Information processing system, information processing method, and computer program

Abstract

One aspect of the disclosure concerns an information processing system comprising a processor configured to execute a program so as to acquire an optical property of an object, acquire information on an inclination of a surface of the object, and estimate a moisture content of the object based on the optical property and the information on the inclination. Another aspect of the disclosure concerns an information processing system comprising a processor configured execute a program so as to acquire an optical property of an object, acquire information on an inclination of a surface of the object, and estimate presence or absence of a chemical agent or a content of a chemical agent in the object based on the optical property and the information on the inclination.

Description

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM

[0001] CROSS REFERENCE TO RELATED APPLICATIONS The present application claims priority to Japanese Patent Application No. 2023-188845, filed November 2, 2023, the contents of which are incorporated herein by reference in their entirety. The present disclosure relates to an information processing system, an information processing method, and a program.

[0002] For materials such as iron ore and coal used as a steelmaking raw material or fuel for power generation, it is necessary to maintain the moisture content of the materials within an appropriate range. In the case where a material is too dry, powder dust will be generated, while in the case where the moisture content of the material is too much, it may clog during transportation or require a large amount of heat for drying, resulting in reduced energy efficiency.

[0003] Thus, it is necessary to understand the moisture content, or the like included in the material to maintain the moisture content within an appropriate range by spraying water in the case where the material is dry, or by spraying a chemical agent such as a water shielding agent in the case where the moisture content is too much.

[0004] Patent document 1 discloses a method of measuring the moisture content of the surface of a pile of iron ore by using a near-infrared moisture content meter to understand the moisture content included in the material and perform water spraying for preventing powder dust.

[0005] [Patent Document 1] JP2008-050076 A

[0006] However, the present inventors have found that, when estimating the moisture content, etc., of the surface of the pile as described above, the estimation is often influenced by the surrounding environment, and it is sometimes difficult to properly manage the numerical values.

[0007] The present disclosure has been made in view of the above circumstances and aims to properly estimate the moisture content, etc. of an object.

[0008] According to one aspect of the present disclosure, an information processing system is provided. The information processing system acquires an optical property of an object. Information on an inclination of a surface of the object is acquired. A moisture content of the object based on the optical property and the information on the inclination is estimated.

[0009] Aspects described in the following Appendixes may also be applied. (Appendix 1) An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on an inclination of a surface of the object; and estimate a moisture content of the object based on the optical property and the information on the inclination. (Appendix 2) The information processing system according to Appendix 1, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the inclination into a learned model learned using an optical property of an object and information on an inclination of a surface of the object as input data and a moisture content of an object as output data; and acquire a moisture content output from the learned model as a result of an estimation. (Appendix 3) An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on an inclination of a surface of the object; and estimate presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the inclination. (Appendix 4) The information processing system according to Appendix 3, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the inclination into a learned model learned using an optical property of an object and information on an inclination of a surface of the object as input data and presence or absence of a chemical agent or a content of a chemical agent contained in an object as output data; and acquire presence or absence of a chemical agent or a content of a chemical agent contained in the object, which is output from the learned model, as a result of an estimation. (Appendix 5) The information processing system according to any one of Appendixes 1 to 4, wherein: the information on the inclination is either or both an inclined surface azimuth of the object or / and an inclined surface angle of the object. (Appendix 6) The information processing system according to Appendix 5, wherein: the processor is further configured to execute the program so as to: acquire the information on the inclination from a sensor. (Appendix 7) An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on a position of a light source relative to the object; and estimate a moisture content of the object based on the optical property and the information on the position of the light source. (Appendix 8) The information processing system according to Appendix 7, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the position of the light source into a learned model learned using an optical property of an object and information on a position of a light source relative to the object as input data and a moisture content of an object as output data; and acquire a moisture content output from the learned model as a result of an estimation. (Appendix 9) An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on a position of a light source relative to the object; and estimate presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the position of the light source. (Appendix 10) The information processing system according to Appendix 9, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the position of the light source into a learned model learned using an optical property of an object and information on a position of a light source relative to the object as input data and presence or absence of a chemical agent or a content of a chemical agent contained in an object as output data; and acquire presence or absence of a chemical agent or a content of a chemical agent contained in an object, which is output from the learned model, as a result of an estimation. (Appendix 11) The information processing system according to any one of Appendixes 7 to 10, wherein: the information on the position of the light source is either or both information on an altitude of the light source relative to the object or / and information on an azimuth of the light source relative to the object. (Appendix 12) The information processing system according to any one of Appendixes 1 to 11, wherein: the processor is further configured to execute the program so as to output a result of an estimation. (Appendix 13) An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on an inclination of a surface of the object; and estimating a moisture content of the object based on the optical property and the information on the inclination. (Appendix 14) An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on an inclination of a surface of the object; and estimating presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the inclination. (Appendix 15) An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on a position of a light source relative to the object; and estimating a moisture content of the object based on the optical property and the information on the position of the light source. (Appendix 16) An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on a position of a light source relative to the object; and estimating presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the position of the light source. (Appendix 17) A program configured to allow a computer to function as the information processing system according to any one of Appendixes 1 to 12.

