Stoker type processing device, processing method of object to be processed, and processing program

Abstract

This stoker type processing device comprises: a stoker that supports an object to be processed and that conveys the object to be processed in a conveyance direction intersecting the vertical direction; an imaging device that captures an image of the inside of a field of view including a plurality of sections on the stoker; a region evaluation unit that, on the basis of a two-dimensional image of inside of the field of view obtained by imaging by the imaging device, identifies a target region in which the object to be processed is present, in the image of the inside of the field of view; a distance distribution evaluation unit that acquires distance distribution information indicating the distance from the imaging device to an object in the field of view; and a deposition state evaluation unit that acquires a relative relationship between the deposition states of the object to be processed in the plurality of sections, on the basis of deposition state information obtained by combining the distance distribution information and the result of the identification of the target region.

Description

Stoker-type treatment device, treatment method for material to be treated, and treatment program

[0001] The present disclosure relates to a stoker-type processing device, a processing method for an object to be processed, and a processing program.

[0002] Patent document 1 discloses a stoker-type incinerator that includes a thermal image capturing unit that captures thermal images of garbage in a target area, which is at least a portion of the area on the grate section, and a calculation unit that acquires garbage height information that indicates the distribution of garbage heights on the target area based on the thermal image.

[0003] Japanese Patent Application Laid-Open No. 2017-187228

[0004] The present disclosure provides a stoker-type processing device, a processing method for a processing object, and a processing program that are useful for simplifying the process of acquiring the accumulation status of a processing object on a stoker.

[0005] [1] A stoker-type processing device comprising: a stoker that supports an object to be processed and transports the object to be processed in a transport direction that intersects the vertical direction; an imaging device that captures an image within a field of view that includes multiple compartments on the stoker; an area evaluation unit that identifies a target area within the field of view in which the object to be processed is located based on a two-dimensional image within the field of view obtained by imaging with the imaging device; a distance distribution evaluation unit that acquires distance distribution information that indicates the distance from the imaging device to an object within the field of view; and a deposition situation evaluation unit that acquires the relative relationship of the deposition situations of the object to be processed in the multiple compartments based on deposition situation information obtained by combining the distance distribution information and the result of identifying the target area.

[0006] [2] The stoker-type treatment device described in [1] above, wherein the plurality of compartments are aligned in the conveying direction.

[0007] [3] The stalker-type processing device described in [1] or [2] above, wherein the imaging device forms an image of mid-infrared light incident from the field of view to generate an image within the field of view.

[0008] [4] A stalker-type processing device described in any one of [1] to [3] above, wherein the distance distribution evaluation unit acquires the distance distribution information by inputting the image within the field of view into a distance estimation model constructed by machine learning so as to output information representing the distribution of distances to objects in the two-dimensional image in response to the input of the two-dimensional image.

[0009] [5] A stoker-type treatment device described in any one of [1] to [4] above, wherein the deposition status evaluation unit acquires the relative relationship by inputting the deposition status information into a judgment model constructed by machine learning so as to output information indicating the relative relationship of the deposition status of the treatment object in the multiple sections in response to input information corresponding to the deposition status information.

[0010] [6] A stalker-type treatment device described in any one of [1] to [4] above, wherein the distance distribution information includes information indicating the distance for each pixel in the image within the field of view, and the deposition status evaluation unit calculates, for each pixel in the deposition status information, three-dimensional coordinates consisting of the two-dimensional coordinates of the pixel and the distance, and obtains the relative relationship based on area information indicating areas corresponding to each of the multiple sections on a three-dimensional graph with the two-dimensional coordinates and the distance as variables, and the calculation results of the three-dimensional coordinates for each pixel in the deposition status information.

[0011] [7] A stalker-type processing device described in any one of [1] to [6] above, wherein the area evaluation unit identifies the target area by inputting the image within the field of view into an area identification model constructed by semantic segmentation so as to output information indicating the results of classifying each pixel in the two-dimensional image into an area where the processing object exists and an area where the processing object does not exist, in response to the input of a two-dimensional image.

[0012] [8] A stalker-type processing device described in any one of [1] to [6] above, wherein the area evaluation unit identifies the target area by performing a process of comparing the standard deviation of pixel values ​​in a divided area of ​​the image within the field of view with a threshold value while sliding the coordinates of a representative point of the divided area.

[0013] [9] A stalker-type processing device described in any one of [1] to [6] above, wherein the area evaluation unit identifies the target area by performing a process of detecting edges in the image within the field of view.

[0014]

[10] The area evaluation unit acquires a plurality of field-of-view images including the field-of-view image, the plurality of field-of-view images being obtained by continuing to capture images of the imaging device for a predetermined period of time, and identifies the target area by performing a process of comparing, for each pixel in the plurality of field-of-view images, the standard deviation of the change in pixel value over time with a threshold value. This is a stalker-type processing device described in any one of [1] to [6] above.

[0015]

[11] A stoker-type treatment device described in any one of [1] to

[10] above, further comprising a control unit that adjusts the treatment of the treatment object on the stoker so as to reduce the difference in the deposition status of the treatment object between the multiple compartments based on the relative relationship acquired by the deposition status evaluation unit.

[0016]

[12] A method for processing an object to be processed, comprising: a transporting step of transporting the object to be processed in a transport direction intersecting the vertical direction by a stoker supporting the object; an imaging step of capturing an image of a field of view including a plurality of compartments on the stoker using an imaging device; an area evaluation step of identifying a target area within the field of view image in which the object to be processed exists based on a two-dimensional image within the field of view obtained by imaging with the imaging device; a distance distribution evaluation step of acquiring distance distribution information indicating the distance from the imaging device to an object within the field of view; and a deposition situation evaluation step of acquiring the relative relationship of the deposition situations of the object to be processed in the plurality of compartments based on deposition situation information obtained by combining the distance distribution information and the result of identifying the target area.

[0017]

[13] A processing program for causing a computer to execute the following steps: an image acquisition process for acquiring a two-dimensional image within a field of view obtained by using an imaging device to capture an image within a field of view that includes multiple compartments on a stoker that supports an object to be processed and transports the object to be processed in a transport direction that intersects the vertical direction; an area evaluation process for identifying a target area within the field of view image in which the object to be processed exists based on the image within the field of view; a distance distribution evaluation process for acquiring distance distribution information that indicates the distance from the imaging device to an object within the field of view; and a deposition situation evaluation process for acquiring the relative relationship of the deposition situation of the object to be processed in the multiple compartments based on deposition situation information obtained by combining the distance distribution information and the result of identifying the target area.

[0018] According to the present disclosure, a stoker-type treatment device, a treatment method for a treatment object, and a treatment program are provided that are useful for simplifying the process of acquiring the accumulation status of a treatment object on a stoker.

[0019] FIG. 1 is a schematic diagram showing an example of a stalker-type processing device. FIG. 2 is a schematic plan view showing an example of a stalker. FIG. 3 is a schematic diagram showing an example of an image obtained by an imaging device. FIG. 4 is a block diagram showing an example of the functional configuration of a control device. FIG. 5 is a schematic diagram showing an example of a process for acquiring a distance distribution. FIG. 6 is a schematic diagram showing an example of a process for specifying a target area. FIG. 7 is a diagram showing an example of accumulation status information. FIG. 8 is a schematic diagram showing an example of a process for determining a relative relationship between accumulation statuses. FIG. 9 is a schematic diagram showing an example of learning data used for constructing a determination model. FIG. 10 is a block diagram showing an example of the hardware configuration of a control device. FIG. 11 is a flowchart showing an example of a series of control processes executed by the control device. FIG. 12 is a diagram showing an example of a process for specifying a target area. FIG. 13 is a diagram showing an example of a process for specifying a target area. FIG. 14(a) is a schematic diagram illustrating the relationship between pixels in an image within the field of view and three-dimensional coordinates. FIG. 14(b) is a diagram showing three-dimensional plot data obtained from the accumulation status information. FIG. 15 is a schematic diagram showing an example of information indicating multiple sections on a three-dimensional graph.

