from the sensor unit 32 of the waste incineration facility 30. The combustion state may be, for example, measurement values ​​from various thermometers, various concentration meters, and exhaust gas flow meters as combustion state quantities inside the incinerator 33 during operation, or combustion process measurement values ​​such as the amount of waste 40 cut out. The combustion state is not limited to these state quantities, and other state quantities inside the incinerator 33 may also be used. The combustion acquisition unit 115 stores combustion information 126 including the acquired combustion state quantities in the memory unit 12. The combustion information 126 includes information such as the measured time or elapsed time.

[0072] Furthermore, the learning unit 116 as a learning means can derive a moisture content determination model 121 by machine learning using the captured image information of the waste 40 in the storage pit 26 as a learning input parameter and the moisture content set for the acquired image information as a learning output parameter. The generated moisture content determination model 121 is supplied to the learning unit 116 from the computer that performed the machine learning and stored in the memory unit 12.

[0073] Next, the storage pit 26 will be described. Fig. 4 is a plan view from above along the horizontal plane of the storage pit 26 according to this embodiment. In the example shown in Fig. 4, for example, the horizontal size of the bucket 253 is used as a reference, and a plane parallel to the bottom surface of the storage pit 26 is divided and addresses (x, y) are set. Note that various division methods can be used for dividing the storage pit 26. Furthermore, regarding the address (x, y), when the size of the bucket 253 is used as a reference, both variables x and y are discrete, but they may also be set continuously.

[0074] Waste 40 is dumped from a refuse collection vehicle into storage pit 26 toward loading area 261 after an entrance door provided in storage pit 26 is opened. The entrance doors are, for example, of an openable / closable type, and a plurality of such doors are provided at predetermined intervals along the side wall of storage pit 26. Waste 40 to be brought into waste storage equipment 20 of waste treatment facility 3 is dumped into storage pit 26 through one of the entrance doors. The waste 40 is transferred, mixed, and dumped into dump hopper 331 in storage pit 26 by three-axis drive of crane 252.

[0075] At least one, and preferably multiple, imaging cameras 231, 232 are disposed as the imaging unit 23, for example, near the upper waist wall of the storage pit 26. When multiple cameras are installed, it is preferable to install them in a position where there is a low possibility of a blind spot occurring, for example, on the side of the waste 40 loading area 261, in order to suppress the occurrence of a blind spot of the camera. In FIG. 4, two imaging cameras 231, 232 are provided. The imaging cameras 231, 232 are configured to have a photographing camera such as a CCD camera or a CMOS camera, but are not necessarily limited to these. The imaging cameras 231, 232 are configured to be able to identify the color tone of the waste 40, which changes depending on the type of waste 40.

[0076] The imaging cameras 231, 232 may be installed, for example, on the upper part of the inner wall of the storage pit 26, on a wall capable of capturing images of the waste 40 in the storage pit 26. The imaging cameras 231, 232 may be one, or three or more cameras may be installed. Various devices can be used as the imaging unit 23 as long as they are capable of detecting the type of waste 40. In addition, the LiDAR included in the sensor unit 24 described above can be installed near the imaging cameras 231, 232.

[0077] The input / output unit 14 can be composed of, for example, a touch panel display, a speaker microphone, and the like. The input / output unit 14 as an input means includes an interface that inputs various information transmitted from, for example, the imaging unit 23 or the sensor unit 24 installed in the waste storage facility 20 through the communication unit 22 and outputs the information to the control unit 11. The information may be transmitted from the imaging unit 23 or the sensor unit 24 to the input / output unit 14 by wired communication or wireless communication. The input / output unit 14 also includes a user interface such as a keyboard, input buttons, a lever, a touch panel for manual input superimposed on a display such as a liquid crystal display, or a microphone for voice recognition. The input / output unit 14 is configured to be capable of inputting predetermined information to the control unit 11 by an operator or the like operating the input / output unit 14. The input / output unit 14 as an output means displays an image of the inside of the storage pit 26 of the waste storage facility 20 on a display monitor, displays characters, figures, and the like on the screen of a touch panel display, and outputs sound from a speaker, according to the control by the control unit 11. That is, the input / output unit 14 is configured to be capable of notifying predetermined information to the outside. The input section and the output section in the input / output section 14 may be configured separately.