[0010] According to the above-mentioned aspects, it is possible to properly estimate the moisture content, etc. of an object.

[0011] FIG. 1 is a diagram showing an example of a system configuration of an information processing system 1000.FIG. 2 is a diagram showing an example of a hardware configuration of a Personal Computer (PC).FIG. 3 is an activity diagram showing an example of information processing in the Personal Computer (PC).FIG. 4 is an activity diagram showing an example of information processing in the Personal Computer (PC) of a modified example 1.FIG. 5 is a diagram showing an example of a system configuration of an information processing system of a modified example 2.FIG. 6 is an activity diagram showing an example of information processing in the Personal Computer (PC) of a modified example 3.FIG. 7 is an activity diagram showing an example of information processing in the Personal Computer (PC) of a modified example 4.

[0012] Hereinafter, an embodiment of the present disclosure will be described. Note that various features described in an embodiment 1 below (including the modified examples described later. The same applies hereafter.) can be combined with each other.

[0013] By the way, a program for realizing a software in the embodiment 1 may be provided as a non-transitory computer readable medium that can be read by a computer, may be provided for download from an external server, or may be provided in such a manner that the program can be activated on an external computer to realize functions thereof on a client terminal (so-called cloud computing).

[0014] In the embodiment 1, the "unit" may include, for instance, a combination of hardware resources implemented by a circuit in a broad sense and information processing of software that can be concretely realized by these hardware resources. In addition, various information is performed in the embodiment 1, and the information can be represented by, for instance, physical values of signal values representing voltage and current, high and low signal values as a set of binary bits consisting of 0 or 1, or quantum superposition (so-called qubits), and communication / calculation can be executed on a circuit in a broad sense.

[0015] Further, the circuit in a broad sense is a circuit realized by combining at least an appropriate number of a circuit, a circuitry, a processor, a memory, or the like. In other words, a circuit includes application specific integrated circuit (ASIC), programmable logic device (e.g., simple programmable logic device (SPLD), complex programmable logic device (CPLD), field programmable gate array (FPGA)), or the like.

[0016] 1. System Configuration FIG. 1 is a diagram showing an example of a system configuration of an information processing system 1000. As shown in FIG. 1, the information processing system 1000 includes a Personal Computer (PC) 100 and an aerial vehicle 110 as a system configuration. The PC 100 and the aerial vehicle 110 are communicatively connected via a network 150.

[0017] The PC 100 executes information processing described in the embodiment 1. The details of information processing in the PC 100 will be described with reference to an activity diagram and the like described below. The aerial vehicle 110 includes an imaging portion 120. The aerial vehicle 110 flies over a material 130 and captures an image of the material 130 with the imaging portion 120. The material 130 is an object for which the moisture content, etc. is to be estimated. The material 130 may be inorganic or organic. In the embodiment 1, the material 130 is described as a steelmaking raw material or fuel for power generation.