[0020] An embodiment will be described below with reference to the drawings. In the description, the same elements or elements having the same functions are designated by the same reference numerals, and redundant description will be omitted.

[0021] Fig. 1 schematically shows a stoker-type treatment apparatus according to one embodiment. The stoker-type treatment apparatus 1 shown in Fig. 1 is an apparatus that heat-treats a treatment object while transporting it in a transport direction that intersects the vertical direction. In Fig. 1, the transport direction is indicated by "D1", and the transport direction D1 is, for example, a horizontal direction. The treatment object that is the target of treatment by the stoker-type treatment apparatus 1 is, for example, waste such as industrial waste or general waste.

[0022] When the material to be treated is waste, the stoker-type treatment device 1 combusts (thermally treats) the waste supplied by the dust feeder 14 and sends it to a bottom ash chute. The stoker-type treatment device 1 is also called a stoker-type incinerator. The material to be treated is not limited to waste. The material to be treated may also be biomass. The stoker-type treatment device 1 includes a stoker furnace 10, an imaging device 90, and a control device 100.

[0023] The stoker furnace 10 accommodates materials to be treated and transports them in a transport direction D1 that intersects (e.g., is perpendicular to) the vertical direction. The stoker furnace 10 includes, for example, a furnace body 11, a stoker 30, a drive device 33, and a blower 40. The furnace body 11 accommodates the materials to be treated. The furnace body 11 forms a storage space extending along the transport direction D1. The furnace body 11 has a receiving section 12 and a sending section 13 at both ends of the furnace body 11. The receiving section 12 receives the materials to be treated into the space within the furnace body 11. The sending section 13 sends the materials to be treated out of the space within the furnace body 11.

[0024] When the material to be treated is waste, the receiving section 12 receives the waste supplied by the dust feeder 14. The sending section 13 sends the waste to a main ash chute provided below the furnace body 11.

[0025] The stoker 30 is provided at the bottom of the furnace body 11, supports the material to be treated, and transports the material in the transport direction D1. The stoker 30 has, for example, a plurality of fixed grates 31 and a plurality of movable grates 32. The plurality of fixed grates 31 are lined up along the transport direction D1. The plurality of movable grates 32 are provided to correspond to the plurality of fixed grates 31, respectively. Each of the plurality of movable grates 32 is provided, for example, on top of the corresponding fixed grate 31.

[0026] The drive device 33 reciprocates each of the plurality of movable grates 32 along the conveying direction D1. The drive device 33 reciprocates each of the plurality of movable grates 32 along the conveying direction D1 by, for example, an electric motor or a hydraulic cylinder. The drive device 33 is configured to be able to individually change the grate speed of the plurality of movable grates 32.

[0027] The grate speed is the transport speed of the material to be treated by the reciprocating movement of the movable grate 32. To change the grate speed, the drive device 33 may change the displacement speed of the movable grate 32 in one reciprocating movement, may change the displacement stroke of the movable grate 32 in one reciprocating movement, or may change the number of reciprocating movements of the movable grate 32 per unit time. To change the grate speed, the drive device 33 may change two or more of the displacement speed, the displacement stroke, and the number of reciprocating movements.

[0028] The heights of the multiple fixed grates 31 may decrease in a stepped manner toward the conveying direction D1 (downstream in the conveying direction D1). In this case, each time the object to be treated moves from one fixed grate 31 to the next fixed grate 31, it descends by the amount of the step between the fixed grates 31. In this way, conveying the object to be treated in the conveying direction D1 includes displacing the object to be treated in the conveying direction D1 while also displacing the object to be treated in a direction different from the conveying direction (for example, downward).

[0029] In this disclosure, the terms "upstream" and "downstream" are used based on the direction in which the material to be treated is transported. That is, in the stoker-type treatment device 1, the material to be treated is transported from upstream to downstream. Furthermore, the direction from upstream to downstream is referred to as "forward," and the direction from downstream to upstream is referred to as "rearward." The terms "right" and "left" are used based on the view from upstream to downstream (viewing forward).

[0030] Each of the multiple movable grates 32 may be divided into multiple zones 34 aligned in the width direction D2 of the furnace body 11 (see FIG. 2). The width direction D2 is a direction perpendicular to both the vertical direction and the conveying direction D1. Each of the multiple movable grates 32 is divided into two zones 34, for example, a zone 34 located on the right side and a zone 34 located on the left side. The drive device 33 may be configured to be able to change the grate speed of each of the multiple zones 34 individually.

[0031] The blower 40 sends gas for heat treatment (e.g., combustion) from below the stoker 30 through the multiple fixed grates 31 and the movable grate 32 to the material to be treated. The gas for heat treatment is, for example, an oxygen-containing gas such as air. The gas for heat treatment may be at room temperature or may be preheated. The blower 40 may be configured to be able to individually change the amount of air blown to multiple blowing areas 43 on the stoker 30. The multiple blowing areas 43 may correspond to the multiple fixed grates 31, respectively.

[0032] The air blowing unit 40 includes, for example, an air blowing source 41 and a plurality of valves 42. The air blowing source 41 pressure-feeds gas for heat treatment to a plurality of air blowing areas 43 using, for example, a blower. The plurality of valves 42 adjust the flow rate of gas from the air blowing source 41 to each of the plurality of air blowing areas 43. Each of the plurality of air blowing areas 43 may be divided into a plurality of air blowing zones 44 corresponding to the plurality of zones 34, respectively (see FIG. 2 ), and the air blowing unit 40 may be configured to be able to individually change the amount of air blown to each of the plurality of air blowing zones 44.

[0033] The imaging device 90 captures an image within a predetermined field of view FV on the stoker 30. The imaging device 90 is directed toward the predetermined field of view FV on the stoker 30 from the front in the conveying direction D1. For example, the imaging device 90 is located forward of the fixed grate 31 located most downstream in the conveying direction D1 and directed toward the rear. The imaging device 90 may be arranged such that the field of view (direction toward the center of the field of view FV) is directed from diagonally above toward the top surface of the stoker 30. For example, the imaging device 90 is arranged at a position higher than the stoker 30 and directed diagonally downward. The imaging device 90 may be arranged so that each of the multiple fixed grates 31 is at least partially within the field of view FV.

[0034] The imaging device 90 has a predetermined viewpoint VP and generates a two-dimensional image (image data) as information for acquiring the distance distribution from the viewpoint VP to objects (substances) within the field of view FV. Hereinafter, the two-dimensional image generated by the imaging device 90 will be referred to as the "field of view image VI." The imaging device 90 generates the field of view image VI based on light from the field of view FV. The imaging device 90 may be configured to generate the field of view image VI by, for example, focusing mid-infrared light incident from the field of view FV. Mid-infrared light is electromagnetic waves with a wavelength of approximately 2.5 μm to approximately 4 μm. Mid-infrared light can acquire information that is less affected by flames, water vapor, carbon dioxide, and the like generated by the combustion of the object to be processed.

