JP7613327B2 - Information processing device, information processing method, program, incineration management system, and operation method of incineration management system - Google Patents

Description

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.

Waste of various types, such as household garbage, pruning branches, and sludge, are brought into waste incineration facilities 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 the waste to a specified level. After the mixing process, the waste is loaded into an incinerator using a crane or other device and incinerated.

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

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 operating history of the waste treatment plant and labels on which workers classify the quality of waste 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 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 operating history of the waste treatment plant, and labels on which workers classify the quality of waste based on image data of waste in the garbage pit.

In addition to the technology described in Patent Document 1, research is also being conducted that focuses on the moisture content of waste and uses it to control incinerator combustion. For example, Patent Document 2 discloses technology related to an incinerator structure that has a moisture meter at the bottom end of the hopper's legs, measures the moisture content of the waste just before it is fed into the incinerator, and attempts to estimate the nature of the waste based on the measured moisture content.

JP 2019-207099 A JP 2020-085279 A

However, in the technology disclosed in Patent Document 1 mentioned above, the values indicating the characteristics of the waste identified based on the operating history of the waste treatment plant used as training data are values estimated based on time-delayed data obtained by burning the waste. Furthermore, the labels may differ depending on the worker who creates the training data. In other words, the technology described in Patent Document 1 has the problem that the characteristics of the waste before it is actually burned are not actually measured, making it difficult to accurately control the combustion.

In addition, the technology disclosed in Patent Document 2 measures the moisture content of the waste by installing a moisture meter at the bottom end of the legs of a hopper installed just before the incinerator, which creates the problem that if a sudden change occurs in the combustion, it becomes difficult to respond to the change in combustion.

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

The present invention has been made in consideration of these circumstances, and its purpose 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.

In order to solve the above-mentioned problems and achieve the object, an information processing device according to one aspect 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 image information acquired from an image capturing unit that captures images of 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 the moisture content of the waste stored in the storage pit corresponding to the address in the storage pit as an output parameter, and the moisture content determination model is a learning model that is generated in advance by at least one of machine learning and statistical methods, with the image 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.

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

In the information processing device according to one aspect 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.

In this configuration of an information processing device according to one aspect of the present invention, the control unit determines the moisture content for each address, and when the mixing method is moisture standard mode, selects an address to hold the waste and an address to dump the waste from among the multiple addresses in the storage pit based on the moisture content so as to mix together 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 the predetermined set value.

In this configuration of an information processing device according to one aspect of the present invention, 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 for holding the waste and an address for dumping 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.

An information processing method according to one aspect 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 acquired from an imaging unit that images the waste stored in the storage pit as input parameters in a storage unit, inputting the input parameters read from the storage unit into a moisture content determination model, and outputting the moisture content of the waste stored in the storage pit corresponding to the address in the storage pit as an output parameter, and the moisture content determination model is a learning model that is 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.

A program according to one aspect of the present invention causes a control unit of an information processing device that includes a control unit for managing waste stored in a storage pit configured to store waste and waste after removal from the storage pit and before incineration in an incinerator to generate a moisture content determination model using at least one of machine learning and statistical techniques, with image information of the waste stored in the storage pit as learning input parameters and the moisture percentage of the waste after removal and before incineration as learning output parameters, and store the model in a memory unit; store image information acquired from an image capture unit that captures the waste stored in the storage pit as input parameters in the memory unit; input the input parameters read from the memory unit to the moisture content determination model read into the memory unit; and output the moisture percentage of the waste stored in the storage pit corresponding to the address in the storage pit as an output parameter.

An incineration management system according to one aspect of the present invention includes 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.

The incineration management system according to one aspect of the present invention is the above invention, further comprising an incinerator capable of incinerating the waste after it has been removed.

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 store waste and equipped with an imaging unit capable of imaging the waste; 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 acquired from the imaging unit; and an incinerator capable of incinerating the waste after it has been removed, wherein the control unit inputs the imaging information read from the memory unit. The moisture content determination model is input as an input parameter to the moisture content determination model, and the moisture content of the waste stored in the storage pit corresponding to the address in 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. The moisture content determination model is a learning model that is generated in advance by at least one of machine learning and statistical methods, 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.

The information processing device, information processing method, program, incineration management system, and incineration management system operation method according to the present invention make it possible to properly obtain information on the moisture content of waste and stably burn the waste.