[0018] The "steelmaking raw material" means a material used as a raw material and fuel for steelmaking at a steelmaking facility such as a steelmaking plant, and examples thereof include coal, iron steel, dust, slag, coke, sinter as well as auxiliary raw materials such as limestone and dolomite. Furthermore, the "fuel for power generation" means fuel used as fuel for power generation at a power generation facility such as a power plant, and examples thereof include coal and biomass fuel. More typically, the material 130 is coal or iron ore. The "moisture content" in the specification may be an absolute mass or may be a concept including a moisture content per unit mass of a material, that is, the percentage of a moisture content.

[0019] The aerial vehicle 110 captures an image, etc. of the material 130 from the sky with the imaging portion 120. A more specific example of the imaging portion 120 is a hyperspectral camera or a multispectral camera. A multispectral camera is a camera that can observe wavelengths in the near-infrared band in addition to the visible light band. A hyperspectral camera is a camera that can observe a much larger number of bands than a multispectral camera and can acquire more detailed information. The aerial vehicle 110 transmits spectrum information acquired by the imaging portion 120 to the PC 100 via a network 150. A more specific example of the aerial vehicle 110 is a drone. In the embodiment 1, the spectrum information acquired by the imaging portion 120 of the aerial vehicle 110 is described as being transmitted to the PC 100 via the network 150. However, the information may be stored in a storage medium or the like. Then, the PC 100 into which the storage medium is inserted may read the spectrum information of the material 130 from the storage medium.

[0020] Here, the information processing system described in the claims may be configured with a plurality of devices or a single device. In the case where the information processing system described in the claims is configured with a single device, an example of such a device is the PC 100. In the case where the information processing system described in the claims is configured with a plurality of devices, examples thereof include the PC 100 and the aerial vehicle 110, or a cloud system, etc., which is configured with a plurality of servers that provides the functions of the PC 100.

[0021] 2. Hardware configuration (1) Hardware configuration of PC 100 FIG. 2 is a diagram showing an example of a hardware configuration of the PC 100. As shown in FIG. 2, the PC 100 includes a controller 210, a storage unit 220, an input unit 230, an output unit 240, and a communication unit 250 as a hardware configuration.

[0022] The controller 210 is a CPU (Central Processing Unit) or the like and controls the entire PC 100.

[0023] The storage unit 220 may be any of HDD (Hard Disk Drive), ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid Sate Drive), etc., or any combination thereof. The storage unit 220 stores the data (e.g., spectrum information transmitted from the aerial vehicle 110, a learned model to be described later, learning data for training a model, and data required for learning data) used when a program and the controller 210 execute processing based on a program. The storage unit 220 is an example of a storage medium. In the specification, the data used when the controller 210 executes processing based on the program is described as being stored in the storage unit 220, but the data may be stored in a storage unit or the like of another device that can communicate with the PC 100. In other words, the data may be stored in a storage unit of any device as long as the data can be referenced or used by the controller 210. The controller 210 executes processing based on the programs stored in the storage unit 220, thereby realizing the functions of the PC 100 and information processing in the activity diagrams shown in FIG. 3 to FIG. 6 described later.

[0024] The input unit 230 is a keyboard, a mouse, or the like, and inputs operation information such as a selection operation and an input operation by a user. The output unit 240 is a display or the like and displays information such as the results of processing by the controller 310.

[0025] The communication unit 260 connects the PC 100 to the network 150 and controls communication with other devices.

[0026] (2) Hardware configuration of aerial vehicle 110 The aerial vehicle 110 includes a controller, a storage unit, a communication unit, an imaging portion 120 and the like as a hardware configuration. The controller is a CPU or the like. The storage unit is a memory such as RAM or ROM. The controller executes processing based on the programs stored in the storage unit, thereby realizing the functions, etc. of the aerial vehicle 110. The communication unit connects the aerial vehicle 110 to the network 150 and controls communication with other devices. An example of the communication is wireless communication or the like. The imaging portion 120 is a hyperspectral camera, a multispectral camera or the like as described above, and acquires spectrum information, etc. of the material 130. For example, the imaging portion 120 irradiates the material 130 from a plurality of angles and acquires spectrum information for each of them. In addition, the imaging portion 120 may also irradiate polarized light onto the material 130 and acquires spectrum information. Note that, in addition to these hardware configurations, there are other hardware configurations essential to the aerial vehicle 110, such as a motor and propellers, but these configurations are omitted for the sake of simplicity of explanation.