[0035] 3 schematically shows an example of a field-of-view image VI acquired by imaging by the imaging device 90. As shown in FIG. 3, the field of view FV of the imaging device 90 includes multiple sections SC corresponding to the multiple deposition areas on the stoker 30. The multiple deposition areas are areas where the material to be processed is deposited. For example, the multiple deposition areas are areas obtained by dividing the upper surface of the stoker 30. In one example, the multiple deposition areas correspond to the multiple fixed grates 31 described above.

[0036] One of the multiple sections SC corresponds to one of the multiple fixed grates 31. One section SC may be divided into two zones 34 (two ventilation zones 44) arranged side by side. In the example shown in FIG. 3 , the multiple sections SC are arranged in the conveying direction D1. Each of the multiple sections SC can be considered to be an area divided in the conveying direction D1 based on the fixed grate 31. For example, "section 1," "section 2," "section 3," "section 4," and "section 5" are arranged in order from upstream. Each of the multiple sections SC can also be referred to as a combustion zone.

[0037] The control device 100 controls the treatment of the treatment object on the stoker 30. Controlling the treatment of the treatment object on the stoker 30 includes at least controlling the drive device 33 or the blower 40. In order to control the treatment of the treatment object, the control device 100 may control either the drive device 33 or the blower 40, or may control both the drive device 33 and the blower 40.

[0038] In order to improve the efficiency of treatment of the material to be treated (e.g., thermal treatment including incineration) in the stoker furnace 10, it is necessary to appropriately distribute the degree of accumulation of the material to be treated on the stoker 30. For example, if the amount of material to be treated accumulated at one location on the stoker 30 is greater than at other locations, there is a possibility that treatment at that location will not proceed sufficiently. Therefore, the control device 100 evaluates the relative relationship between the accumulation status of the material to be treated in multiple compartments on the stoker 30, and controls the drive device 33 and the like to adjust the accumulation status of the material to be treated based on the evaluation results. The configuration of the control device 100 will be described in detail below.

[0039] Fig. 4 illustrates an example of the functional configuration of the control device 100. As shown in Fig. 4, the control device 100 has, for example, functional components (hereinafter referred to as "functional blocks"), a distance distribution evaluation unit 112, a distance distribution storage unit 116, an area evaluation unit 118, a deposition status evaluation unit 120, and a control unit 122. The processing performed by these functional blocks corresponds to the processing performed by the control device 100. Hereinafter, the processing performed by the control device 100 will be referred to as "control processing" to distinguish it from the physical processing of the processing target on the stoker 30.

[0040] The distance distribution evaluation unit 112 acquires distance distribution information DI indicating distances from the image capture device 90 to objects within the field of view FV. The distance distribution evaluation unit 112 stores the acquired distance distribution information DI in the distance distribution storage unit 116. The distance distribution evaluation unit 112 acquires the distance distribution information DI from the in-field image VI using, for example, a distance estimation model M1.

[0041] 5 schematically shows a process of obtaining distance distribution information DI from a field-of-view image VI using a distance estimation model M1. The distance estimation model M1 is a trained model constructed by machine learning so as to output information representing the distribution of distances to objects in a two-dimensional image in response to an input of the two-dimensional image. The distance distribution evaluation unit 112 may obtain, as the distance distribution information DI, information output from the distance estimation model M1 when the field-of-view image VI is input to the distance estimation model M1.

[0042] In one example, the distance distribution information DI is image information having a pixel value for each pixel in the field-of-view image VI, and includes information (pixel value) indicating the distance for each pixel in the field-of-view image VI. In the distance distribution information DI, the magnitude of the pixel value represents the magnitude of the distance. For example, the distance distribution information DI is grayscale information, where a darker color (closer to black) represents a shorter distance, and a lighter color (closer to white) represents a longer distance.

[0043] The distance estimation model M1 is constructed in advance by machine learning based on a plurality of training data sets so as to output, in response to an input of a two-dimensional image, information representing the distribution of distances to objects in the two-dimensional image. Each of the plurality of training data sets includes, for example, data relating to the two-dimensional image and actual measurement data of a distance distribution indicating the distance to an object for each pixel in the two-dimensional image. The distance estimation model M1 may be constructed based on a plurality of training data sets acquired in a space different from the stoker 30 (a space outside the stoker furnace 10). Specific examples of learning algorithms used to construct the distance estimation model M1 include a Vision Transformer (ViT) or a neural network.

[0044] The area evaluation unit 118 identifies an area in the field of view image VI where the processing target exists (hereinafter referred to as the "target area Z1") based on the two-dimensional field of view image VI obtained by imaging by the imaging device 90. The area evaluation unit 118 identifies the target area Z1 in the field of view image VI, for example, by using an area identification model M2.

[0045] 6 schematically shows the process of identifying a target region Z1 in a field-of-view image VI using a region identification model M2. The region identification model M2 is a trained model constructed by semantic segmentation so as to output information indicating the results of classifying each pixel in a two-dimensional image into a region where a processing target object exists and a region where a processing target object does not exist. Semantic segmentation is a type of machine learning algorithm.

[0046] The region identification model M2 outputs a classification result for each pixel in the field-of-view image VI, determining whether the pixel is a region where a processing target object exists or a region where a processing target object does not exist. As a result, in the data output from the region identification model M2, the region (entire region) on the image corresponding to the field-of-view image VI is divided into a target region Z1 and a region other than the target region Z1 (hereinafter referred to as a "non-target region Z2"). In one example, the region evaluation unit 118 acquires a data set indicating the coordinates of all pixels divided into the target region Z1 as information indicating the identification result of the target region Z1.

[0047] The region identification model M2 is constructed in advance by machine learning using semantic segmentation based on multiple training data sets so as to output information indicating the results of classifying each pixel in the two-dimensional image into a region where a processing object exists and a region where a processing object does not exist, in response to an input two-dimensional image. The multiple training data sets include, for example, data related to the two-dimensional image and correct answer data indicating whether or not a processing object exists for each pixel in the two-dimensional image. The correct answer data regarding whether or not a processing object exists may be created by converting the results of a judgment made by a person, such as an operator of the stoker-type processing apparatus 1, into data. The region identification model M2 may be constructed based on multiple training data sets acquired in a space other than the stoker 30 (a space outside the stoker furnace 10).

[0048] The accumulation status evaluation unit 120 acquires the relative relationship of the accumulation status of the processing object in the multiple sections SC based on accumulation status information DI1 obtained by combining the distance distribution information DI and the result of identifying the target area Z1. The accumulation status evaluation unit 120 first generates accumulation status information DI1 by combining the distance distribution information DI stored in the distance distribution storage unit 116 with information indicating the result of identifying the target area Z1 (based on this information).

[0049] 7 schematically illustrates a process for generating deposition status information DI1 by combining the distance distribution information DI and the result of identifying the target area Z1. The deposition status information DI1 is image information obtained by extracting the target area Z1 from the distance distribution information DI, which is image information, or image information obtained by masking the target area Z1 with a non-target area Z2, which is an area other than the target area Z1. In the deposition status information DI1, for example, the coordinates of all pixels in a portion of the distance distribution information DI that is divided into the target area Z1 are associated with pixel values ​​indicating the magnitude of the distance (depth). The deposition status information DI1 does not necessarily need to include information (coordinates and pixel values) regarding the portion of the distance distribution information DI that is divided into the non-target area Z2.

[0050] The accumulation status information DI1 indicates the position (coordinate) at which the processing object is present in the field of view FV (field of view image VI) and, for each coordinate at which the processing object is present, the distance at that coordinate. In this way, the accumulation status information DI1 is information that indicates the accumulation status of the processing object on the stoker 30. Therefore, the accumulation status information DI1 includes information that indicates the relative relationship between the accumulation status of the processing object in multiple sections.

