Information processing device, information processing method, and program

Abstract

To provide an information processing system, information processing method, and program which are capable of estimating the operating state of a facility.SOLUTION: An information processing system includes: an acquisition unit that acquires detection information detected in the air, containing state information corresponding to the state of a specified emission in a specified area space on the ground, and an operating state estimation unit that estimates the operating state of a facility associated with the area space based on the detection information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Patent document 1 describes an image processing method for estimating the filling volume of a container based on satellite images. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2019-514123 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the image processing method described in Patent Document 1 has the problem that while it is possible to estimate the filling volume of a container at the time that an image was taken based on each image, it is not possible to estimate whether the contents of the container are in use, i.e., whether the equipment is in operation.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide an information processing device, an information processing method, and a program capable of estimating the operating state of equipment. [Means for solving the problem]

[0006] In order to solve the above problems, the information processing device disclosed herein includes an acquisition unit that acquires detection information detected in the air including status information corresponding to the state of a specified emission within a specified area space on the ground, and an operation status estimation unit that estimates the operation status of equipment associated with the area space based on the detection information.

[0007] The information processing method of the present disclosure includes a step of acquiring detection information detected in the air, the detection information including status information corresponding to the status of a specified emission within a specified area space on the ground, and a step of estimating the operating status of equipment associated with the area space based on the detection information.

[0008] The program of the present disclosure causes a computer to execute the steps of acquiring detection information detected in the air, the detection information including status information corresponding to the status of a specified emission within a specified area space on the ground, and estimating the operating status of equipment associated with the area space based on the detection information. Effect of the Invention

[0009] According to the information processing device, information processing method, and program of the present disclosure, the operating state of equipment can be estimated. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of a basic configuration of an information processing device according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a schematic diagram showing a configuration example of facility information according to the first embodiment of the present disclosure. [Diagram 3] FIG. 2 is a schematic diagram showing an example of an area space according to the first embodiment of the present disclosure. [Figure 4] FIG. 2 is a schematic diagram showing an example of an area space according to the first embodiment of the present disclosure. [Diagram 5] FIG. 2 is a schematic diagram showing an example of an area space according to the first embodiment of the present disclosure. [Figure 6] FIG. 1 is a schematic diagram showing an example of the configuration of a trained model according to a first embodiment of the present disclosure. [Figure 7] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 8] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 9] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 10] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 11] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 12] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 13] 5 is a flowchart showing an operation example of the information processing device according to the first embodiment of the present disclosure. [Figure 14] FIG. 11 is a schematic diagram showing an example of the configuration of a trained model according to a second embodiment of the present disclosure. [Figure 15] FIG. 11 is a schematic diagram showing an example of the configuration of a trained model according to a second embodiment of the present disclosure. [Figure 16] 10 is a flowchart showing an operation example of the information processing device according to the second embodiment of the present disclosure. [Figure 17] 10 is a flowchart showing an operation example of the information processing device according to the second embodiment of the present disclosure. [Figure 18] 10 is a flowchart showing an operation example of the information processing device according to the second embodiment of the present disclosure. [Figure 19] FIG. 11 is a schematic diagram showing an example of an area space according to a second embodiment of the present disclosure. [Figure 20] FIG. 11 is a schematic diagram showing an example of an area space according to a second embodiment of the present disclosure. [Figure 21] 13 is a flowchart showing an operation example of the information processing device according to the third embodiment of the present disclosure. [Figure 22] 13 is a flowchart showing an operation example of the information processing device according to the third embodiment of the present disclosure. [Figure 23] FIG. 13 is a schematic diagram illustrating an example of an area space according to a third embodiment of the present disclosure. [Figure 24] FIG. 13 is a schematic diagram illustrating an example of an area space according to a third embodiment of the present disclosure. [Diagram 25] FIG. 13 is a schematic diagram illustrating an example of an area space according to a third embodiment of the present disclosure. [Figure 26]13 is a flowchart showing an operation example of the information processing device according to the fourth embodiment of the present disclosure. [Figure 27] 13 is a flowchart showing an operation example of the information processing device according to the fourth embodiment of the present disclosure. [Figure 28] FIG. 13 is a schematic diagram showing an example of an area space according to a fourth embodiment of the present disclosure. [Figure 29] 13 is a flowchart showing an operation example of the information processing device according to the fifth embodiment of the present disclosure. [Diagram 30] FIG. 1 is a system diagram illustrating an example of a business flow according to an embodiment of the present disclosure. [Diagram 31] FIG. 1 is a schematic block diagram illustrating a configuration of a computer according to at least one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] <Basic configuration example of each embodiment> Hereinafter, an information processing device, an information processing method, and a program according to an embodiment of the present disclosure will be described with reference to the drawings. Note that in each drawing, the same or corresponding components are designated by the same reference numerals and descriptions thereof will be omitted as appropriate.

[0012] Fig. 1 is a block diagram showing an example of a basic configuration of an information processing device according to an embodiment of the present disclosure. The basic configuration of the information processing device 1 shown in Fig. 1 is the basic configuration of the information processing device of the first to fifth embodiments described below, and the information processing device according to the first to fifth embodiments can be configured by changing (including omissions and additions) a part of the configuration of the information processing device 1 shown in Fig. 1. In the description of each embodiment, the changed parts from the information processing device 1 shown in Fig. 1 will be mainly described.

[0013] 1 can be configured using a computer and peripheral devices such as a mouse, a touch panel, and a display device of the computer, and includes the following units as a functional configuration configured by a combination of hardware such as a computer and software such as a program executed by the computer. That is, the information processing device 1 includes an acquisition unit 11, an operation state estimation unit 12, a production volume estimation unit 13, a position information acquisition unit 14, a state estimation unit 15, a trained model 16, detection information 17, equipment information 18, and a model training unit 19. The trained model 16 includes at least one of a general-purpose trained model 161 and an individual trained model 162.

[0014] The acquisition unit 11 acquires detection information detected in the air including status information corresponding to the status of a predetermined emission 41 in a predetermined area space 51 on the ground, for example from a detection information providing system 2, and stores it as detection information 17 in a storage device or the like provided in a computer constituting the information processing device 1. The detection information is visible light image data, thermal infrared image data, or the like corresponding to the area space 51 acquired from the air by an artificial satellite 21 (or a drone, an aircraft, a high-rise building), or the like.