[0027] 3. Information processing FIG. 3 is an activity diagram showing an example of information processing in the PC 100. It is assumed that the spectrum information of the material 130 captured by the imaging portion 120 of the aerial vehicle 110 is transmitted to the PC 100 and stored in the storage unit 220 together with the measured date and time information.

[0028] In Activity A300, the controller 210 acquires the spectrum information of the material 130 from the storage unit 220, etc., and determines the optical properties of the material 130 based on the acquired spectrum information. The optical properties include absorptance, reflectance, transmittance, scattering rate, refractive index and polarization rate. In the specification, the optical properties also include a value determined by the function of a difference between two optical properties of the same material 130 for light of two different wavelengths.

[0029] For example, in the case where the optical properties for light of wavelengths inm and jnm are Oiand Oj, respectively, the difference between the two optical properties is expressed as Oj- Oi, and the function of the difference is expressed as f (Oj- Oi). Note that Oiand Ojmay be identical optical properties or different optical properties. The "identical optical properties" refer to the case where, for example, both Oiand Ojare reflectance, and the "different optical properties" refers to the case where, for example, Oiis reflectance and Ojis absorptance.

[0030] Furthermore, the form of the function of a difference is not particularly limited as long as it is a function of Oj- Oi, and functions such as Oj- Oi,C (Oj- Oi), C / (Oj- Oi), COj-Oi, eOj-Oi, log (Oj- Oi) (in the equation, C is an arbitrary constant) can be used. Moreover, the function of a difference may additionally include Oiand Ojin the equation, and for example, the following Equations (1) to (3) may be used.

[0031] (Oj- Oi) / Oj... (1) (Oj- Oi) / Oi... (2) (Oj- Oi) / (Oj+ Oi) ... (3)

[0032] In one embodiment, a normalization difference spectral index (NDSI) may be used as the function of a difference. Specifically, when Riand Rjrepresent reflectance for light of wavelengths inm and jnm, respectively, the normalization difference spectral index is expressed as the following Equation (4).

[0033] NDSI = (Rj- Ri) / (Rj+ Ri) ... (4) Unless otherwise mentioned in the following specification, the optical properties of the material 130 refer to the NDSI value calculated using Equation (4).

[0034] The processing of Activity A300 is an example of processing of acquiring the optical properties of the material 130 that is an object.

[0035] In Activity A310, the controller 210 determines information on the inclination of the material 130 from the storage unit 220 or the like. Here, the controller 210 acquires information on the inclination of the material 130 from a sensor installed on a stand or the like on which the material 130 is placed. Then, the controller 210 stores the information in the storage unit 220 or the like together with the measured date and time information. An example of the sensor is a 3D gyro sensor or the like. However, this does not limit the sensor. Any sensor may be used as long as the inclination of the material 130 can be determined by directly installing the sensor on the material 130 and measuring the inclination thereof, or by measuring the inclination of a stand, etc. on which the material 130 is placed. Alternatively, the controller 210 may generate three-dimensional data from the captured image captured by the imaging portion 120 and measure the inclination of the generated three-dimensional data. The imaging portion 120 is also an example of the sensor. In addition, for example, an image of the material 130 is captured by another camera mounted on a drone or the like to acquire a captured image. Then, the controller 210 may determine the inclination of the material 130 from the three-dimensional data generated by using this captured image. The other camera in such a configuration is also an example of the sensor. The information on the inclination is either or both the inclined surface azimuth of the object (the material 130 in the example of the embodiment 1) or / and the inclined surface angle of the object. The inclined surface azimuth indicates the direction of the inclined surface in clockwise degrees, with the north being 0 degree, the east being 90 degrees, the south being 180 degrees, and the west being 270 degrees. The inclined surface angle indicates the inclination of the inclined surface, with the horizontal being 0 degree and the vertical being 90 degrees. Furthermore, information determined from either or both the inclined surface azimuth of the object or / and the inclined surface angle of the object may also be included in the information on the inclination. The processing of Activity A310 is an example of processing of acquiring information on the inclination of the surface of the material 130 that is an object. Note that, as long as the controller 210 can determine the inclined surface azimuth of the material 130 and the inclined surface angle of the material 130, etc. by image analysis (e.g., gloss) from a plurality of pieces of spectrum information (multispectral images) of the material 130 captured by the imaging portion 120, the inclined surface azimuth of the material 130 and the inclined surface angle of the material 130, etc. may be determined by such a method.