[0051] The information indicating the relative relationship of the accumulation status is, for example, information indicating which compartment has the most accumulated material to be processed. The information indicating the relative relationship of the accumulation status may include information identifying one or more compartments with the most accumulated material to be processed and a numerical value indicating how much material to be processed has accumulated in those one or more compartments compared to other compartments. Alternatively, the information indicating the relative relationship of the accumulation status may be the relative amount of material to be processed in each compartment. For example, when compartments 1 to 5 are set, the relative amount of material to be processed in each compartment can be expressed as (compartment 1, compartment 2, compartment 3, compartment 4, compartment 5) = (10%, 10%, 10%, 60%, 10%).

[0052] After generating the accumulation status information DI1, the accumulation status evaluation unit 120 acquires the relative relationship of the accumulation status of the processing objects in the multiple sections SC based on the accumulation status information DI1. The accumulation status evaluation unit 120 acquires the relative relationship of the accumulation status of the processing objects in the multiple sections SC, for example, by using a determination model M3.

[0053] 8 schematically shows a process of acquiring the relative relationship of the deposition status of the processing object in the multiple sections SC using a determination model M3. The determination model M3 is a trained model constructed by machine learning so as to output information indicating the relative relationship of the deposition status of the processing object in the multiple sections SC in response to input of input information corresponding to the deposition status information DI1. The input information corresponding to the deposition status information DI1 means information of the same type as the deposition status information DI1.

[0054] The input data to the determination model M3 is deposition status information DI1 obtained from a combination of the distance distribution information DI and the result of identifying the target area Z1. The output data from the determination model M3 (indicated by "OD" in Figure 8) is information indicating the relative relationship between the deposition status of the processing object in multiple sections SC. The output from the determination model M3 need only be information indicating the relative relationship between the deposition status of the processing object in multiple sections SC, and does not have to be image information. In Figure 8 and other figures, the relative relationship between the deposition status is displayed as a diagram to clearly show the calculation content of the determination model M3. In some figures such as Figure 8, sections 1 to 5 are denoted as "SC1" to "SC5," and "right" and "left" are denoted as "R" and "L," respectively.

[0055] The determination model M3 is constructed in advance by machine learning based on multiple learning data sets so as to output the relative relationships of the processing objects in multiple sections SC in response to input of deposition status information DI1. FIG. 9 illustrates multiple learning data sets used in constructing the determination model M3. Each of the multiple learning data sets includes, for example, deposition status information DI1 and ground truth data TD (performance data) indicating the relative relationships of the deposition statuses of the processing objects when the deposition status information DI1 is obtained. Specific examples of learning algorithms used to construct the determination model M3 include ViT or neural networks.

[0056] When preparing a plurality of learning data sets, the correct answer data TD regarding the relative relationship of the deposition status of the processing target may be created by converting the results of judgment made by a person such as an operator of the stoker-type processing device 1 into data. The operator preparing the correct answer data TD may determine the correct answer data TD while viewing the deposition status information DI1 displayed as an image and the field-of-view image VI that is the source of the deposition status information DI1.

[0057] In an image acquired by the imaging device 90 (camera), the size of an object on the image changes depending on the distance (depth) to the object. That is, even if an object is actually the same size, if the distance from the camera is large, the size on the image will be small, and if the distance from the camera is small, the size on the image will be large. As a result, if the degree of accumulation of processing objects for each section is simply evaluated on the image, there is a concern that processing objects in the foreground will be overestimated and processing objects in the background will be underestimated. Therefore, an operator or the like who prepares the correct answer data TD may determine the correct answer data TD taking into account the depth on the image (distance from the imaging device 90).

[0058] As shown in FIG. 4 , the control device 100 may have, as functional blocks, a model construction unit 124 and a model storage unit 126. The model construction unit 124 constructs at least a portion of the distance estimation model M1, the region identification model M2, and the determination model M3 (e.g., each of all three trained models) through machine learning. The model construction unit 124 stores the constructed one or more trained models (e.g., the distance estimation model M1, the region identification model M2, and the determination model M3) in the model storage unit 126. The distance distribution evaluation unit 112, the region evaluation unit 118, and the deposition status evaluation unit 120 may each use the corresponding trained model stored in the model storage unit 126.

[0059] The control unit 122 adjusts the processing of the treatment object on the stoker 30 so as to reduce the difference in the accumulation status of the treatment object among the plurality of sections SC based on the relative relationship (the relative relationship of the accumulation status of the treatment object in the plurality of sections SC) acquired by the accumulation status evaluation unit 120. For example, the control unit 122 adjusts the processing of the treatment object on the stoker 30 so as to reduce the amount of treatment object on a section SC among the plurality of sections SC that is evaluated as having a relatively large amount of treatment object accumulated compared to the other sections.

[0060] In one example, the control unit 122 adjusts the grate speed of the movable grate 32 for each section SC using the drive device 33 so as to reduce the difference in the accumulation status of the materials to be treated among the multiple sections SC. Instead of or in addition to adjusting the grate speed, the control unit 122 adjusts the amount of air blown from the air blower 40 using the valve 42 for each section SC so as to reduce the difference in the accumulation status of the materials to be treated among the multiple sections SC.

[0061] Fig. 10 is a block diagram illustrating an example of the hardware configuration of the control device 100. The control device 100 is a control computer such as a programmable logic controller, and as shown in Fig. 10, has a circuit 190. The circuit 190 has a processor 191, a memory 192, a storage 193, an imaging control circuit 194, a grate control circuit 195, and a blower control circuit 196.

[0062] The storage 193 stores processing programs for causing the control device 100 to execute at least an image acquisition process, an area evaluation process, a distance distribution evaluation process, and an accumulation status evaluation process. These processes will be described later. The storage 193 stores, for example, processing programs for causing the control device 100 to configure each of the above-mentioned functional blocks. Specific examples of the storage 193 include read-only memory, non-volatile memory, and a hard disk. The storage 193 may also be a disk or portable media such as a USB memory.

[0063] The memory 192 temporarily stores the program loaded from the storage 193. A specific example of the memory 192 is a random access memory. The processor 191 configures the above-mentioned functional blocks in the control device 100 by executing the program loaded in the memory 192. The processor 191 stores the calculation results in the process of executing the program in the memory 192 as appropriate, and performs further calculations using the calculation results stored in the memory 192.

[0064] The imaging control circuit 194 acquires a field-of-view image VI from the imaging device 90 based on a command from the processor 191. The grate control circuit 195 controls the drive device 33 based on a command from the processor 191. The air blowing control circuit 196 controls the valve 42 based on a command from the processor 191.

[0065] [Method for Processing Object] Next, a method for processing an object to be processed, which is executed in the stoker-type processing apparatus 1, will be described. This processing method includes at least a transporting step, an imaging step, a region evaluation step, a distance distribution evaluation step, and a deposition status evaluation step. The transporting step is a step of transporting the object to be processed in a transport direction D1 that intersects the vertical direction by a stoker 30 that supports the object to be processed. The imaging step is a step of imaging a field of view FV that includes multiple sections SC on the stoker 30 by an imaging device 90. When this imaging step is executed, the image acquisition step is executed. The image acquisition step is a step of the control device 100 acquiring an image VI within the field of view. The distance distribution evaluation unit 112 of the control device 100 may execute the image acquisition step.