FIG. 1 is a block diagram showing an incineration management system according to one embodiment of the present invention. FIG. 2 is a diagram illustrating a waste treatment facility according to one embodiment of the present invention. FIG. 3 is a block diagram illustrating a management device according to an embodiment of the present invention. FIG. 4 is a plan view of a retention pit according to one embodiment of the present invention. FIG. 5 is a flow chart for explaining a method for operating a waste storage facility controlled by a management device according to an embodiment of the present invention.

One embodiment of the present invention will be described below with reference to the drawings. Note that in all the drawings of the embodiment below, the same or corresponding parts are given the same reference numerals. Furthermore, the present invention is not limited to the embodiment described below.

(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.

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. The waste treatment facility 3 includes at least a waste storage facility 20 and a 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 part of the waste treatment facility 3. The management device 10 may also be provided inside the waste storage facility 20 or the waste incineration facility 30, and the location of the management device 10 is not limited.

The network 2 is configured by appropriately combining wired and wireless communications, 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, and VPNs (Virtual Private Networks). The management device 10, waste storage facility 20, and waste incineration facility 30 are connected via the network 2.

(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 25, and a storage pit 26. The gripper 25 is movably provided in the storage pit 26, and the imaging unit 23 and the sensor unit 24 are also provided therein. The gripper 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 25.

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 memory 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 memory unit such as the RAM or ROM.

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, which functions as a transmitter and a receiver, is connected to the network 2 and communicates with the management device 10.

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, using 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.

The sensor unit 24 is configured to include at least one moisture meter 241. The moisture meter 241, which serves as a gripping moisture content measuring unit that measures the moisture content of the waste 40 gripped in the bucket 253, can employ various methods. Examples of methods of the moisture meter 241 include a transmission type microwave intensity method, a contact type microwave intensity method, a contact type electrostatic capacitance method, a transmission type electrostatic capacitance method, and an infrared intensity method.

The transmission type microwave intensity method is a method in which emitted microwaves are transmitted through the waste and the moisture content of the waste is measured based on the attenuation rate of the received microwaves. The transmission type microwave intensity method can ensure a wide range of waste 40 that can be measured, so it is suitable for measuring waste 40 with an uneven distribution of moisture content, such as the waste 40 in the chute section 336 described below.

The moisture meter 241, which 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 claw 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. In the transmission type microwave intensity moisture meter, the moisture percentage contained in the waste 40 can be measured based on the attenuation rate of the microwaves.

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 transmitted by the transmitter to the voltage received by the receiver, as the attenuation rate (or amount of attenuation) when the microwave passes 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.

The moisture meter 241 using the contact capacitance method is a so-called contact capacitance moisture meter, and includes a capacitance meter using the contact capacitance method and a moisture percentage calculator connected to the capacitance meter. The capacitance meter measures the capacitance of the waste 40 that is in contact with the claws of the bucket 253. The moisture percentage calculator can derive the moisture percentage corresponding to the measured 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 measured 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.

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, such as 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, and in this case the moisture meter 241 is provided separately from the bucket 253.

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 by associating it 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.

The storage pit 26 is a pit that can temporarily store waste 40. The waste 40 in the storage pit 26 is grasped by the gripping part 25 and supplied to the incinerator 33 of the waste incineration facility 30 for incineration.

The gripping unit 25 grips and moves the waste 40 stored in the storage pit 26. The bucket 253, which serves as an opening/closing unit, is capable of gripping the waste 40. The crane 252, which serves 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.

Parameters related to the work operation of the crane 252 installed 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 communication unit 22. Parameters related to the work operation of the crane 252 in the two dimensions (x, y) include, but are not limited to, the current position, the movement path, the movement range, and the movement 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 communication unit 22. Parameters related to the work operation of the bucket 253 in the three dimensions (x, y, z) include, but are not limited to, the current position, the opening time, the closing time, the number of openings and closings, and the opening amount.

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. The 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 that they can travel along the 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 rise and fall freely 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 waste 40 is dropped into the input hopper 331. As a result, the gripping unit 25 is configured to grasp and lift the waste 40 from within the storage pit 26 and input it into the input hopper 331.

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. 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 relationship 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 relationship database.

(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 amount of combustion air, the amount of cooling air, the waste feeder feed speed, and the grate feed speed as the manipulated variables of the respective manipulated variables based on the setting of a predetermined manipulated variable reference value. The sensor unit 32 includes a moisture meter 321 as a moisture percentage measuring 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 burned, 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.

As shown in FIG. 2, the incinerator 33 according to this embodiment is, for example, a grate-type waste incinerator. Note that the incinerator 33 is not limited to a grate-type waste incinerator, and may be another type of waste incinerator, or a gasification melting furnace may also be used. The incinerator 33 includes a waste incinerator, a waste gasification melting furnace, and the like.