[0015] The state information included in the detection information is, for example, information that is presumed to be included when the discharged object 41 is present in the visible light image data obtained by capturing the areal space 51, and can be defined by information on the luminance value of each RGB (color) channel or the luminance value of a predetermined gradation of gray, or the shape and size of a range having the luminance value. Alternatively, the state information included in the detection information is, for example, information that is presumed to be included when the discharged object 41 is present in the thermal infrared image data obtained by capturing the areal space 51, and can be defined by information on the luminance value of a predetermined gradation, or the shape and size of a range having the luminance value. Alternatively, the state information included in the detection information may be, for example, information (such as information corresponding to the ground or a building) that is presumed to be included when the discharged object 41 is not present in the visible light image data or thermal infrared image data obtained by capturing the areal space 51.

[0016] The emissions 41 are substances emitted from equipment 71 such as production equipment in a plant or the like, and are, for example, white smoke, gas, heat, mist, etc. In this case, a chimney 61 that emits the emissions 41 is the analysis target that serves as the reference for determining the state of the emissions 41. The analysis target can be specified by the structure that emits the emissions or the emission location. However, the analysis target may be multiple objects such as multiple chimneys, cooling towers, drains, etc., or the entire plant or part of a plant equipped with multiple pieces of equipment. The area space 51 is defined so as to surround the analysis target (in this case, the chimney 61).

[0017] The operation state estimation unit 12 estimates the operation state of the equipment 71 associated with the area space 51 based on the detection information 17. Based on the state information corresponding to the state of the discharged material 41 contained in the detection information 17, the operation state estimation unit 12 estimates that the equipment 71 is operating when the discharge unit 41 is discharging, and estimates that the equipment 71 is not operating (non-operating) when the discharge unit 41 is not discharging, for example.

[0018] The operating state estimation unit 12 can estimate the operating state by using a trained model 16 that inputs detection information and outputs a classification result that classifies the operating state into at least operating or non-operating. The trained model 16 is, for example, a model having a neural network as an element, and weighting coefficients between neurons in each layer of the neural network are optimized by machine learning so that a desired solution is output for a large amount of input data. The trained model 16 is, for example, composed of a combination of a program that performs calculations from input to output and weighting coefficients (parameters) used in the calculations. The general-purpose trained model 161 is, for example, a model having a neural network as an element, and is a model trained using multiple pieces of detection information obtained in multiple areal spaces 51 as teacher data, and can be commonly applied to multiple areal spaces 51. The individual trained model 162 is, for example, a model having a neural network as an element, and is a model retrained for each areal space 51 using, as initial values, weighting coefficients between neurons in each layer of a neural network (general-purpose trained model 161) trained using multiple pieces of detection information corresponding to multiple different areal spaces 51 as teacher data. The individual learning model 162 is prepared for each areal space 51 and applied to each areal space 51. By using the weighting coefficients obtained by the general-purpose trained model 161 as initial values, even if the amount of detection information 17 obtained for each areal space 51 is small, the detection information 17 obtained in different areal spaces 51 can be used as training data, allowing the model to be trained efficiently.

[0019] The trained model 16 (general trained model 161 and individual trained model 162) may further input meteorological information corresponding to the area space 51 and output a classification result. The trained model 16 (general trained model 161 and individual trained model 162) may further input a previous classification result and output the classification result. For example, for equipment that is operated continuously for a certain period of time, the operating state of the estimation target is affected by the operating state a predetermined time (e.g., one day) ago, so that the estimation accuracy can be improved by further recursively inputting a previous estimation result.

[0020] Furthermore, when the equipment 71 is a production equipment, the production volume estimation unit 13 estimates the production volume of the equipment 71 (production equipment) based on the operation state of the equipment 71 estimated by the operation state estimation unit 12. The production volume estimation unit 13 estimates the production volume to be a predetermined volume when the equipment is operating, and estimates the production volume to be a predetermined volume (or zero) smaller than when the equipment is operating, based on, for example, equipment information (production volume when the equipment is operating, or information for estimating the production volume when the equipment is operating) included in the equipment information 18 prepared in advance.

[0021] The position information acquisition unit 14 acquires position information of one or more mobile terminals 31 within a predetermined region corresponding to the area space 51, for example, from the position information providing system 3. The state estimation unit 15 estimates a non-operating state when the operation state of the equipment 71 is non-operating based on the position information acquired by the position information acquisition unit 14. The non-operating state is, for example, a state that indicates whether the equipment 71 is normally non-operating or abnormally non-operating (for example, due to construction or the like). For example, the state estimation unit 15 can estimate normal non-operating state when there is no significant change in the position information by comparison with the position information obtained during operation, and estimate abnormal non-operating state when there is a significant change in the position information.

[0022] When the emissions 41 are white smoke and the detection information 17 is color or monochrome image data, the production volume estimation unit 13 can estimate the production volume by correcting it according to the season. Since the ease of imaging white smoke is affected by temperature and humidity, and is also affected by the season, the correction can reduce the estimation error.

[0023] Furthermore, in the case where the emission 41 is heat, and the detection information 17 is thermal infrared image data, the operation state estimation unit 12 may estimate the operation state of the equipment 71 based on a predetermined temperature difference based on the detection information 17. For example, the operation state can be estimated from the difference between the inlet temperature and the outlet temperature of the cooling water.

[0024] In addition, when the equipment 71 is a production equipment and a power generation equipment, when the operation state estimation unit 12 cannot estimate the operation state based on the detection information 17, the production volume estimation unit 13 may estimate the production volume of the power generation equipment based on the configuration of the system to which the power generation equipment belongs.

[0025] In addition, the model creation unit 19 uses the detection information 17, etc. to train or retrain the trained model 16.

[0026] As described above, according to the information processing device 1 of this embodiment, the operating status of the equipment 17 can be estimated based on detection information (detection information 17) detected in the air, which includes status information corresponding to the status of a specified emission material 41 within a specified area space 51 on the ground.

[0027] First Embodiment Next, an information processing device, an information processing method, and a program according to the first embodiment of the present disclosure will be described with reference to Figs. 2 to 13. Fig. 2 is a schematic diagram showing a configuration example of facility information according to the first embodiment of the present disclosure. Figs. 3 to 5 are schematic diagrams showing an example of an area space according to the first embodiment of the present disclosure. Fig. 6 is a schematic diagram showing a configuration example of a trained model according to the first embodiment of the present disclosure. Figs. 7 to 13 are flowcharts showing an operation example of the information processing device according to the first embodiment of the present disclosure.

[0028] In the information processing device 1 according to the first embodiment, the location information acquisition unit 14, the state estimation unit 15, and the individual trained model 162 can be omitted from the configuration shown in Fig. 1. In the first embodiment and each of the following embodiments, the target of the estimation of the operating state is a production facility (power generation facility) that produces electricity. In the first embodiment, the detection information is color image data.