[0066] The area evaluation step is a step of identifying a target area Z1 in the field of view image VI, in which the processing target object exists, based on the field of view image VI obtained by imaging by the imaging device 90. The area evaluation unit 118 of the control device 100 may execute the area evaluation step. The distance distribution evaluation step is a step of acquiring distance distribution information DI indicating the distance from the imaging device 90 to an object in the field of view FV. The distance distribution evaluation step may be executed by the distance distribution evaluation unit 112 of the control device 100.

[0067] The accumulation status evaluation step is a step of acquiring the relative relationship of the accumulation status of the processing object in the multiple sections SC based on accumulation status information DI1 obtained by combining the distance distribution information DI and the result of identifying the target area Z1. The accumulation status evaluation step may be executed by the accumulation status evaluation unit 120 of the control device 100.

[0068] The imaging step, the area evaluation step, the distance distribution evaluation step, and the accumulation situation evaluation step may be executed after the model construction step of constructing the distance estimation model M1, the area identification model M2, and the determination model M3 has been executed. The model construction step may be executed before the stalker-type treatment device 1 is operated, or in the initial stage immediately after the stalker-type treatment device 1 is operated.

[0069] 11 is a flowchart showing a series of control processes executed by the control device 100 after the stoker-type treatment device 1 is put into operation and after the model construction process is executed. While this series of control processes is being executed, the combustion of the material to be treated on the stoker 30 and other processes continue.

[0070] First, the control device 100 executes step S11. In step S11, for example, the control device 100 waits until it is time to evaluate the accumulation status of the material to be treated on the stoker 30. This evaluation timing is set in advance, and is set, for example, so that the evaluation is repeated at a predetermined cycle.

[0071] Next, the control device 100 executes steps S12 and S13. In step S12, for example, the control device 100 causes the imaging device 90 to capture an image and acquires a field-of-view image VI from the imaging device 90. The control device 100 may include, as a functional block, an image acquisition unit that causes the imaging device 90 to capture an image and acquires the field-of-view image VI from the imaging device 90. In step S13, for example, the distance distribution evaluation unit 112 acquires, from the field-of-view image VI acquired in step S12, distance distribution information DI indicating distances to objects in the field-of-view image VI. In one example, the distance distribution evaluation unit 112 inputs the field-of-view image VI to a distance estimation model M1 and acquires information output from the distance estimation model M1 as distance distribution information DI.

[0072] Next, the control device 100 executes step S14. In step S14, for example, the area evaluation unit 118 identifies a target area Z1 in the field of view image VI where the processing target exists, based on the field of view image VI obtained in step S12. In one example, the area evaluation unit 118 identifies the target area Z1 in the field of view image VI from the field of view image VI obtained when the processing target exists on the stoker 30, without using any other image (for example, without using the field of view image VI in an empty state where no processing target exists on the stoker 30 as a comparison object). The area evaluation unit 118 inputs the field of view image VI to the area identification model M2 and identifies the target area Z1 from the information output from the area identification model M2.

[0073] Next, the control device 100 executes step S15. In step S15, for example, the accumulation situation evaluation unit 120 acquires accumulation situation information DI1 by combining the distance distribution information DI acquired in step S13 with the result of identifying the target area Z1 in step S14. In one example, the accumulation situation evaluation unit 120 acquires the accumulation situation information DI1 by extracting information on pixels that fall within the target area Z1 from the distance distribution information DI.

[0074] Next, the control device 100 executes step S16. In step S16, for example, the accumulation situation evaluation unit 120 acquires the relative relationship of the accumulation situations of the treatment target material in the multiple sections SC on the stoker 30 based on the accumulation situation information DI1 acquired in step S15. In one example, the accumulation situation evaluation unit 120 inputs the accumulation situation information DI1 into a determination model M3 and acquires information output from the determination model M3 as information indicating the relative relationship. The accumulation situation evaluation unit 120 may also acquire information indicating one or more sections among the multiple sections SC in which the accumulation amount of the treatment target material is greater than the other sections as information indicating the relative relationship.

[0075] Next, the control device 100 executes step S17. In step S17, for example, the control unit 122 controls the processing of the treatment target material on the stoker 30 based on the relative relationship obtained in step S16 so as to reduce the difference in the accumulation status of the treatment target material among the multiple sections SC. In one example, the control unit 122 adjusts the grate speed using the drive device 33 based on the relative relationship so as to reduce the difference in the accumulation amount of the treatment target material among the multiple sections SC. The control unit 122 may control the drive device 33 to increase the grate speed corresponding to the section SC that is evaluated to have a larger amount of treatment target material than the other sections in the relative relationship obtained in step S16.

[0076] The control device 100 repeatedly executes the series of processes of steps S12 to S17 at each evaluation timing, thereby reducing the bias in the accumulation state of the processing objects among the multiple sections SC.

[0077] [Modifications] The series of processes shown in Fig. 11 is an example and can be modified as appropriate. In the series of processes described above, the control device 100 may execute one step and the next step in parallel, or may execute the steps in an order different from that of the example described above. The control device 100 may omit any step. In the series of processes described above, the control device 100 may execute steps with content different from that of the example described above.

[0078] 2 and 3 , each of the multiple sections SC is divided into two groups arranged side by side. The control device 100 may acquire the relative relationship of the processing objects in the multiple sections SC for each of the left and right groups. In this case, the determination model M3 may be constructed by machine learning so as to output information indicating the relative relationship for each of the left and right groups.

[0079] The method for identifying the target region Z1 in the field of view image VI is not limited to the method using the region identification model M2 based on semantic segmentation. Figure 12 schematically shows a process for identifying the target region Z1 using a calculation different from the calculation using the region identification model M2. The region evaluation unit 118 repeatedly performs a process (hereinafter referred to as "threshold processing") of comparing the standard deviation of pixel values ​​in a divided region M of the field of view image VI with a threshold value. The divided region M is a portion of the field of view image VI. The divided region M is set to a size that will cause variation in pixel values ​​when the processing target object is included within the region.

[0080] The region evaluation unit 118 identifies the target region Z1 by performing the threshold processing while sliding (scanning) the coordinates of the representative point of the divided region M. The representative point of the divided region M is, for example, the center of the divided region M or the upper left corner of the divided region M. When sliding the coordinates of the representative point, the initial position of the divided region M is the upper left corner of the field-of-view image VI, and the coordinates are changed one by one in the horizontal direction on the image. After moving to the far right in the horizontal direction, the vertical coordinate is changed by one, and then the coordinate is changed in the horizontal direction. The region evaluation unit 118 repeats sliding the coordinates of the representative point and performing threshold processing until the divided region M moves to the lower right corner of the field-of-view image VI.

[0081] In FIG. 12 , an example of a divided region M when a processing object is not included is indicated by "M1," and the divided region M when a processing object is not included has a small variation in pixel values. An example of a divided region M when a processing object is included is indicated by "M2," and the divided region M when a processing object is included has a large variation in pixel values. If the standard deviation of pixel values ​​in a divided region M is larger than a threshold, the region evaluation unit 118 may determine that a processing object is present at the coordinates of a representative point of the divided region M. If the standard deviation of pixel values ​​in a divided region M is smaller than a threshold, the region evaluation unit 118 may determine that a processing object is not present at the coordinates of a representative point of the divided region M.

[0082] The area evaluation unit 118 may identify the target area Z1 in the field of view image VI by a set of coordinates of the representative points determined to have a standard deviation of pixel values ​​greater than a threshold. The threshold used for comparison with the standard deviation may be set by checking in advance the difference in the standard deviation of pixel values ​​depending on whether or not a processing target object is present. Even in the method using threshold processing, the area evaluation unit 118 identifies the target area Z1 in the field of view image VI from the field of view image VI obtained when a processing target object is present on the stoker 30, without using any other images.