[0036] The order in which Activity A300 and Activity A310 are processed does not matter. Activity A300 may precede Activity A310, or Activity A300 may follow Activity A310. Activity A300 and Activity A310 may be executed simultaneously. However, the controller 210 acquires the optical properties and the information on the inclination which are measured at the same date and time based on the date and time information or the like.

[0037] In Activity A320, the controller 210 acquires a learned model from the storage unit 220 or the like and inputs the optical properties of the material 130 obtained in Activity A300 and the information on the inclination of the surface of the material 130 obtained in Activity A310 into the acquired learned model. Here, the learned model is a learned model learned using the optical properties of the material 130 and the information on the inclination of the material 130 as input data and the moisture content of the material 130 as output data. The controller 210 acquires the moisture content output from the learned model as an estimation value. The processing of Activity A320 is an example of processing of estimating the moisture content of the material 130 that is an object based on the optical properties and the information on the inclination. Performing such processing makes it possible to properly estimate the moisture content, etc. of the material 130 that is an object.

[0038] Note that the controller 210 uses a learned model to obtain the output data from the input data, but the controller 210 may also use a function indicating the relationship between the input data and the output data or a lookup table, etc. to obtain the output data from the input data. The same applies to the following modified examples, etc.

[0039] In Activity A330, the controller 210 outputs the acquired moisture content. The controller 210 outputs the moisture content by generating a screen including the moisture content and displaying it on the output unit 240, generating a file, etc. including the moisture content and storing it in the storage unit 220, or generating a file, etc. including the moisture content and transmitting it to another device via the communication unit 250. The processing of Activity A330 is an example of processing of outputting the result of the estimation.

[0040] According to the processing of the embodiment 1, it is possible to properly estimate the moisture content, etc., of the material 130 that is an object. Furthermore, the result of the estimation can be output to a screen or other device. Note that, as another example of the processing of FIG. 3 performed by the controller 210, for example, the following aspect can be given. For example, in the case where a mass of the material 130 is arranged in a predetermined range or over, the controller 210 may divide the range in which the material 130 exists into certain ranges (areas). Then, the controller 210 may execute the processing as shown in FIG. 3 for each range. For example, the controller 210 may acquire optical properties for each range and output the estimated moisture content for each range (area). The measurement range of the optical properties may be automatically determined by the controller 210 based on the size of the mass of the material 130 and may be instructed to the imaging portion 120, or may be determined based on the user's setting operation via the input unit 230, etc. When outputting the moisture content estimated for each range (area), the controller 210 outputs the range and the estimation value of the range in association with each other. The controller 210 may output the estimation value as characters or numerical values or may output the estimation value in a color corresponding to the estimation value. The controller 210 may also output any combination of these pieces of information. The output method of such an aspect is the same as that shown in FIG. 4, FIG. 6, and FIG. 7 below.

[0041] (Modified example 1) This section describes a modified example 1 of the embodiment 1. In the modified example 1, the points different from those in the embodiment 1 will be described. The modified example 1 is a partial modification of the processing, etc. of the embodiment 1, and is included in the embodiment 1, rather than being an embodiment different from the embodiment 1. FIG. 4 is an activity diagram showing an example of information processing in the PC 100 of the modified example 1. In FIG. 4, the same reference numerals are used for the same parts as those in the information processing in the activity diagram of FIG. 3.