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 to 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 into the air supply system 333, the feed speed of the grate 332, and the discharge speed of the dust feeder 335 are variable.

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 configuration of the moisture meter 321 is the same as 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.

(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 control unit 21 and the communication unit 22 described above, respectively.

The storage unit 12 is composed of a storage medium selected from a volatile memory such as RAM, a non-volatile memory such as ROM, an erasable programmable ROM (EPROM), a hard disk drive (HDD, Hard Disk Drive), and a removable medium. Note that 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 attachable memory card.

The memory 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 memory, CD-ROMs, DVD-ROMs, and flexible disks for wide distribution. The control unit 11 loads the programs stored in the memory unit 12 into the working area of the main memory unit, executes them, and can realize functions that meet a specified purpose by controlling each component through the execution of the programs.

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 searchable database.

The moisture content determination model 121 is composed of a learning model in which the input parameter is the imaging information 124 acquired from the imaging unit 23 that images the waste 40 in the storage pit 26, and the output parameter is moisture information representing the moisture percentage ρ for each address (x, y) of the waste 40. 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.

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 section 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 the time-series imaging information 124 as the learning input parameter and the time-series moisture percentage information 125 as the learning output parameter as the training data, with the input/output data set as training data. Note that the machine learning can employ various types of machine learning, such as deep learning using a neural network. As a result, when the moisture content determination model 121 receives the imaging information 124 captured by the imaging cameras 231 and 232 as the input parameter, it can output the moisture percentage information 125 of the moisture percentage ρ for each address (x, y) of the input waste 40 as the output parameter.

The type information 122 includes information on the type of waste 40 in the storage pit 26. The type information 122 includes, for example, but is not limited to, 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. 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.

The deposition information 123 includes information regarding 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 of 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.

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 the 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 on the three-dimensional coordinates of the waste 40 in the storage pit 26.

Furthermore, the deposition information 123 may include information generated in association with the determined type information 122 of the waste 40. That is, the deposition information 123 may include three-dimensional map information generated by associating 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 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.

In addition, when the waste 40 in the storage pit 26 is managed as multiple units divided into, for example, a predetermined rectangular parallelepiped shape, specifically, a cube 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.

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 about the crushing area (crushing area), which is an area where garbage is crushed by the bucket 253 or the like. This makes it possible for the control unit 11 to separate the deposition information for each of the carry-in area, transfer area, throw-in area, and crushing area.

In this embodiment, the functions of the moisture amount derivation unit 111, type determination unit 112, pile information generation unit 113, crane control unit 114, combustion acquisition unit 115, and learning unit 116 are executed by executing the various programs loaded by the control unit 11. The various programs also include programs that realize artificial intelligence and learned models that can realize the processing according to this embodiment.

The moisture content derivation unit 111, which serves as a moisture content 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 carrying-in area, a transfer area, and a throw-in area based on the address (x, y). The carrying-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 carrying-in area by a crane 252 provided in the storage pit 26. The throw-in area is an input area where the waste 40 to be thrown into the incinerator 33 is stored.

The moisture content derivation unit 111 can calculate or determine the moisture content ρ of the waste 40 in the chute section 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 memory unit 12. The moisture content derivation unit 111 inputs the imaging information 124 of the waste 40 in the storage pit 26, which is acquired continuously or intermittently along a time series from the imaging unit 23, 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 in the waste 40 in the chute section 336, as an output parameter from the moisture content determination model 121.

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.

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 area (section) partitioned at a predetermined interval along the x direction and a predetermined interval along 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.

The type determination unit 112 determines the type (also called classification) of the waste 40 brought into the receiving 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 provided by 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.

The deposition information generating unit 113 generates deposition information for 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 in 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 that maps the surface shape of the waste 40. The deposition information 123 includes three-dimensional mapping information of the surface shape of the waste 40.

Specifically, when the sensor unit 24 includes, for example, LiDAR, the electromagnetic waves used are typically near-infrared or infrared. This makes it difficult for cameras to capture images in dark places, but LiDAR makes it possible to measure distances even in dark places. In addition, the dimensions of objects that can be detected by LiDAR are greater than or equal to the wavelength. The electromagnetic waves emitted from the LiDAR have a high light flux density, high coherence, and an extremely short wavelength, so they are easily reflected even when irradiated on a small object. Therefore, the LiDAR emits electromagnetic waves toward the waste 40, making it 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 makes it easy for the LiDAR to accurately and easily measure the distance to the surface or surface layer of the waste 40 stored in the storage pit 26. In addition, by measuring distance using LiDAR or the like, the time response can be made on the order of milliseconds, so the measurement interval can be shortened and the distance from the LiDAR to the waste 40 can be measured almost continuously.