[0083] Instead of using the region identification model M2, the region evaluation unit 118 may identify the target region Z1 by executing a process of detecting edges in the field-of-view image VI. The region evaluation unit 118 may execute edge detection using the Canny method as the process of detecting edges. For example, the region evaluation unit 118 executes edge detection using the Canny method by sequentially smoothing the image using a Gaussian filter or the like, identifying potential edge locations using a Sobel filter, and detecting edges using hysteresis.

[0084] By performing edge detection processing, the boundary between the target region Z1 and the non-target region Z2 in the field of view image VI can be detected as an edge. The region evaluation unit 118 may determine that the region surrounded by the set of coordinates detected as edges in the field of view image VI is the target region Z1. Even in the method using edge detection, the region evaluation unit 118 identifies the target region Z1 in the field of view image VI from the field of view image VI obtained when the processing target object is present on the stoker 30, without using any other images.

[0085] Instead of using the region identification model M2, the region evaluation unit 118 may identify the target region Z1 by evaluating the change in pixel value over time for each pixel in multiple field-of-view images VI. In one example, the region evaluation unit 118 acquires multiple field-of-view images VI obtained by continuing image capture by the imaging device 90 for a predetermined period of time. The predetermined period of time may be from several tens of seconds to several minutes, for example, 30 to 90 seconds. The predetermined period of time may be shorter than the time from the evaluation timing to the next evaluation timing.

[0086] The region evaluation unit 118 may acquire multiple field-of-view images VI by causing the imaging device 90 to capture images every second during the predetermined time. Figure 13 shows (N+1) field-of-view images VI obtained by repeatedly capturing images with the imaging device 90 during a period T corresponding to the predetermined time. The region evaluation unit 118 performs a process of comparing, for each pixel in the multiple field-of-view images VI, the standard deviation of the time change in pixel value at that pixel with a threshold value.

[0087] When focusing on a single pixel, if no processing target is present at that pixel, the change in pixel value over time is small, resulting in a small standard deviation of the pixel value. On the other hand, if a processing target is present at that pixel, the change in pixel value over time is large, resulting in a large standard deviation of the pixel value. The area evaluation unit 118 may determine the last in-field image VI(N) obtained within a predetermined period of time as the image to be used to identify the target area Z1. The area evaluation unit 118 may identify the target area Z1 in the in-field image VI(N) by using a set of coordinates of pixels whose standard deviation in the change in pixel value over time is determined to be greater than a threshold.

[0088] Even in the method that uses a process that compares the standard deviation of pixel values ​​over time with a threshold value, the area evaluation unit 118 identifies the target area Z1 in the field of view image VI from (multiple) field of view images VI obtained when the processing target object is present on the stalker 30, without using any other images.

[0089] The method for evaluating the relative relationship from the deposition status information DI1 is not limited to the method using the determination model M3, which is a trained model constructed by machine learning. A method for evaluating (obtaining) the relative relationship using a method different from the method using the determination model M3 will be described with reference to Figures 14(a), 14(b), and 15.

[0090] The deposition status evaluation unit 120 executes the following two processes. The first process is a process of calculating, for each pixel in the deposition status information DI1, three-dimensional coordinates consisting of the two-dimensional coordinates and distance (depth from the imaging device 90) of that pixel. The second process is a process of obtaining a relative relationship based on information indicating areas corresponding to multiple sections SC on a three-dimensional graph using the two-dimensional coordinates and the distance as variables, and the calculation results of the three-dimensional coordinates for each pixel in the deposition status information.

[0091] Here, the three-dimensional coordinates used in the above two processes are expressed as "(d2, z, d3)". d2 is the horizontal coordinate of the accumulation status information DI1 (two-dimensional image), and z is the vertical coordinate of the accumulation status information DI1. d3 is the distance from the imaging device 90 at the coordinate (d2, z), and can also be referred to as the coordinate in the depth direction D3. In the various examples described above, the coordinates (d2, z) of each pixel may be the same among the field-of-view image VI, the distance distribution information DI, and the accumulation status information DI1. The unit of the two-dimensional coordinates may also be the unit of distance from a predetermined reference position.

[0092] 14(a) schematically shows the three-dimensional coordinates of several pixels. The two-dimensional coordinates of the pixel designated by "A" (hereinafter referred to as "pixel A") are (350, 2000), and the distance of pixel A in the distance distribution information DI is 2500. The two-dimensional coordinates of the pixel designated by "B" (hereinafter referred to as "pixel B") are (400, 500), and the distance of pixel B in the distance distribution information DI is 500. The two-dimensional coordinates of the pixel designated by "C" (hereinafter referred to as "pixel C") are (1200, 1500), and the distance of pixel C in the distance distribution information DI is 1500.

[0093] The right side of Fig. 14(a) illustrates an example of a three-dimensional graph (hereinafter referred to as "three-dimensional graph Gr") in which two-dimensional coordinates on the image and distance from the imaging device 90 are variables. This three-dimensional graph Gr is a graph having a three-dimensional Cartesian coordinate system, and the three axes represent distance in the width direction D2, vertical direction (direction Z), and depth direction D3. In the three-dimensional graph Gr shown in Fig. 14(a), the three-dimensional coordinates of pixel A, pixel B, and pixel C are plotted.

[0094] The left side of Fig. 14(b) illustrates deposition status information DI1, and the right side of Fig. 14(b) illustrates a three-dimensional graph Gr obtained by plotting the three-dimensional coordinates of each pixel in the distance distribution information DI on a graph. In Fig. 14(b), the darkened parts in the three-dimensional graph Gr are a set of points plotting the three-dimensional coordinates of all pixels (all pixels in a region having a pixel value) in the distance distribution information DI. This three-dimensional graph Gr can show how much of the object to be processed has deposited at what location (i.e., the deposition status of the object to be processed).

[0095] Areas corresponding to multiple sections SC can be defined in advance on the three-dimensional graph Gr. FIG. 15 schematically illustrates the result of defining areas corresponding to multiple sections SC on the three-dimensional graph Gr. The control device 100 has, for example, an area information storage unit 132 as a functional block (see FIG. 4). The area information storage unit 132 stores area information indicating areas corresponding to multiple sections SC on the three-dimensional graph Gr. The area information is defined in advance, for example, by an operator of the stalker-type processing device 1. The area information defines a three-dimensional range on the three-dimensional graph Gr for each section SC.

[0096] In one example, the accumulation situation evaluation unit 120 calculates the three-dimensional coordinates (d2, z, d3) of each pixel from the two-dimensional coordinates and pixel values ​​of all pixels in the accumulation situation information DI1. Then, the accumulation situation evaluation unit 120 counts how many three-dimensional coordinates of pixels in the accumulation situation information DI1 are included in each section SC whose range on the three-dimensional graph is defined by the area information. Thereafter, the accumulation situation evaluation unit 120 obtains the relative relationship of the accumulation situations of the processing object in the multiple sections SC from the relative difference in the number of pixels in the multiple sections SC.

[0097] For example, when the data plotted in the three-dimensional graph Gr shown in Fig. 14(b) is obtained and the number of pixels falling in each of sections 1 to 5 (SC1 to SC5) shown in Fig. 15 is counted, the number of pixels in sections 4 and 5 is greater than in the other sections. In this case, the accumulation status evaluation unit 120 evaluates that more material to be processed has accumulated in sections 4 and 5 than in the other sections.