[0042] In Activity A420, the controller 210 of the modified example 1 (hereinafter, simply referred to as the controller 210) acquires a learned model of the modified example 1 (hereinafter, simply referred to as the learned model) from the storage unit 220, etc., and then inputs the optical properties of the material 130 obtained in Activity A300 and the information on the inclination of the surface of the material 130 obtained in Activity A310 into the acquired learned model. Here, the learned model is a learned model learned using the optical properties of the material 130 and the information on the inclination of the material 130 as input data and the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 as output data. The controller 210 acquires the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 that is an object, which is output from the learned model, as an estimation value. The processing of Activity A420 is an example of processing of estimating the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 that is an object. Here, the "chemical agent" may be any known chemical agents that can be combined with the material 130. For example, in the case where the material 130 (object) is a steelmaking raw material or fuel for power generation, and the material 130 is piled up in a pile state, the chemical agent may be a dust-proof agent.

[0043] The dust-proof agent is not particularly limited but may be wax emulsion solution, resin emulsion solution, silicone oil, mineral oil, heavy oil, or the like. Wax emulsion is categorized to natural wax and synthesis wax as the raw material of wax. Among these, the natural wax is not particularly limited but may be, for example, animal-based wax (such as beeswax or spermaceti), plant-based wax (such as carnauba wax, rice wax, or candelilla wax), petroleum-based wax (such as paraffin wax or micro crystallin wax), or mineral-based wax (such as montan wax or ceresin wax). The synthesis wax is not particularly limited but may be, for example, polyethylene wax, modified natural wax, or hardened oil such as grease. Emulsion resin of the resin emulsion solution is not particularly limited but may be, for example, acrylic resin, acrylic copolymer resin, vinyl acetate-type resin, synthetic rubber, urethane resin, asphalt (emulsifier), acrylic styrene emulsion, styrene butadiene emulsion, ethylene acetic acid vinyl emulsion, or versatate acrylic acid. The silicone oil may be, for example, dimethyl silicone oil or organic functionalized silicone oil (with a functional group such as an amino group, an epoxy group, a mercapto group, a phenyl group, a long-chain alkyl group, or a hydrogen group). Note that the dust-proof agent made of such resin emulsion solution forms a hydrophobic film on the surface of a pile.

[0044] In Activity A430, the controller 210 outputs the acquired presence or absence of a chemical agent or the acquired content of a chemical agent contained in the material 130 that is an object. The controller 210 outputs the moisture content by generating a screen including the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 and displaying it on the output unit 240, generating a file, etc. including the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 and storing it in the storage unit 220, or generating a file, etc. including the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 and transmitting it to another device via the communication unit 250. The processing of Activity A430 is an example of processing of outputting the result of the estimation.

[0045] According to the processing of the modified example 1, it is possible to properly estimate the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 that is an object. In addition, the result of the estimation can be output to a screen or other device.

[0046] (Modified example 2) This section describes a modified example 2 of the embodiment 1. In the modified example 2, the points different from those in the embodiment 1 will be described. The modified example 2 is a partial modification of the processing, etc. of the embodiment 1, and is included in the embodiment 1, rather than being an embodiment different from the embodiment 1. FIG. 5 is a diagram showing an example of the system configuration of the information processing system 1000 of the modified example 2. The material 130 of the modified example 2 is placed on a conveyor belt and is moving. In such a configuration, an imaging portion 120 of the modified example 2 (hereinafter, simply referred to as the imaging portion 120) may be included in the information processing system 1000 in any manner as long as an image of the material 130 can be captured from above, without being provided on an aerial vehicle. The imaging portion 120 of the modified example 2 includes a controller, a memory, a communication unit and the like. The controller is a CPU or the like. The memory stores programs and data used when the controller executes processing. The communication unit connects the imaging portion 120 to the network 150.