[0078] (Operation method of incineration management system) Next, a method for operating the incineration management system 1 including the waste treatment facility 3 according to this embodiment will be described. Fig. 5 is a flow chart for explaining a method for operating the waste storage facility 20 of the incineration management system controlled by the management device 10. In the following description, information is transmitted and received between the respective components via the communication units 13, 22 and the network 2, but a detailed description of this point will be omitted.

[0079] As shown in Fig. 5, first, in step ST1, the moisture amount derivation unit 111 of the control unit 11 of the management device 10 controls the imaging unit 23 via the control unit 21 of the waste storage facility 20 to capture an image of the waste 40 in the storage pit 26. The imaging unit 23 generates imaging information 124 of the waste 40 and transmits it to the management device 10. Based on the imaging information 124 supplied from the imaging unit 23, the moisture amount derivation unit 111 generates deposition information 123 associated with the position (x, y) or three-dimensional position (x, y, z) (hereinafter collectively referred to as position) of the captured waste 40, and stores it in the memory unit 12.

[0080] Next, the process proceeds to step ST2, where the type determination section 112 of the control section 11 determines the type and state of the captured waste 40 based on the imaging information 124 supplied from the imaging section 23, and stores this as type information 122 in the memory section 12. The pile information generation section 113 also generates pile information 123 by correlating the generated type information 122 with the position of the waste 40, and stores this in the memory section 12. This makes it possible to distinguish the type and state of the waste 40 by correlating it with the position (x, y).

[0081] Next, the process proceeds to step ST3, where the moisture meter 321 measures the moisture percentage ρ of the waste 40 in the chute section 336. The moisture meter 321 measures the moisture percentage of the waste 40 in the chute section 336 before it is carried into the incinerator 33, i.e., before it is burned. It is preferable that the moisture meter 321 measures the moisture percentage ρ of the waste 40 in the chute section 336 continuously, but it may be measured intermittently. The moisture amount derivation section 111 acquires the measurement value of the moisture percentage ρ measured by the moisture meter 321.

[0082] Next, proceeding to step ST4, the moisture content derivation unit 111 inputs the acquired imaging information 124 as an input parameter to the moisture content determination model 121. The moisture content determination model 121 outputs moisture content information 125 including the moisture content ρ for each address (x, y) of the storage pit 26 as an output parameter.

[0083] Next, proceeding to step ST5, the combustion acquisition unit 115 acquires measurements related to the combustion state, such as combustion process measurements, from the sensor unit 32 of the waste incineration facility 30. The combustion acquisition unit 115 stores the combustion information 126 in the storage unit 12. Note that the processes of the above-mentioned steps ST1 to ST5 can be performed in parallel as appropriate, and the order of execution is not limited to the above-mentioned order.

[0084] Proceeding to step ST6, the crane control unit 114 reads out the combustion information 126 from the memory unit 12 and selects one of the mixing methods, the moisture-based mode (1) or the combustion-based mode (2). The moisture-based mode (1) is a mode in which the waste 40 is mixed so that the distribution of the moisture percentage ρ is approximately constant and approximately uniform for each address (x, y) in the storage pit 26. The combustion-based mode (2) is a mode in which the waste 40 is mixed or fed so that the moisture percentage ρ is adjusted based on the combustion information 126 including information on the combustion state in the incinerator 33.

[0085] If the crane control unit 114 selects the moisture-based mode based on the combustion information 126 in step ST6 (step ST6:1), the process proceeds to step ST7. In step ST7, the crane control unit 114 controls the crane 252 in the gripper 25 so that the distribution of the moisture percentage of the waste 40 in the storage pit 26 becomes substantially uniform throughout. For example, the crane control unit 114 moves the waste 40 so as to mix the waste 40 at the address (x, y) in the storage pit 26 having a high moisture percentage ρ with the waste 40 at the other address (x', y') having a low moisture percentage ρ. This makes it possible to make the moisture percentage ρ of the waste 40 in the storage pit 26 substantially uniform over a predetermined range, for example, over the entirety. The high or low moisture percentage ρ can be determined by setting a predetermined value for the moisture percentage ρ in advance and determining whether the moisture percentage ρ is equal to or higher than the set value or lower than the predetermined value.