[0098] As described above, in an image acquired by the imaging device 90 (camera), the size of an object on the image changes depending on the distance (depth) to the object. To avoid overestimating a processing target object at a certain location on the image and underestimating a processing target object at a certain distance, the accumulation status evaluation unit 120 may correct the number of pixels included in the section SC according to the distance in the depth direction D3. For example, for each section SC, the accumulation status evaluation unit 120 may add or multiply the number of pixels included in the section by a correction constant according to the distance of the section in the depth direction D3. The correction constant is predetermined so that its value increases as the distance in the depth direction D3 increases.

[0099] The objects for evaluating the relative relationship of the accumulation status of the processing object are not limited to multiple sections SC arranged in the conveying direction D1. Instead of or in addition to evaluating the relative relationship in the conveying direction D1, the accumulation status evaluation unit 120 may evaluate the relative relationship between two or more sections arranged in the width direction D2. In one example of the various examples described above, at least some of the matters described in the other examples may be combined.

[0100] [Summary of the present disclosure] The stoker-type treatment device (1) described above includes a stoker (30) that supports an object to be treated and transports the object to be treated in a transport direction (D1) that intersects the vertical direction; an imaging device (90) that captures an image of a field of view (FV) that includes multiple compartments (SC) on the stoker (30); an area evaluation unit (118) that identifies a target area (Z1) in the field of view image (VI) where the object to be treated exists, based on a two-dimensional image (VI) in the field of view obtained by imaging with the imaging device (90); a distance distribution evaluation unit (112) that acquires distance distribution information indicating the distance from the imaging device (90) to an object in the field of view (FV); and a deposition situation evaluation unit (120) that acquires the relative relationship of deposition situations of the object to be treated in the multiple compartments (SC) based on deposition situation information (DI1) obtained by combining the distance distribution information (DI) and the result of identifying the target area (Z1). The accumulation status information (DI1) obtained by combining the distance distribution information (DI) and the result of identifying the target area (Z1) includes information indicating the position of the object to be treated in the field of view (FV) and the extent to which it has accumulated compared to other positions. By obtaining the relative relationship between the accumulation statuses of the object to be treated in multiple compartments (SC) based on such accumulation status information (DI1), the accumulation status of the object to be treated on the stoker (30) can be easily obtained. Therefore, the stoker-type treatment device (1) is useful for simplifying the process of obtaining the accumulation status of the object to be treated on the stoker.

[0101] In the stoker-type treatment apparatus (1) described above, the multiple compartments (SC) may be arranged in the conveying direction (D1), which can reduce the difference in the accumulation state (accumulation amount) of the treatment object in the conveying direction (D1).

[0102] In the stoker-type treatment apparatus (1) described above, the imaging device (90) may generate a field-of-view image (VI) by focusing mid-infrared light incident from the field of view (FV). In this case, the imaging device (90) can acquire information that is less affected by flames, steam, carbon dioxide, and the like generated by the combustion of the treatment object. Therefore, the accumulation status of the treatment object on the stoker can be acquired with high accuracy.

[0103] In the stalker-type processing device (1) described above, the distance distribution evaluation unit (112) may acquire distance distribution information (DI) by inputting the field-of-view image (VI) to a distance estimation model (M1) constructed by machine learning so as to output information representing the distribution of distances to objects in the two-dimensional image in response to the input of the two-dimensional image. In this case, by using a trained model, it is not necessary to acquire distance information separately from the field-of-view image (VI). This is useful for simplifying the device.

[0104] In the stoker-type treatment device (1) described above, the accumulation situation evaluation unit (120) may acquire the relative relationship by inputting the accumulation situation information (DI1) to a determination model (M3) constructed by machine learning so as to output information indicating the relative relationship of the accumulation situations of the treatment object in multiple compartments (SC) in response to input information corresponding to the accumulation situation information (DI1). In this case, the accumulation situation information (DI1), which serves as input data, can be input to the model to acquire the relative relationship, thereby simplifying the calculation for acquiring the accumulation situation.

[0105] In the stoker-type processing device (1) described above, the distance distribution information (DI) may include information indicating the distance for each pixel in the field-of-view image (VI). The accumulation situation evaluation unit (120) may calculate three-dimensional coordinates consisting of the two-dimensional coordinates of each pixel in the accumulation situation information (DI1) and the distance for each pixel, and obtain the relative relationship based on area information indicating regions corresponding to each of multiple sections (SC) on a three-dimensional graph (Gr) using the two-dimensional coordinates and the distance as variables, and the calculation results of the three-dimensional coordinates for each pixel in the accumulation situation information (DI1). In this case, there is no need to create a determination model to obtain the relative relationship from the accumulation situation information (DI1), thereby simplifying the preparation work.

[0106] In the stoker-type treatment device (1) described above, the area evaluation unit (118) may input the field-of-view image (VI) into the area identification model (M2) constructed by semantic segmentation so as to output information indicating the classification results for each pixel in the two-dimensional image into an area where the treatment object exists and an area where the treatment object does not exist, in response to the input of a two-dimensional image. In this case, to identify the target area (Z1), no image other than the field-of-view image (VI) obtained when the stoker-type treatment device (1) is operating and the treatment object is present on the stoker (30) is required. For example, it is possible to obtain an initial image corresponding to the field-of-view image (VI) before the stoker-type treatment device (1) is operating (when the treatment object is not on the stoker 30), and calculate the difference between the field-of-view image (VI) and the initial image to identify the area where the treatment object exists. However, there is no need to use such an initial image. This is therefore even more useful for simplifying the process of acquiring the deposition status of the treatment object on the stoker.

[0107] In the stoker-type treatment device (1) described above, the area evaluation unit (118) may identify the target area (Z1) by performing a process of comparing the standard deviation of pixel values ​​in the divided area (M) of the field-of-view image (VI) with a threshold value while sliding the coordinates of the representative point of the divided area (M). Similarly, in this case, in order to identify the target area (Z1), no images other than the field-of-view image (VI) obtained when the stoker-type treatment device (1) is operating and there is a treatment target on the stoker (30) are required. This is therefore even more useful in simplifying the process of acquiring the deposition status of the treatment target on the stoker.

[0108] In the stoker-type treatment device (1) described above, the area evaluation unit (118) may identify the target area (Z1) by executing a process for detecting edges in the field-of-view image (VI). Similarly, in this case, in order to identify the target area (Z1), no images other than the field-of-view image (VI) obtained when the stoker-type treatment device (1) is operating and the treatment target is present on the stoker (30) are required. This is therefore even more useful for simplifying the process of acquiring the accumulation status of the treatment target on the stoker.

[0109] In the stoker-type treatment device (1) described above, the area evaluation unit (118) may acquire a plurality of field-of-view images (VI(0) to VI(N)), including the field-of-view image (VI), by continuing imaging with the imaging device (90) for a predetermined period of time, and identify the target area (Z1) by performing a process of comparing, for each pixel in the plurality of field-of-view images (VI(0) to VI(N)), the standard deviation of the time change in pixel value with a threshold value. In this case, in order to identify the target area (Z1), no images other than the plurality of field-of-view images (VI) obtained when the stoker-type treatment device (1) is operating and the treatment target is present on the stoker (30) are required. Therefore, this is even more useful for simplifying the process of acquiring the accumulation status of the treatment target on the stoker.