[0029] FIG. 2 shows an example of the configuration of the facility information 18 shown in FIG. 1 in the first embodiment. The facility information 18 shown in FIG. 2 includes an attribute information database 181 and a geographic information database 182. The attribute information database 181 stores an "object No." which is an identification number of an object to be analyzed (such as a chimney 61), attribute information (output, fuel type, plant name) indicating attributes of the facility 71, and geographic information (latitude and longitude P1, P2, P3, and P4 of four vertices when the areal space 51 is a quadrilateral polygon) in association with a "unit name" which is the name of the facility 71. The geographic information database 182 stores an "object No." and geographic information (latitude and longitude P1, P2, P3, and P4 of four vertices when the areal space 51 is a quadrilateral polygon) in association with each other.

[0030] The facility information 18 can be set using maps (satellite images, etc.) and coordinate information obtained from a base map service 301 that provides map information, public information 302 on the Internet, etc., information obtained from an external information service (external service) 303, and information estimated from the appearance and dimensions (appearance and dimensions of an object) 304 of an object (facility, etc.) obtained from image data, etc.

[0031] (Processing flow of the information processing device of the first embodiment) In the information processing device 1 of the first embodiment, the production volume of the equipment associated with the analysis target is calculated according to the following steps (1) to (9).

[0032] (1) For the image (detection information 17) obtained by photographing, an analysis object (such as a plant, a chimney, a cooling tower, etc.) suitable for estimating production volume based on the state of emissions (white smoke, gas, fog, etc.) is identified, and geographical information is added to an area space centered on the identified analysis object. For example, for a satellite image (such as an image of a plant area space 501 shown in FIG. 3), the position of a power plant chimney 601 is marked as a polygon ("rectangle") at four points P1, P2, P3, and P4 as shown in FIG. 4, and thus geographical information can be added to an area space 511 centered on the analysis object. Note that in FIG. 3 and FIG. 4, the chimney 601 is a chimney for emitting exhaust gas 401 shared by power generation units 701 and 702. Alternatively, for the image of the plant area space 501 shown in Figure 3, for example, by marking the analysis object while specifying the position of the cooling tower 602 in the shape of a polygon ("rectangle") with four points P1, P2, P3 and P4 as shown in Figure 5, geographic information can be added to the area space 512 centered on the analysis object.

[0033] (2) The attribute information of the analysis target is associated with the area space to which the geographical information is assigned, and stored as, for example, facility information 18 shown in Fig. 2. For example, the attribute information of the object is associated with the marked chimney and power plant based on the publicly available information on the Internet, external services, and appearance and dimensions, and stored in the attribute information database 181.

[0034] (3) Images corresponding to the area space based on the geographic information are acquired from any image provider. For example, images for a target period (e.g., daily images for several months or several years) are downloaded for the target of analysis and stored as detection information 17.

[0035] (4) The acquired images are classified into learning images and inference images. For example, the downloaded images are classified into learning images and inference images by, for example, random sampling.

[0036] (5) Status information (operation information) required to estimate the production volume of the target to be analyzed is added to the learning images, images in which emissions are being emitted are labeled as "operating," images in which emissions are not being emitted are labeled as "stopped," and images in which it is difficult to distinguish are labeled as "unknown," and learning is performed using a learning model to generate a classifier (trained model 16) that automatically infers the status information of any image. For example, learning images in which white smoke is being emitted are labeled as Active (operating), images in which white smoke is not being emitted are labeled as Inactive (not operating), and images in which it is unclear whether white smoke is being emitted or not are labeled as Unknown, and learning is performed using a learning model (convolutional neural network) to generate a trained model.

[0037] (6) An image for inference is input to a classifier (trained model 16), and status information (operation information) of the analysis target is obtained as a classification result. By inputting the image for inference into the model, the operating status of the plant (Active / Inactive / Unknown) is estimated.

[0038] (7) Based on the classification results, combine the attribute information of the analysis target to obtain the production volume of the analysis target. The estimated plant operating status and attribute information such as output from (2) are used to estimate the power generation volume. For example, if minute white smoke cannot be detected and the plant is marked Inactive, the power generation volume will be too low, and if the plant is marked Active but all of its associated units are not operating, the power generation volume will be too high. Therefore, a correction is made using, for example, "Active" / "Inactive" = 0.7 / 0.3 rather than 1 / 0.

[0039] (8) Changes in classification accuracy due to seasonal fluctuations, and parameters for accurately classifying actual production volume based on the classification results are obtained, and a production volume estimation calculation formula for any set is generated. For example, changes in classification accuracy due to seasonal fluctuations are corrected as follows. That is, since the classification accuracy of white plume is highest in December, January, and February, and the detection accuracy of white plume is lowest in July and August (the degree of moisture condensation differs due to the effects of temperature and humidity), if the production volumes in December, January, and February are taken as the standard (1x), the production volume in July and August is set to 1.5x, and the production volume from September to November and April to June is set to 1.3x.

[0040] (9) Calculate the production volume of the analysis target in any set. For example, aggregate the estimated power generation volume by area.

[0041] (Example of trained model configuration) Next, an example of the configuration of the trained model 16 (general-purpose trained model 161) will be described with reference to FIG. 6. The input to the trained model 16 shown in FIG. 6 is an array of m×n×3 elements for each RGB channel representing the areal space 511, etc. The data structure of one color image is made up of pixel values ​​of an m-row, n-column matrix according to the image size and resolution for each of the three RGB channels, which becomes the input to the convolutional neural network. In addition, the output side is the probability of being classified into each of the three classification numbers ("operating", "not operating", and "unknown") as the output of the softmax function at the final stage of the model, and the sum of the three is 1 (in the example shown in FIG. 6, the probability of each classification is "operating" = 0.78, "not operating" = 0.12, and "unknown" = 0.10).

[0042] (Example of operation of information processing device) Next, an example of the operation of the information processing device 1 during image acquisition processing will be described with reference to FIG. 7. In the image acquisition processing, the information processing device 1 first selects an image (step S111), then specifies an analysis target for the image (step S112), then assigns geographic information to the analysis target (step S113), then assigns attribute information to the analysis target (step S114), and stores the geographic / attribute information regarding the analysis target (step S115). The information processing device 1 repeatedly executes the processing of steps S111 to S115 until the selection is completed (until "Y" is obtained in step S116). On the other hand, when the selection is completed (when "Y" is obtained in step S116), the information processing device 1 acquires an image based on the geographic information (step S117) and stores the acquired image (step S118).