[0086] Thereafter, the process proceeds to step ST8, where the crane control unit 114 determines whether the mixing operation has been completed. If the crane control unit 114 determines that the mixing operation has been completed (step ST7: Yes), the process returns to step ST2. On the other hand, if the crane control unit 114 determines that the mixing operation has not been completed (step ST7: No), the process returns to step ST4.

[0087] On the other hand, if the crane control unit 114 selects the combustion standard mode based on the combustion information 126 in step ST6 (step ST6:2), the process proceeds to step ST9. In step ST9, the crane control unit 114 derives the desired moisture percentage ρ to be supplied to the incinerator 33 as the supply moisture percentage based on the combustion information 126. The crane control unit 114 selects an address for holding the waste 40 and an address for releasing the waste 40 from among the multiple addresses in the storage pit 26 so that the moisture percentage ρ approximately matches the supply moisture percentage, and controls the crane 252 to agitate the waste.

[0088] That is, the crane control unit 114 controls the crane 252 in the gripping unit 25 based on the combustion information 126 so as to adjust the moisture percentage ρ to a value that will result in a desired combustion state inside the incinerator 33. In this case, the adjustment of the moisture percentage ρ is performed on the waste 40 that is transported from the storage pit 26 and fed into at least the feeding hopper 331. As a result, specifically, for example, when the combustion information 126 acquired from the sensor unit 32 indicates a large amount of steam generation or a high temperature inside the furnace, the crane control unit 114 can perform control to feed waste 40 with a high moisture percentage ρ. Also, when the combustion information 126 acquired from the sensor unit 32 indicates a small amount of steam generation or a low temperature inside the furnace, the crane control unit 114 can perform control to feed waste 40 with a low moisture percentage ρ.

[0089] Next, the process proceeds to step ST8, and if the crane control unit 114 determines that the mixing operation has ended (step ST8: No), the process returns to step ST2. On the other hand, if the crane control unit 114 determines that the mixing operation has not ended (step ST8: No), the process returns to step ST4.

[0090] The processing of steps ST1 to ST9 is repeatedly executed during operation of the waste storage facility 20. The operation of the waste storage facility 20 by the management device 10 is executed in the above manner.

[0091] In the embodiment described above, the combustion calories of the waste 40 are the main operating index in the operation of the incinerator 33 that incinerates the waste 40. In this regard, since the combustion calories of general waste 40 have a strong correlation with the magnitude of the moisture percentage ρ, the combustion calories of the waste 40 can be accurately estimated by measuring the moisture percentage ρ. Furthermore, by performing stirring work or inputting the waste 40 into the input hopper 331 based on the measured moisture percentage ρ, the combustion state can be adjusted based on the moisture percentage ρ. Furthermore, by using the image information 124 of the waste 40 in the storage pit 26 as an input parameter for learning, and using the measured value of the moisture percentage ρ by the moisture meter 321 provided in the chute section 336 and the address (x, y) of the waste 40 input into the input hopper 331 as output parameters for learning, the moisture content determination model 121 can be generated, making it possible to derive an estimate of the moisture percentage ρ and the combustion calories of the waste 40 for each address (x, y) in the storage pit 26. This makes it possible to more appropriately mix the waste 40 in the storage pit 26 by mixing the waste 40 based on the moisture percentage ρ determined from the imaging information 124 and the moisture percentage information 125 including the combustion calories. That is, the moisture percentage ρ of the waste 40 in the storage pit 26 can be made substantially uniform as necessary, and in some cases, the waste 40 can be mixed or reloaded based on the combustion state of the waste 40 in the incinerator 33, making it possible to stabilize the combustion state in the incinerator 33.

[0092] Although one embodiment of the present invention has been specifically described above, the present invention is not limited to the above-mentioned embodiment, and various modifications based on the technical idea of ​​the present invention are possible. For example, the numerical values ​​given in the above-mentioned embodiment are merely examples, and numerical values ​​different from these may be used as necessary, and the present invention is not limited by the description and drawings that form a part of the disclosure of the present invention according to this embodiment.