Also, 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 be 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 from at least one of the multiple LiDARs to the waste 40 can be measured, so the possibility of measuring the moisture content ρ at a predetermined position is improved compared to when 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 instantly with a higher probability by the sensor unit 24.

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 units 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 l ₁ from the LiDAR near the imaging camera 231 to the predetermined position P(x, y) and the angle θ ₁ with the horizontal. Similarly, the waste level z at the predetermined position P(x, y) can be specified by the distance l ₂ from the LiDAR near the imaging camera 232 to the predetermined position P(x, y) and the angle θ ₂ 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.

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

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

The combustion acquisition unit 115, which serves 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 include, for example, measurements from various thermometers, various concentration meters, and exhaust gas flow meters as combustion state quantities inside the incinerator 33 during operation, and combustion process measurements such as the amount of waste 40 cut out. The combustion state is not limited to these state quantities, and other state quantities within 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.

The learning unit 116, which serves 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 is stored in the memory unit 12.

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 to divide 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.

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

At least one, and preferably multiple, imaging cameras 231, 232 serving as the imaging unit 23 are disposed near, for example, the upper waist wall of the storage pit 26. When multiple cameras are installed, in order to prevent blind spots from occurring, it is preferable to install them in a position where there is a low possibility of blind spots occurring, for example, on the side of the waste 40 loading area 261. In FIG. 4, two imaging cameras 231, 232 are provided. The imaging cameras 231, 232 are configured to have a 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.

The imaging cameras 231, 232 may be installed, for example, at the top of the inner wall of the storage pit 26, on a wall that can capture images of the waste 40 in the storage pit 26. There may be only one imaging camera 231, 232, 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.

The input/output unit 14 can be composed of, for example, a touch panel display or a speaker microphone. 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 it to the control unit 11. The transmission of information from the imaging unit 23 or the sensor unit 24 to the input/output unit 14 may be performed 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 able to input 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 the display monitor, displays characters and figures on the screen of the touch panel display, and outputs sound from the speaker according to the control by the control unit 11. That is, the input/output unit 14 is configured to be able to notify the predetermined information to the outside. The input and output sections of the input/output unit 14 may be configured separately.

(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.

As shown in FIG. 5, first, in step ST1, the moisture content 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 content 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.

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).

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 also be measured intermittently. The moisture amount derivation section 111 acquires the measurement value of the moisture percentage ρ measured by the moisture meter 321.

Next, the process proceeds to step ST4, where 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.

Next, the process proceeds to step ST5, where 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 memory unit 12. Note that the processes of steps ST1 to ST5 described above can be performed in parallel as appropriate, and the order of execution is not limited to the order described above.

Moving 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 content ρ in the storage pit 26 is approximately constant and approximately uniform for each address (x, y). The combustion-based mode (2) is a mode in which the waste 40 is mixed or fed so as to adjust the moisture content ρ based on the combustion information 126, which includes information on the combustion state in the incinerator 33.

In step ST6, if the crane control unit 114 selects the moisture-based mode based on the combustion information 126 (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 content of the waste 40 in the storage pit 26 is approximately 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 where the moisture content ρ is high with the waste 40 at the other address (x', y') where the moisture content ρ is low. This makes it possible to make the moisture content ρ of the waste 40 in the storage pit 26 approximately uniform over a predetermined range, for example, over the entire area. The moisture content ρ can be determined by setting a predetermined value for the moisture content ρ in advance and determining whether it is equal to or higher than the set value or lower than the predetermined value.

Then, 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.

On the other hand, if the crane control unit 114 selects the combustion reference 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.

That is, the crane control unit 114 controls the crane 252 in the gripping unit 25 based on the combustion information 126 to adjust the moisture content ρ so that the incinerator 33 is in the desired combustion state. In this case, the moisture content ρ is adjusted for the waste 40 that is transported from the storage pit 26 and fed into at least the feed hopper 331. As a result, specifically, for example, when the combustion information 126 obtained 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 control the feeding of waste 40 with a high moisture content ρ. Also, when the combustion information 126 obtained 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 control the feeding of waste 40 with a low moisture content ρ.

Then, the process proceeds to step ST8. 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.

The processing of steps ST1 to ST9 is repeatedly performed while the waste storage facility 20 is in operation. In this manner, the management device 10 operates the waste storage facility 20.