[0047] In the case of the configuration shown in FIG. 5, the information processing system 1000 may be provided with a sensor 500 that measures the inclination of the conveyor belt. In the case where the sensor 500 that measures the inclination of the conveyor belt is provided in the information processing system 1000, the controller 210 of the modified example 2 may determine information on the inclination of the surface of the material 130 based on the information from the sensor 500. Examples of the sensor 500 include a LiDAR sensor, a 3D gyro sensor, and the like. However, this does not limit the sensor 500. Any sensor may be used as long as the inclination of the material 130 can be determined by directly measuring the inclination of the material 130, or by measuring the inclination of something (a conveyor belt in the example of the modified example 2) on which the material 130 is placed. Alternatively, three-dimensional data may be generated from the captured image captured by the imaging portion 120 to measure the inclination. In addition, for example, an image of the material 130 is captured by another camera mounted on a drone or the like to acquire a captured image. Then, the controller 210 may determine the inclination of the material 130 from the three-dimensional data generated by using this captured image. The other camera in such a configuration is also an example of a sensor.

[0048] The modified example 2 can also achieve the same effects as those of the embodiment 1 described above.

[0049] (Modified example 3) This section describes a modified example 3 of the embodiment 1. In the modified example 3, the points different from those in the embodiment 1 will be described. The modified example 3 is a partial modification of the processing, etc. of the embodiment 1, and is included in the embodiment 1, rather than being an embodiment different from the embodiment 1. FIG. 6 is an activity diagram showing an example of information processing in the PC 100 of the modified example 3. In FIG. 6, the same reference numerals are used for the same parts as those in the information processing in the activity diagram of FIG. 3.

[0050] In Activity A 610, the controller 210 of the modified example 3 (hereinafter, simply referred to as the controller 210) acquires information on the position of a light source relative to the material 130. An example of a light source will be described below using the sun. For example, the controller 210 acquires the date and time information and position information, etc. of the spectrum information included in the attribute information of the spectrum information used in Activity A300. Then, the controller 210 acquires information on the position of the sun at the position indicated by the position information and at the date and time indicated by the date and time information from an external system based on the position information and the date and time information. The information on the position of the sun is either or both the information on the altitude of the sun relative to the object or / and the information on the azimuth of the sun relative to the object. Furthermore, the information obtained from either or both the information on the altitude of the sun relative to the object or / and the information on the azimuth of the sun relative to the object may also be included in the information on the position of the sun. The processing of Activity A610 is an example of processing of acquiring information on the position of the light source relative to the material 130 that is an object.

[0051] Other examples of light sources include light bulbs, fluorescent lights, lamps, LEDs (Light Emitting Diode), etc. In the case where the light source is a light bulb, fluorescent light, a lamp, LED, etc., the controller 210 may acquire information on the position of a preset light bulb, fluorescent light, lamp, LED, etc. from a predetermined device, or the controller 210 may acquire information that is preset in a predetermined file, etc. stored in the storage unit 220, etc. In addition, in the case where the imaging portion 120 includes a light source such as a light bulb, a fluorescent light, a lamp, an LED, etc. in the image capturing range, the controller 210 may specify the information on the position of the light source from the light source included in the image captured by the imaging portion 120.

[0052] The order in which Activity A300 and Activity A610 are processed does not matter. Activity A300 may precede Activity A610, or Activity A300 may follow Activity A610. Activity A300 and Activity A610 may be executed simultaneously. However, the controller 210 acquires the optical properties and the information on the position of the light source which are measured at the same date and time based on the date and time information or the like.

[0053] In Activity A620, the controller 210 acquires a learned model of the modified example 3 (hereinafter, simply referred to as a learned model) from the storage unit 220 or the like and inputs the optical properties of the material 130 obtained in Activity A300 and the information on the position of the light source relative to the material 130 obtained in Activity A610 into the acquired learned model. Here, the learned model is a learned model learned using the optical properties of the material 130 and the information on the position of the light source relative to the material 130 as input data and the moisture content of the material 130 as output data. The controller 210 acquires the moisture content output from the learned model as an estimation value. The processing of Activity A620 is an example of processing of estimating the moisture content of the material 130 that is an object based on the optical properties and the information on the position of the light source. Performing such processing makes it possible to properly estimate the moisture content, etc. of the material 130 that is an object.

[0054] In Activity A630, the controller 210 outputs the acquired moisture content. The controller 210 outputs the moisture content by generating a screen including the moisture content and displaying it on the output unit 240, generating a file, etc. including the moisture content and storing it in the storage unit 220, or generating a file, etc. including the moisture content and transmitting it to another device via the communication unit 250.