[0043] Next, an example of the operation of the information processing device 1 in the learning phase will be described with reference to FIG. 8. In the learning phase, the information processing device 1 first classifies images into learning images and inference images (step S121), then selects learning images (step S122), then displays the selected learning images on a predetermined display device (step S123), and then labels the learning images (step S124). The information processing device 1 repeatedly executes the processes of steps S122 to S124 until the selection is completed (until "Y" is obtained in step S125). On the other hand, when the selection is completed (when "Y" is obtained in step S125), the information processing device 1 performs learning using the labeled learning images (step S126) and stores them as a learned model (step S127).

[0044] Next, with reference to FIGS. 9 to 13, an operation example of the information processing apparatus 1 during the estimation phase will be described. In the estimation phase, the information processing apparatus 1 acquires an inference image (step S131), inputs the inference image into a learned model (step S132), and outputs (records) information regarding the operating state (step S133). The information processing apparatus 1 repeatedly executes the processes of steps S131 to S133 until the acquisition is completed (until it becomes "Y" in step S134). When the acquisition is completed (when it becomes "Y" in step S134), the information processing apparatus 1 calculates the power generation amount based on the information regarding the operating state and the attribute information (step S135), corrects the power generation amount based on the classification accuracy (step S136), aggregates the power generation amount for each area (step S137), and outputs the aggregation result (step S138).

[0045] FIGS. 10 and 11 show a specific example of the process of step S135. In this example, the process of step S135 is composed of the processes of steps S1351 to S1355 shown in FIGS. 10 and 11. In step S1351, the plant operating state is arranged in time series for an arbitrary object (hereinafter, an example of "object No. 1"). In step S1352, operation = 1, non-operation = 0, n / a (missing), and unknown = "latest 0 / 1" are assigned. In step S1353, using a constant k (0 < k < 1), k is assigned to 1 and 1 - k is assigned to 0. In step S1354, the total value of the outputs of the units associated with the object (1200 MW in the case of object No. 1) is multiplied. Then, in step S1355, the above process is similarly performed for objects No. 1 to N, and the estimated power generation amount that can be grasped from the object is obtained.

[0046] Also, FIG. 12 shows a specific example of the process of step S136 as step S1361. In step S1361, since the degree of the condensation phenomenon of the moisture contained in the exhaust gas varies depending on the outside air temperature and humidity, in order to correct the power generation amount based on the classification accuracy of the classifier (learned model), a parameter α that changes every season (month) is multiplied, and the estimated power generation amount (MWh) is calculated over 24 hours (however, the example in FIG. 12 is the value before multiplying by 24 hours).

[0047] 13 shows a specific example of the process of step S137 as step S1371. In step S1371, the estimated power generation amount calculated based on other unit information (area, country, fuel type, company name, etc.) associated with each object is totaled (FIG. 13 shows an example in which the estimated power generation amount is totaled for each area (prefecture)).

[0048] <Second embodiment> Next, an information processing device, an information processing method, and a program according to the second embodiment of the present disclosure will be described with reference to Figs. 14 to 20. Figs. 14 and 15 are schematic diagrams showing a configuration example of a trained model according to the second embodiment of the present disclosure. Fig. 14 shows the trained model 16 shown in Fig. 1 as trained model 16b, the general-purpose trained model 161 shown in Fig. 1 as trained model 161b, and the individual trained model 162 shown in Fig. 1 as trained model 162b. Fig. 15 shows the trained model 16 shown in Fig. 1 as trained model 16c, the general-purpose trained model 161 shown in Fig. 1 as trained model 161c, and the individual trained model 162 shown in Fig. 1 as trained model 162c. Figs. 16 to 18 are flowcharts showing an operation example of an information processing device according to the second embodiment of the present disclosure. Figs. 19 and 20 are schematic diagrams showing an example of an areal space according to the second embodiment of the present disclosure.

[0049] In the information processing device 1 according to the second embodiment, the position information acquisition unit 14 and the state estimation unit 15 may be omitted from the configuration shown in FIG.

[0050] The second embodiment improves the accuracy of estimating production volume compared to the first embodiment by performing learning and estimation taking into account meteorological information (temperature, humidity, etc.) in the vicinity of the analysis target (plant, chimney, etc.) (FIGS. 14 and 15).

[0051] In addition, the weighting matrix set in the convolutional neural network set in the classifier (trained models 161b and 161c) created in the same manner as in the first embodiment is used as the initial value of the weights of the classifier (trained models 162b and 162c), and when inferring images of each plant, three classifications of "operating", "non-operating", and "unknown" are assigned to the images acquired of the object in the plant, label information is prepared, and additional re-learning is performed to improve the classification accuracy.

[0052] When learning is performed as in the first embodiment, a plant (object) is not specified, but learning is performed across multiple types of objects. For example, if 100 images are acquired from each of 200 chimneys in Japan, the total number of images will be 20,000, but in the case of the first embodiment, an initial value is given to the parameter Wij using an arbitrary method based on this data, and one general-purpose trained model is generated using the data and labels.

[0053] On the other hand, in the second embodiment, in order to improve accuracy, the weights Wij of the trained models (trained models 161b and 161c) trained in the same manner as in the first embodiment are set as initial values, and the classifiers (trained models 162b and 162c) are trained again using the data of each object and its label. In this way, the number of classifiers is generated as many as the number of objects. For example, if there are 200 chimneys, 200 classifiers will be created, and each created classifier has parameters corresponding to each object, so that versatility is reduced while classification accuracy is improved. The classifier generated in the first embodiment is called a general-purpose classifier, and the classifier newly generated in the second embodiment is called an individual classifier. Note that, depending on the attribute information of the plant, the previous judgment result affects the current judgment result, so that the previous judgment result can be recursively incorporated into the classification algorithm (trained models 161c and 162c). Note that, in the trained models 161c and 162c shown in FIG. 15, multiple memories M are newly provided.

[0054] (Processing flow of the information processing device of the second embodiment) In the information processing device 1 of the second embodiment, the production volume of the equipment associated with the analysis target is calculated according to the following steps (1) to (9).

[0055] (1) to (3), (4), (7), and (9) are the same as those in the first embodiment. However, in the process of (3) in the second embodiment, weather information for the date and time when the image obtained from the geographic information in (3) in the first embodiment was captured is obtained (FIGS. 19 and 20). For the weather information, for example, known weather information for the location closest to the location of the analysis target (such as the closest city) is used.

[0056] (5) Learn using meteorological information and generate a classifier.

[0057] (6) Meteorological information and the image for inference are input to the classifier, and status information of the object to be analyzed is obtained as the classification result.

[0058] (8) Since meteorological information is used when generating the classifier, correction for seasonal changes, which was performed in the first embodiment, is not necessary.