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 a learning input parameter, 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 learning output parameters, the moisture content determination model 121 is 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 allows the waste 40 to be stirred more appropriately in the storage pit 26 by stirring the waste 40 based on the moisture content ρ determined from the imaging information 124 and the moisture content information 125, including the calories burned. In other words, the moisture content ρ of the waste 40 in the storage pit 26 can be made substantially uniform as needed, and in some cases, the waste 40 can be stirred 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.

Although one embodiment of the present invention has been specifically described above, the present invention is not limited to the above-described embodiment, and various modifications based on the technical concept of the present invention are possible. For example, the numerical values given in the above-described 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 part of the disclosure of the present invention according to this embodiment.

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

In the embodiment described above, two imaging cameras 231, 232 are provided at the two upper corners of the storage pit 26, but four imaging cameras may be provided at the four upper corners 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, thereby reducing blind spots.

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, it is also possible to add the power generation-based mode (3) to be able to deal with cases where the amount of power generation changes depending on whether it is day or night. 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 waste 40 with a moisture content ρ according to the derived combustion calories is supplied to the incinerator 33.

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 also be used. For example, other supervised learning such as support vector machines, decision trees, naive Bayes, and k-nearest neighbors may also be used. Also, semi-supervised learning may be used instead of supervised learning.

In addition, in one embodiment, the "unit" described above can be read as a "circuit" or the like. For example, a control unit can be read as a control circuit.

Note that in the explanation of the flowcharts in this specification, the order of processing between steps is clearly indicated using expressions such as "first," "then," and "continue." However, 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 are no contradictions.

Further advantages and modifications may be readily derived by those skilled in the art. The broader aspects of the present disclosure are 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.

1 Incineration management system 2 Network 3 Waste treatment facility 10 Management device 11, 21 Control unit 12 Memory unit 13, 22 Communication unit 14 Input/output unit 20 Waste storage facility 23 Imaging unit 24, 32 Sensor unit 25 Grip unit 26 Storage pit 30 Waste incineration facility 31 Combustion control device 33 Incinerator 40 Waste 111 Moisture amount derivation unit 112 Type determination unit 113 Accumulation information generation unit 114 Crane control unit 115 Combustion acquisition unit 116 Learning unit 121 Moisture amount determination model 122 Type information 123 Accumulation information 124 Imaging information 125 Moisture percentage information 126 Combustion information 231, 232 Imaging camera 241, 321 Moisture meter 251 Load meter 252 Crane 253 Bucket 254 Wire rope 255 Crab 256 Crane girder 261 Carry-in area 331 Feeding hopper 332, fire grate 333, air supply system 334, combustion chamber 335, dust supply device 336, chute section

Claims (10)

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 has been generated in advance by at least one of machine learning and statistical methods, using imaging 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 it has been removed and before it is incinerated and synchronized with the imaging information as an output parameter for learning. The control unit is
The information processing apparatus according to claim 1 , further comprising: a moisture content determining unit that determines the moisture content for each address; and agitating the waste in the storage pit is controlled based on the moisture content. The control unit is
Acquire combustion information indicating a combustion state of waste in the incinerator;
The information processing apparatus according to claim 1 or 2, further comprising control for changing a stirring method for stirring the waste in the storage pit based on the combustion information and the derived moisture percentage of the waste in the storage pit. The control unit is
determining the moisture content for each address;
The information processing device according to claim 3, wherein when the mixing method is a moisture standard 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 as to mix together waste at addresses where the moisture content is equal to or greater than a predetermined set value and waste at addresses where the moisture content is less than a predetermined set value. The control unit is
determining the moisture content for each address;
The information processing device of claim 3, wherein when the mixing method is the combustion standard mode, a supply moisture percentage to be supplied to the incinerator is selected based on the combustion information, and an address for holding the waste and an address for discharging the waste are selected from a plurality of addresses in the storage pit based on the moisture percentage so that the moisture percentage approximately matches the supply moisture percentage, and the waste is mixed. 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 has been generated in advance by at least one of machine learning and statistical methods, using imaging 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 it has been removed and before it is incinerated and synchronized with the imaging information as an output parameter for learning. 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;
A program for inputting the input parameters read from the memory unit into the moisture content determination model read into the memory unit, and outputting as an output parameter the moisture content of the waste stored in the storage pit corresponding to the address within the storage pit. 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;
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. The incineration management system according to claim 8, further comprising an incinerator capable of incinerating the waste after the waste is removed. 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 methods, using 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.An operating method for an incineration management system.

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