[0110] The stoker-type treatment apparatus (1) described above may further include a control unit (122) that adjusts the treatment of the treatment object on the stoker (30) so as to reduce the difference in the accumulation state of the treatment object among the plurality of compartments (SC) based on the correlation acquired by the accumulation state evaluation unit (120). In this case, the difference in the accumulation state of the treatment object among the plurality of compartments (SC) is reduced, which is useful for improving the efficiency of treatment by the stoker-type treatment apparatus (1).

[0111] The above-described method for treating an object to be treated includes a transport step of transporting the object to be treated in a transport direction (D1) intersecting the vertical direction by a stoker (30) supporting the object to be treated, an imaging step of capturing an image of a field of view (FV) including multiple compartments (SC) on the stoker (30) using an imaging device (90), a region evaluation step of identifying a target region (Z1) in the field of view image (VI) where the object to be treated exists based on a two-dimensional field of view image (VI) captured by the imaging device (90), a distance distribution evaluation step of acquiring distance distribution information (DI) indicating the distance from the imaging device (90) to an object in the field of view (FV), and a deposition status evaluation step of acquiring the relative relationship between the deposition status of the object to be treated in the multiple compartments (SC) based on deposition status information (DI1) obtained by combining the distance distribution information (DI) and the result of identifying the target region (Z1). This treatment method, like the stoker-type treatment device (1), is useful for simplifying the process of acquiring the deposition status of the object to be treated on the stoker.

[0112] The processing program described above is a processing program for causing a computer to execute the following steps: an image acquisition step for acquiring a two-dimensional field-of-view image (VI) obtained by capturing an image of a field of view (FV) including multiple compartments (SC) on a stoker (30) that supports the object to be processed and transports the object to be processed in a transport direction (D1) that intersects the vertical direction using an imaging device (90); a region evaluation step for identifying a target region (Z1) in the field-of-view image (VI) where the object to be processed exists based on the field-of-view image (VI); a distance distribution evaluation step for acquiring distance distribution information (DI) indicating the distance from the imaging device (90) to an object in the field of view (FV); and a deposition situation evaluation step for acquiring the relative relationship between the deposition situations of the object to be processed in the multiple compartments (SC) based on deposition situation information (DI1) obtained by combining the distance distribution information (DI) and the result of identifying the target region (Z1). This processing program, like the stoker-type processing apparatus (1), is useful for simplifying the process of acquiring the deposition situation of the object to be processed on the stoker.

[0113] 1...stoker-type processing device, 30...stoker, D1...conveying direction, SC...section, 90...imaging device, FV...field of view, VI...image within field of view, 112...distance distribution evaluation unit, DI...distance distribution information, M1...distance estimation model, 118...area evaluation unit, Z1...target area, M2...area identification model, 120...accumulation status evaluation unit, M3...judgment model.

Claims

1. A stoker-type processing device comprising: a stoker that supports an object to be processed and transports the object to be processed in a transport direction that intersects the vertical direction; an imaging device that images a field of view that includes multiple compartments on the stoker; an area evaluation unit that identifies a target area in the field of view image in which the object to be processed exists based on a two-dimensional image within the field of view obtained by imaging with the imaging device; a distance distribution evaluation unit that obtains distance distribution information that indicates the distance from the imaging device to an object within the field of view; and a deposition situation evaluation unit that obtains the relative relationship of the deposition situations of the object to be processed in the multiple compartments based on deposition situation information obtained by combining the distance distribution information with the result of identifying the target area.

2. The stoker-type treatment device according to claim 1, wherein the plurality of compartments are aligned in the conveying direction.

3. The stalker-type processing device according to claim 1, wherein the imaging device forms an image of mid-infrared light incident from the field of view to generate the image within the field of view.

4. The stalker-type processing device of claim 1, wherein the distance distribution evaluation unit acquires the distance distribution information by inputting the image within the field of view into a distance estimation model constructed by machine learning so as to output information representing the distribution of distances to objects in a two-dimensional image in response to the input of the two-dimensional image.

5. A stoker-type treatment device as described in any one of claims 1 to 4, wherein the accumulation situation evaluation unit obtains the relative relationship by inputting accumulation situation information into a judgment model constructed by machine learning so as to output information indicating the relative relationship of the accumulation situations of the treatment object in the multiple sections in response to input information corresponding to the accumulation situation information.

6. A stoker-type processing device as described in any one of claims 1 to 4, wherein the distance distribution information includes information indicating the distance for each pixel in the field of view image, and the accumulation situation evaluation unit calculates, for each pixel in the accumulation situation information, three-dimensional coordinates consisting of the two-dimensional coordinates of the pixel and the distance, and obtains the relative relationship based on area information indicating regions corresponding to each of the multiple sections on a three-dimensional graph with the two-dimensional coordinates and the distance as variables, and the calculation results of the three-dimensional coordinates for each pixel in the accumulation situation information.

7. A stalker-type processing device as described in any one of claims 1 to 4, wherein the area evaluation unit identifies the target area by inputting the image within the field of view into an area identification model constructed by semantic segmentation so as to output information indicating the result of classifying each pixel in the two-dimensional image into an area where the processing object exists and an area where the processing object does not exist in response to an input of a two-dimensional image.

8. A stalker-type processing device as described in any one of claims 1 to 4, wherein the area evaluation unit identifies the target area by performing a process of comparing the standard deviation of pixel values ​​in a divided area of ​​the image within the field of view with a threshold value while sliding the coordinates of a representative point of the divided area.

9. A stalker-type processing device according to any one of claims 1 to 4, wherein the area evaluation unit identifies the target area by executing a process for detecting edges in the image within the field of view.

10. A stalker-type processing device as described in any one of claims 1 to 4, wherein the area evaluation unit acquires a plurality of in-field images including the in-field image, the plurality of in-field images being obtained by continuing imaging with the imaging device for a predetermined period of time, and identifies the target area by performing a process of comparing, for each pixel in the plurality of in-field images, the standard deviation of the change in pixel value over time with a threshold value.

11. A stoker-type treatment device as described in any one of claims 1 to 4, further comprising a control unit that adjusts the treatment of the object on the stoker so as to reduce differences in the accumulation conditions of the object to be treated among the multiple sections based on the relative relationship obtained by the accumulation condition evaluation unit.

12. A method for processing an object to be processed, comprising: a transporting step of transporting the object to be processed in a transport direction intersecting the vertical direction by a stoker supporting the object to be processed; an imaging step of capturing an image of a field of view including a plurality of compartments on the stoker by an imaging device; an area evaluation step of identifying a target area in the field of view image in which the object to be processed exists based on a two-dimensional image of the field of view obtained by imaging with the imaging device; a distance distribution evaluation step of acquiring distance distribution information indicating the distance from the imaging device to an object in the field of view; and a deposition situation evaluation step of acquiring the relative relationship of the deposition situations of the object to be processed in the plurality of compartments based on deposition situation information obtained by combining the distance distribution information with the result of identifying the target area.

13. A processing program for causing a computer to execute the following steps: an image acquisition step for acquiring a two-dimensional image within a field of view obtained by capturing an image of a field of view including multiple compartments on a stoker that supports an object to be processed and transports the object to be processed in a transport direction that intersects the vertical direction using an imaging device; an area evaluation step for identifying a target area within the field of view image in which the object to be processed exists based on the image within the field of view; a distance distribution evaluation step for acquiring distance distribution information indicating the distance from the imaging device to an object within the field of view; and a deposition situation evaluation step for acquiring the relative relationship of the deposition situation of the object to be processed in the multiple compartments based on deposition situation information obtained by combining the distance distribution information with the result of identifying the target area.

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