[0093] In the embodiment described above, the moisture meter 241 is configured as a capacitance contact moisture meter, but it is also possible to adopt a microwave contact moisture meter, an infrared moisture meter, or the like.

[0094] In the embodiment described above, two imaging cameras 231, 232 are provided at two corners in the upper part of the storage pit 26, but four imaging cameras may be provided at four corners in the upper part of the storage pit 26. Similarly, four distance measuring sensors may be provided at the four corners. In this case, blind spots of one or two imaging cameras 231, 232 can be captured by at least one of the other two imaging cameras, so that blind spots can be reduced.

[0095] In the embodiment described above, the moisture-based mode (1) and the combustion-based mode (2) are used as mixing and reloading modes controlled by the crane control unit 114, but other modes can also be used. Furthermore, the power generation-based mode (3) can be added to accommodate cases where the amount of power generation changes depending on the time of day. In this case, the combustion calories required as the combustion calories of the waste 40 to be supplied to the incinerator 33 can be derived according to the amount of power generation, and mixing can be controlled so that the waste 40 with a moisture content ρ according to the derived combustion calories is supplied to the incinerator 33.

[0096] In addition, when artificial intelligence is used in the above-described embodiment, deep learning using a neural network is used as an example of machine learning, but machine learning or statistical methods based on other methods may be used. For example, other supervised learning such as a support vector machine, a decision tree, a naive Bayes method, or a k-nearest neighbor method may be used. Semi-supervised learning may be used instead of supervised learning.

[0097] In addition, in one embodiment, the above-mentioned "unit" can be read as a "circuit" etc. For example, a control unit can be read as a control circuit.

[0098] In the description of the flowcharts in this specification, the order of processing between steps is clearly indicated using expressions such as "first," "then," and "continue," but the order of processing required to implement this embodiment is not uniquely determined by these expressions. In other words, the order of processing in the flowcharts described in this specification can be changed as long as there is no contradiction.

[0099] Further advantages and modifications can be readily derived by those skilled in the art. The disclosure in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Thus, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and equivalents thereof. [Explanation of symbols]

[0100] 1 Incineration management system 2 Network 3 Waste treatment facilities 10 Management device 11,21 Control section 12 Storage section 13,22 Communications Department 14 Input / output section 20 Waste storage facility 23 Imaging unit 24,32 Sensor section 25 Gripping part 26 Storage Pit 30 Waste incineration facility 31 Combustion control device 33 Incinerator 40 Waste 111 Moisture content derivation part 112 Type determination section 113 Deposition information generation section 114 Crane control unit 115 Combustion acquisition unit 116 Learning Department 121 Moisture content determination model 122 Type Information 123 Deposition information 124 Imaging Information 125 Moisture content information 126 Combustion Information 231,232 Imaging camera 241,321 Moisture meter 251 Load cell 252 Crane 253 Bucket 254 Wire Rope 255 Club 256 Crane Girder 261 Loading area 331 Input hopper 332 Grate 333 Air Supply System 334 Combustion Chamber 335 Dust supply device 336 Shooting Club

Claims

1. An information processing device including a control unit that manages waste stored in a storage pit configured to be able to store waste, and waste that has been removed from the storage pit and is yet to be incinerated in an incinerator, A storage unit that stores imaging information acquired from an imaging unit that images the waste stored in the storage pit as input parameters; A control unit that inputs the input parameters read from the storage unit into a moisture content determination model and outputs a moisture content corresponding to an address in the storage pit in the waste stored in the storage pit as an output parameter, The moisture content determination model is a learning model that is generated in advance by at least one of machine learning and statistical techniques, with image information of the waste stored in the storage pit as a learning input parameter, and a moisture content obtained by a moisture meter that measures the moisture content of the waste after it is removed and before it is incinerated and that is synchronized with the image information as a learning output parameter. Information processing device.

2. The control unit is The moisture content is determined for each address, and stirring of the waste in the storage pit is controlled based on the moisture content. The information processing device according to claim 1 .

3. The control unit is Acquire combustion information indicating a combustion state of waste in the incinerator; Based on the combustion information and the derived moisture content of the waste in the storage pit, a control is performed to change a mixing method for mixing the waste in the storage pit.