[0059] (Example of operation of information processing device) Next, an example of the operation of the information processing device 1 during the image acquisition process will be described with reference to FIG. 16. In the image acquisition process, the information processing device 1 first selects an image (step S141), then specifies an analysis target for the image (step S142), then assigns geographic information to the analysis target (step S143), then assigns attribute information to the analysis target (step S144), and then stores the geographic / attribute information related to the analysis target (step S145). The information processing device 1 repeatedly executes the processes of steps S141 to S145 until the selection is completed (until "Y" is obtained in step S146). On the other hand, when the selection is completed (when "Y" is obtained in step S146), the information processing device 1 acquires an image based on the geographic information (step S147), acquires weather information for the date and time when the image was taken based on the geographic information (step S148), and stores the acquired weather information and image data (step S149).

[0060] Next, an example of the operation of the information processing device 1 in the learning phase will be described with reference to FIG. 17. In the learning phase, the information processing device 1 first classifies weather information and image data into learning and inference images (step S151), then selects learning images (step S152), then displays the selected learning images on a predetermined display device (step S153), and then labels the learning images (step S154). The information processing device 1 repeatedly executes the processes of steps S152 to S154 until the selection is completed (until "Y" is obtained in step S155). On the other hand, when the selection is completed (when "Y" is obtained in step S155), the information processing device 1 performs learning using the labeled learning images (step S156), stores them as a learned model (step S157), and re-learns for each analysis target (step S158).

[0061] Next, an example of the operation of the information processing device 1 in the estimation phase will be described with reference to Fig. 18. In the estimation phase, the information processing device 1 acquires an image for inference (step S161), inputs the image for inference to a trained model (step S162), and outputs (records) information about the operating state (step S163). The information processing device 1 repeatedly executes the processes of steps S161 to S163 until acquisition is completed (until "Y" is obtained in step S164). When acquisition is completed (when "Y" is obtained in step S164), the information processing device 1 calculates the amount of power generation based on the information about the operating state and the attribute information (step S165), counts the amount of power generation for each area (step S166), and outputs the counting result (step S167).

[0062] <Third embodiment> Next, an information processing device, an information processing method, and a program according to a third embodiment of the present disclosure will be described with reference to Fig. 21 to Fig. 25. Fig. 21 and Fig. 22 are flowcharts showing an example of an operation of the information processing device according to the third embodiment of the present disclosure. Fig. 23 to Fig. 25 are schematic diagrams showing an example of an area space according to the third embodiment of the present disclosure.

[0063] The third embodiment differs from the first and second embodiments in that it enables additional estimation of status information (non-operating state) as to whether the analysis target (plant, chimney, etc.) is shut down for example due to large-scale construction or for other reasons. As a result, there are cases where it is not clear whether exhaust gas is being generated or not just by the system determining whether or not exhaust gas is being generated. Since both shutdowns due to large-scale construction and shutdowns for other reasons correspond to cases where exhaust gas is not being generated, being able to recognize such status information proves that exhaust gas is not being generated, improving the value of the information.

[0064] (Processing flow of the information processing device of the third embodiment) In the information processing device 1 of the third embodiment, when the operating status of the equipment associated with the analysis target is non-operating in the following steps (1) to (7), the non-operating status (cause of non-operation) is estimated based on the location information of the mobile terminal 31.

[0065] (1) For the image obtained by photographing, geographic information is assigned to the areal space of the range in which the population is counted (area space 501, which is a specified region corresponding to areal space 511 shown in Figure 23), without being limited to the range in which the analysis target is to be identified.

[0066] (2) Based on geographic information, location information of each of multiple devices is obtained.

[0067] (3) The areal space of the image is divided into a plurality of meshes 5011 with a vertical and horizontal distance of k1 (FIGS. 24 and 25). If each device (mobile terminal 31) exists within any mesh 5011, the number of devices in that mesh is input, and if not, 0 is input. If the areal space 501 to be analyzed is not rectangular, the length is extended vertically and horizontally, zeros are inserted therein, and an array with l1 elements vertically and m1 elements horizontally is created.

[0068] (4) This sequence is classified into a learning sequence and an inference sequence. If at least a part of the image (area space 511) acquired in the first embodiment is included in the area space 501 specified in the third embodiment, matching is performed. However, it is made independent as a classifier.

[0069] (5) For the learning sequences, the label “large-scale construction in progress” is assigned to sequences undergoing major construction such as periodic inspections as status information for the analysis target, and the label “normal times” is assigned to sequences undergoing other construction periods.

[0070] (6) As in the second embodiment, a general classifier and an individual classifier are created using the labeled data, and the inference array is used to classify the plant status. (The relationship between the general classifier and the individual classifier is the same as that in the first embodiment, where the general classifier is the individual classifier generated as a result of re-learning using the images and labels of each object, with the parameter Wji known in the first embodiment as the initial value.)

[0071] (7) According to the third embodiment, the plant status information inferred by the first and second embodiments as "shutdown" is further classified as "under large-scale construction" or "normal operation."

[0072] (Example of operation of information processing device) Next, an example of the operation of the information processing device 1 during the location information acquisition process and the learning phase process will be described with reference to Fig. 21. During the location information acquisition and learning phase process, the information processing device 1 first acquires device location information based on the geographic information of the analysis target (step S171), divides the image into meshes (step S172), and determines whether or not a device exists in the mesh (step S173). If a device exists (if "Y" in step S173), the information processing device 1 sets the number of pieces of location information for the mesh. The information processing device 1 repeatedly executes the processes of steps S173 and S174 until the process is completed for all meshes of all images (until both steps S175 and S176 are "Y").

[0073] Next, the information processing device 1 classifies the sequences into learning sequences and inference sequences (step S177), selects a learning sequence (step S178), displays the selected learning sequence (step S179), and labels the learning sequence (step S180). Until the selection is completed (until "Y" is displayed in step S181), the information processing device 1 repeatedly executes the processes of steps S178 to S180.

[0074] Next, the information processing device 1 performs learning using the labeled learning sequence (step S182), stores it as a learned model (step S183), and generates an individual classifier (step S184).

[0075] Next, an example of the operation of the information processing device 1 in the process of the estimation phase will be described with reference to FIG. 22. In the process of the estimation phase, the information processing device 1 first acquires an inference array (step S191), then inputs the inference array to the trained model (step S192), and estimates the non-operating state (step S193). Until the acquisition is completed (until "Y" is obtained in step S194), the information processing device 1 repeatedly executes the processes of steps S191 to S193. Next, the information processing device 1 determines whether or not the plant has an estimated operating state (step S195), and if the plant has an estimated operating state (if "Y" in step S195), the information processing device 1 integrates the results (step S196) and outputs the tabulated results (step S197). On the other hand, if the plant has not an estimated operating state (if "N" in step S195), the information processing device 1 outputs the tabulated results (step S197).