3. The information processing device according to claim 1 or 2.

4. The control unit is determining the moisture content for each address; When the mixing method is a moisture-based mode, an address for holding the waste and an address for dumping the waste are selected from a plurality of addresses in the storage pit based on the moisture content so that waste at an address where the moisture content is equal to or greater than a predetermined set value and waste at an address where the moisture content is less than the predetermined set value are mixed together. The information processing device according to claim 3 .

5. The control unit is determining the moisture content for each address; When the mixing method is the combustion standard mode, a moisture content to be supplied to the incinerator is selected based on the combustion information, and an address for holding the waste and an address for releasing the waste are selected from a plurality of addresses in the storage pit based on the moisture content so that the moisture content approximately matches the supply moisture content, and the waste is mixed. The information processing device according to claim 3 .

6. An information processing method executed by a control unit that manages waste stored in a storage pit configured to be able to store waste, and waste that has been transported from the storage pit and has not yet been incinerated in an incinerator, comprising: The imaging information acquired from the imaging unit that images the waste stored in the storage pit is stored in a storage unit as an input parameter; The input parameters read from the storage unit are input into a moisture content determination model, and a moisture content of the waste stored in the storage pit corresponding to an address in the storage pit is output as an output parameter; The moisture content determination model is a learning model that is generated in advance by at least one of machine learning and statistical techniques, with image information of the waste stored in the storage pit as a learning input parameter, and a moisture content obtained by a moisture meter that measures the moisture content of the waste after it is removed and before it is incinerated and that is synchronized with the image information as a learning output parameter. Information processing methods.

7. A control unit of an information processing device including a control unit for managing waste stored in a storage pit configured to be able to store waste and waste after being transported from the storage pit and before being incinerated in an incinerator, A moisture content determination model is generated by at least one of machine learning and statistical techniques, using image information of the waste stored in the storage pit as an input parameter for learning, and a moisture content obtained by a moisture meter that measures the moisture content of the waste after the waste is removed and before incineration and synchronized with the image information as an output parameter for learning, and is stored in a memory unit; Imaging information acquired from an imaging unit that images the waste stored in the storage pit is stored in the storage unit as an input parameter; The input parameters read from the storage unit are input to the moisture content determination model read into the storage unit, and a moisture content corresponding to an address in the storage pit in the waste stored in the storage pit is output as an output parameter. A program to make that happen.

8. An information processing device according to any one of claims 1 to 5; a storage pit for storing waste; An imaging unit capable of imaging the waste stored in the storage pit; a gripping unit capable of stirring the waste by gripping and releasing the waste under the control of the information processing device; a moisture content measuring unit capable of measuring the moisture content of the waste after it has been discharged and before it is incinerated; Incineration management system.

9. The waste material is further provided with an incinerator capable of incinerating the waste material after the waste material is removed. The incineration management system according to claim 8.

10. A storage pit configured to be capable of storing waste and equipped with an imaging unit capable of imaging the waste; an information processing device having a control unit that manages the waste stored in the storage pit and the waste after being removed from the storage pit and before being incinerated in an incinerator, and controls a gripping unit that moves the waste stored in the storage pit by gripping and releasing it, and a memory unit that stores imaging information of the waste in the storage pit obtained from the imaging unit; An incinerator capable of incinerating the waste after the removal; A method for operating an incineration management system comprising: The control unit: The imaging information read from the storage unit is input as an input parameter to a moisture content determination model, and a moisture content of the waste stored in the storage pit corresponding to an address in the storage pit is output as an output parameter; Obtaining combustion information indicating a combustion state of waste from the incinerator; Based on the combustion information and the moisture content of the waste in the storage pit, which is acquired by a moisture meter that measures the moisture content of the waste in the storage pit and is synchronized with the imaging information, a control is performed to change a mixing method for mixing the waste in the storage pit; The moisture content determination model is a learning model that is generated in advance by at least one of machine learning and statistical techniques, with image information of the waste stored in the storage pit as a learning input parameter and the moisture content of the waste after removal and before incineration as a learning output parameter. How to operate the incineration management system.

Images