[0076] <Fourth embodiment> Next, an information processing device, an information processing method, and a program according to a fourth embodiment of the present disclosure will be described with reference to Fig. 26 to Fig. 28. Fig. 26 and Fig. 27 are flowcharts showing an example of an operation of the information processing device according to the fourth embodiment of the present disclosure. Fig. 28 is a schematic diagram showing an example of an area space according to the fourth embodiment of the present disclosure.

[0077] The fourth embodiment corresponds to a case where the production amount is difficult to estimate depending on the analysis object (plant, chimney, etc.) in the first to third embodiments. For example, on a cloudy day, it is difficult to determine emissions (white smoke, gas, fog, etc.) from an image obtained by photographing, making it difficult to estimate the production amount. Therefore, in the fourth embodiment, by using an image photographed by an infrared probe, it becomes possible to handle a larger number of analysis objects for which the production amount is estimated.

[0078] (Processing flow of the information processing device of the fourth embodiment) In the information processing device 1 of the fourth embodiment, the production volume of the equipment associated with the analysis target is calculated according to the following steps (1) to (5).

[0079] (1) For any analysis target, a semicircle of radius r is drawn with the inlet / outlet ports at the center for the cooling water intake area (area space 515 in FIG. 28) and the warm wastewater discharge area (area space 514 in FIG. 28), and area spaces are specified for each, and geographic information is added. Furthermore, for any analysis target, area spaces 516 and 517 are specified for the exhaust gas outlet duct (chimney 601) and the boiler building, respectively, and geographic information is added.

[0080] (2) The attribute information of the analysis target is associated with the area space to which the geographical information is assigned, and stored as, for example, facility information 18 shown in Fig. 2. For example, the attribute information of the object is associated with the marked chimney and power plant based on the publicly available information on the Internet, external services, and appearance and dimensions, and stored in the attribute information database 181.

[0081] (3) Depending on the performance of the remote sensing device (thermal infrared image sensor), infrared sensor brightness information is obtained for the image acquired based on the above area space. (3-1) For the cooling water intake area and the warm wastewater discharge area, brightness information is obtained for each pixel of the inlet and outlet ports, and the difference between the average brightness value on the inlet side (area space 515) and each pixel on the outlet side (area space 514) is obtained. (3-2) For the exhaust gas outlet duct and the boiler building, luminance information of each infrared sensor is obtained for the images obtained based on the above-mentioned area spaces 516 and 517.

[0082] (4) Each difference value is multiplied by a constant k2 proportional to the distance from the exit port, and the values ​​are classified into a learning array and an inference array.

[0083] Reason for setting the constant k2: The farther away from the outlet port is, the lower the surface temperature of the seawater, rivers, and lakes, so data from a more distant location should be taken into account in determining the operating status even if it is a small change from the inlet temperature. A difference of 1°C at a distant location is more reliable in determining whether the device is operating than a difference of 1°C at a nearby location. In addition, for the exhaust gas outlet duct and the boiler building, the acquired brightness information is classified into a learning array and an inference array without multiplying it by the constant k2. When an image acquired in another embodiment is included in a plant designated in the fourth embodiment, the image is associated with the plant, but the image is made independent as a classifier.

[0084] (5) Using the luminance information classified into the learning array, status information is added and labeling is performed as in the first embodiment, and learning is performed using a learning model to generate a classifier. The relationship between the general-purpose classifier and the individual classifier is the same as the relationship in which the first embodiment is used as the general-purpose classifier, and the parameter Wji known in the first embodiment is used as the initial value, and the individual classifier is generated as a result of re-learning using the image and label of each object.

[0085] (Example of operation of information processing device) Next, an example of the operation of the information processing device 1 during data acquisition and learning phase processing will be described with reference to Fig. 26. The information processing device 1 first acquires luminance information from an image (thermal infrared image) (step S201), and if it is a predetermined area (if "Y" in step S203), acquires the difference in luminance information between areas (step S204), and corrects the luminance information (step S205). If it is not a predetermined area (if "N" in step S203), or if the luminance information has been corrected (step S205), the information processing device 1 determines whether or not processing of all images has been completed (step S206), and repeatedly executes the processing from step S203 to step S205 until it is completed (until "Y" in step S206).

[0086] Next, the information processing device 1 classifies the sequences into learning sequences and inference sequences (step S207), selects a learning sequence (step S208), displays the selected learning sequence (step S209), and labels the learning sequence (step S210). Until the selection is completed (until "Y" is displayed in step S211), the information processing device 1 repeatedly executes the processes from step S208 to step S210.

[0087] Next, the information processing device 1 performs learning using the labeled learning sequence (step S212), stores it as a learned model (step S213), and generates an individual classifier (step S214).

[0088] Next, an example of the operation of the information processing device 1 in the estimation phase will be described with reference to Fig. 27. In the estimation phase, the information processing device 1 acquires data for inference (step S221), inputs the data for inference to a trained model (step S222), and outputs (records) information about the operating state (step S223). The information processing device 1 repeatedly executes the processes of steps S221 to S223 until acquisition is completed (until "Y" is obtained in step S224). When acquisition is completed (when "Y" is obtained in step S224), the information processing device 1 calculates the amount of power generation based on the information about the operating state and the attribute information (step S225), counts the amount of power generation for each area (step S226), and outputs the counting result (step S227).

[0089] <Fifth embodiment> Next, an information processing device, an information processing method, and a program according to a fifth embodiment of the present disclosure will be described with reference to Fig. 29. Fig. 29 is a flowchart showing an operation example of the information processing device according to the fifth embodiment of the present disclosure.

[0090] In the fifth embodiment, in a case where the production volume and state information cannot be estimated in the first to fourth embodiments, the production volume can be estimated.

[0091] (Processing flow of the information processing device of the fifth embodiment) In the information processing device 1 of the fifth embodiment, the production volume of the equipment associated with the analysis target is calculated according to the following steps (1) to (4).

[0092] (1) Identify the system to which the analysis object (plant, chimney, etc.) whose production volume you want to estimate is subordinate, and make a list of the components of that system (including the analysis object). If the analysis object whose production volume you want to estimate is a power plant, the area under the jurisdiction of the central load dispatching center of a company or other entity responsible for controlling frequency and voltage is considered to be the "subordinate system."

[0093] The components that should be listed are the power generation facilities, large-scale demand facilities, substations, and transmission networks related to the supply and demand of electricity, and their attribute information should include information such as the type of power generation (thermal, wind, hydroelectric, etc.), output, heat rate, load change speed, time periods when power generation is possible, power generation constraints, plant shutdown information, contracted energy and voltage, subordinate substations, and transmission line distance.

[0094] (2) The listed components are prioritized for operation using attribute information, etc. For example, if there are 10 wind power generation facilities, 30 thermal power generation facilities, and 10 hydroelectric power generation facilities, a score is assigned in advance to prioritize each facility. The higher the number, the higher the priority, so all renewable energy sources are assigned a score of 100, thermal power generation supercritical units are assigned 60, sub-critical units are assigned 45, and so on, and the score setting is further varied according to the efficiency (heat rate) of the unit, etc.

[0095] (3) Based on the total demand in the system, the analysis targets are considered to be operating in descending order of score. For example, when estimating the amount of power generated by plants in the area under the jurisdiction of Hokkaido Electric Power Network Co., Ltd. on January 10, 2019, the power demand for that time period is first estimated based on weather conditions and past performance. Next, if there is a total power demand of, say, 4,000 MWh (publicly available information), power generation equipment is selected according to score (for example, in descending order of score) to achieve that required amount, and the equipment generating power in the system is identified, taking into account the time periods when power generation is possible and power generation constraints.

[0096] (4) Based on the identified equipment, use it as estimated information for the production volume of the subject of analysis.

[0097] (Example of operation of information processing device) Next, an example of the operation of the information processing device 1 will be described with reference to FIG.

[0098] In the operation example shown in Fig. 29, first, the information processing device 1 judges whether or not estimation based on images and position information is possible (step S231). If it is possible (if "Y" in step S231), the information processing device 1 executes the process of the learning phase in the same manner as in the first embodiment (step S232). On the other hand, if it is not possible (if "N" in step S231), the information processing device 1 identifies the system to which the analysis target is dependent (step S233), identifies the components of the dependent system (step S234), calculates the scores of the components (step S235), calculates the total demand (step S236), estimates the operating state (step S237), and estimates the production volume (step S238).

[0099] <Business flow> Next, a business flow (business model) according to each of the above embodiments will be described with reference to Fig. 30. Fig. 30 is a system diagram showing an example of a business flow according to an embodiment of the present disclosure.

[0100] The information processing device 1 according to each of the above embodiments is operated by a business management company 809 shown in FIG. 30. The information processing device 1 provides the estimated production amount (for example, power generation amount) as data 808 indicating a power generation amount trend to a mobile terminal 807 managed by a customer 805. When the power generation amount trend satisfies a condition set by the customer 805, for example, the mobile terminal 807 issues a transaction signal 806 indicating a favorable opportunity for a transaction such as an investment. In this case, the business management company 809 acquires detection information 803 such as satellite image data from a data supplying company 802, and also acquires arbitrary data 804 from the available data 801. In this case, a fee for the exchange of information flows from the customer 805 to the business management company 809 via a sales channel 810, and also flows from the business management company 809 to the data supplying company 802.

[0101] (Action and effect) According to the information processing device, the information processing method, and the program configured as described above, the operating state of the facility can be estimated.

[0102] (Other embodiments) Although the embodiments of the present disclosure have been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and design changes and the like that do not depart from the gist of the present disclosure are also included.

[0103] Computer Configuration FIG. 31 is a schematic block diagram illustrating a configuration of a computer according to at least one embodiment. The computer 90 comprises a processor 91, a main memory 92, a storage 93, and an interface 94. The information processing device 1 described above is implemented in a computer 90. The operations of the above-mentioned processing units are stored in a storage 93 in the form of a program. The processor 91 reads the program from the storage 93, loads it in a main memory 92, and executes the above-mentioned processes in accordance with the program. The processor 91 also secures storage areas in the main memory 92 corresponding to the above-mentioned storage units in accordance with the program.

[0104] The program may be for realizing a part of the functions to be performed by the computer 90. For example, the program may be for realizing the functions by combining with other programs already stored in the storage or by combining with other programs implemented in other devices. In another embodiment, the computer may include a custom LSI (Large Scale Integrated Circuit) such as a PLD (Programmable Logic Device) in addition to or instead of the above configuration. Examples of PLDs include PAL (Programmable Array Logic), GAL (Generic Array Logic), CPLD (Complex Programmable Logic Device), and FPGA (Field Programmable Gate Array). In this case, some or all of the functions to be realized by the processor may be realized by the integrated circuit.

[0105] Examples of the storage 93 include a hard disk drive (HDD), a solid state drive (SSD), a magnetic disk, a magneto-optical disk, a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), and a semiconductor memory. The storage 93 may be an internal medium directly connected to the bus of the computer 90, or an external medium connected to the computer 90 via an interface 94 or a communication line. In addition, when the program is distributed to the computer 90 via a communication line, the computer 90 that receives the program may load the program into the main memory 92 and execute the above-mentioned process. In at least one embodiment, the storage 93 is a non-transitory tangible storage medium.

[0106] <Additional Notes> The information processing device 1 described in each embodiment can be understood, for example, as follows.

[0107] (1) An information processing device 1 according to a first aspect includes an acquisition unit 11 that acquires detection information detected in the air including status information corresponding to the status of a predetermined emission material 41 in a predetermined area space 51 on the ground, and an operation status estimation unit 12 that estimates an operation status of equipment 71 associated with the area space 51 based on the detection information. According to this aspect and each of the following aspects, the operation status of the equipment can be estimated.

[0108] (2) The information processing device 1 according to the second aspect is the information processing device 1 of (1), wherein the equipment 71 is production equipment, and further includes a production volume estimation unit 13 that estimates the production volume of the production equipment based on the operating status.

[0109] (3) An information processing device 1 according to a third aspect is an information processing device 1 according to (1) or (2), wherein the operation state estimation unit 12 estimates the operation state using a trained model 16 that inputs the detection information and outputs a classification result that classifies the operation state into at least operating or non-operating.

[0110] (4) An information processing device 1 according to a fourth aspect is the information processing device 1 of (3), wherein the trained model 16 includes a model (individually trained model 162) that has been retrained for each areal space using, as initial values, the weighting coefficients Wij between neurons in each layer of a neural network trained using a plurality of pieces of detection information corresponding to a plurality of different areal spaces as teacher data.

[0111] (5) The information processing device 1 according to the fifth aspect is the information processing device 1 of (3) or (4), wherein the trained model 16 further inputs meteorological information corresponding to the areal space and outputs the classification result.

[0112] (6) The information processing device 1 according to a sixth aspect is the information processing device 1 according to any one of (3) to (5), in which the trained model 16 further inputs the previous classification result and outputs the classification result.

[0113] (7) The information processing device 1 according to the seventh aspect is an information processing device 1 according to any one of (1) to (6), and further includes a location information acquisition unit 14 that acquires location information of one or more mobile terminals 31 within a predetermined area (area space 501) corresponding to the area space 511, and a state estimation unit 15 that estimates a non-operating state when the operating state is non-operating based on the location information.

[0114] (8) The information processing device 1 according to an eighth aspect is the information processing device 1 according to any one of (1) to (7), in which the emission 41 is white smoke, and the detection information is color or monochrome image data.

[0115] (9) The information processing device 1 according to a ninth aspect is the information processing device 1 of (2), wherein the emission 41 is white smoke, the detection information is color or monochrome image data, and the production volume estimation unit 13 estimates the production volume by correcting it according to the season.

[0116] (10) The information processing device 1 according to a tenth aspect is the information processing device 1 according to any one of (1) to (7), in which the emission 41 is heat, and the detection information is thermal infrared image data.

[0117] (11) The information processing device 1 according to an eleventh aspect is the information processing device 1 of (2), in which the operational state estimation unit 13 estimates the operational state of the equipment based on a predetermined temperature difference based on the detection information. [Explanation of symbols]

[0118] 1. Information processing device 11 Acquisition Department 12 Operational state estimation unit 13 Production Estimation Department 14 Location information acquisition section 15 State Estimation Unit 16 Pre-trained models 31 Mobile Devices 41 Emissions 51 area space 71 Equipment 31 Mobile Devices 162 individually trained models

Claims

1. An acquisition unit that acquires detection information detected in the air, the detection information including status information corresponding to the status of a specified emission within a specified area space on the ground; an operation state estimation unit that estimates an operation state of a facility associated with the area space based on the detection information; Equipped with the operation state estimation unit estimates the operation state using a trained model that receives the detection information and outputs a classification result that classifies the operation state into at least operation or non-operation; The trained model includes a model that is retrained for each of the areal spaces using weighting coefficients between neurons in each layer of a neural network trained using a plurality of pieces of detection information corresponding to a plurality of different areal spaces as training data as initial values. Information processing device.

2. An acquisition unit that acquires detection information detected in the air, the detection information including status information corresponding to the status of a specified emission within a specified area space on the ground; an operation state estimation unit that estimates an operation state of a facility associated with the area space based on the detection information; Equipped with the operation state estimation unit estimates the operation state using a trained model that receives the detection information and outputs a classification result that classifies the operation state into at least operation or non-operation; The trained model further inputs meteorological information corresponding to the areal space and outputs the classification result. Information processing device.

3. The trained model further inputs meteorological information corresponding to the areal space and outputs the classification result. The information processing device according to claim 1 .

4. An acquisition unit that acquires detection information detected in the air, the detection information including status information corresponding to the status of a specified emission within a specified area space on the ground; an operation state estimation unit that estimates an operation state of a facility associated with the area space based on the detection information; Equipped with the operation state estimation unit estimates the operation state using a trained model that receives the detection information and outputs a classification result that classifies the operation state into at least operation or non-operation; The trained model is an information processing device that further inputs the previous classification result and outputs the classification result.

5. The information processing device according to claim 1 , wherein the trained model further inputs the previous classification result and outputs the classification result.

6. An acquisition unit that acquires detection information detected in the air, the detection information including status information corresponding to the status of a specified emission within a specified area space on the ground; an operation state estimation unit that estimates an operation state of a facility associated with the area space based on the detection information; a location information acquisition unit that acquires location information of one or more mobile devices within a predetermined area corresponding to the area space; a state estimation unit that estimates a non-operating state when the operating state is non-operating based on the position information; An information processing device comprising:

7. a location information acquisition unit that acquires location information of one or more mobile devices within a predetermined area corresponding to the area space; a state estimation unit that estimates a non-operating state when the operating state is non-operating based on the position information; The information processing device according to claim 1 , further comprising:

8. the emissions are white smoke, The detected information is color or monochrome image data. The information processing device according to claim 1 .

9. An acquisition unit that acquires detection information detected in the air, the detection information including status information corresponding to a status of a predetermined emission within a predetermined area space on the ground; an operation state estimation unit that estimates an operation state of a facility associated with the area space based on the detection information; a production volume estimation unit that estimates a production volume of the production facility based on the operation state; Equipped with the emissions are white smoke, the detection information is color or monochrome image data, The production amount estimation unit estimates the production amount by correcting it according to the season. Information processing device.

10. the exhaust is heat; The detection information is thermal infrared image data. The information processing device according to claim 1 .

11. The operation state estimation unit estimates an operation state of the equipment based on a predetermined temperature difference based on the detection information. The information processing device according to claim 10.

12. The facility is a production facility, a production volume estimation unit that estimates a production volume of the production facility based on the operating state; The information processing device according to claim 1 , further comprising:

13. An acquisition unit that acquires detection information detected in the air, the detection information including status information corresponding to a status of a predetermined emission within a predetermined area space on the ground; an operation state estimation unit that estimates an operation state of a facility associated with the area space based on the detection information; a production volume estimation unit that estimates a production volume of the production facility based on the operation state; Equipped with When the production facility is a power generation facility, if the operation state estimation unit cannot estimate the operation state based on the detection information, the production amount estimation unit estimates the production amount of the power generation facility based on a configuration of a system to which the power generation facility belongs. Information processing device.

14. acquiring airborne sensed detection information including condition information corresponding to a given emission condition within a given area space on the ground; estimating an operating state of a facility associated with the area space based on the detection information; Including, The step of estimating the operating state includes estimating the operating state using a trained model that inputs the detection information and outputs a classification result that classifies the operating state into at least operating or non-operating; The trained model includes a model that is retrained for each of the areal spaces using weighting coefficients between neurons in each layer of a neural network trained using a plurality of pieces of detection information corresponding to a plurality of different areal spaces as training data as initial values. Information processing methods.

15. acquiring airborne sensed detection information including condition information corresponding to a given emission condition within a given area space on the ground; estimating an operating state of a facility associated with the area space based on the detection information; A program for causing a computer to execute the following: The step of estimating the operating state includes estimating the operating state using a trained model that inputs the detection information and outputs a classification result that classifies the operating state into at least operating or non-operating; The trained model includes a model that is retrained for each of the areal spaces using weighting coefficients between neurons in each layer of a neural network trained using a plurality of pieces of detection information corresponding to a plurality of different areal spaces as training data as initial values. program.

Images