[0055] The modified example 3 can also achieve the same effects as those of the embodiment 1 described above.

[0056] (Modified example 4) This section describes a modified example 4 of the embodiment 1. In the modified example 4, the points different from those in the embodiment 1 will be described. The modified example 4 is a partial modification of the processing, etc. of the embodiment 1, and is included in the embodiment 1, rather than being an embodiment different from the embodiment 1. FIG. 7 is an activity diagram showing an example of information processing in the PC 100 of the modified example 4. In FIG. 7, the same reference numerals are used for the same parts as those in the information processing in the activity diagram of FIG. 3.

[0057] In Activity A720, the controller 210 of the modified example 4 (hereinafter, simply referred to as the controller 210) acquires a learned model of the modified example 4 (hereinafter, simply referred to as a learned model) from the storage unit 220 or the like and inputs the optical properties of the material 130 obtained in Activity A300 and the information on the position of the light source relative to the material 130 obtained in Activity A610 into the acquired learned model. Here, the learned model is a learned model learned using the optical properties of the material 130 and the information on the position of the light source relative to the material 130 as input data, and the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 as output data. The controller 210 acquires the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 that is an object, which is output from the learned model, as an estimation value. The processing of Activity A720 is an example of processing of estimating the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 that is an object.

[0058] In Activity A730, the controller 210 outputs the acquired presence or absence of a chemical agent or the acquired content of a chemical agent contained in the material 130 that is an object. The controller 210 outputs the presence or absence of a chemical agent or the content of a chemical agent by generating a screen including the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 and displaying it on the output unit 240, generating a file, etc. including the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 and storing it in the storage unit 220, or generating a file, etc. including the presence or absence of a chemical agent or the content of a chemical agent contained in the material 130 and transmitting it to another device via the communication unit 250. The processing of Activity A730 is an example of processing of outputting the result of the estimation.

[0059] According to the processing of the modified example 4, it is possible to properly estimate the presence or absence of a chemical agent, or the content of a chemical agent contained in the material 130 that is an object. In addition, the result of the estimation can be output to a screen or other device.

[0060] (Modified example 5) This section describes a modified example 5 of the embodiment 1. In the modified example 5, the points different from those in the embodiment 1 will be described. The modified example 5 is a partial modification of the processing, etc. of the embodiment 1, and is included in the embodiment 1, rather than being an embodiment different from the embodiment 1 The above-mentioned embodiment 1 and the multiple modified examples may be implemented in any combination. For example, the embodiment 1 and the modified example 3 may be combined, or the modified example 1 and the modified example 4 may be combined. For example, in the case where the embodiment 1 and the modified example 3 is combined, the controller 210 of the modified example 5 (hereinafter, simply referred to as the controller 210) acquires a learned model from the storage unit 220 or the like and inputs the optical properties of the material 130 obtained in Activity A300, the information on the inclination of the surface of the material 130 obtained in Activity A310, and the information on the position of the light source relative to the material 130 obtained in Activity A610 into the acquired learned model. Here, the learned model is a learned model learned using the optical properties of the material 130, the information on the inclination of the surface of the material 130, and the information on the position of the light source relative to the material 130 as input data, and the moisture content of the material 130 as output data. The controller 210 acquires the moisture content output from the learned model as an estimation value.

[0061] As described above, the information on the inclination is either or both the inclined surface azimuth of the object (the material 130) or / and the inclined surface angle of the object. In addition, the information obtained from either or both the inclined surface azimuth of the object or / and the inclined surface angle of the object may also be included in the information on the inclination. The information on the position of the light source is either or both the information on the altitude of the light source relative to the object or / and the information on the azimuth of the light source relative to the object. In addition, the information obtained from either or both the information on the altitude of the light source relative to the object or / and the information on the azimuth of the light source relative to the object may also be included in the information on the position of the light source.

[0062] Here, an example of a light source will be described below using the sun. More preferably, the controller 210 determines a sun incidence angle relative to an object from an inclined surface azimuth of the object, an inclined surface angle of the object, the information on the altitude of the sun relative to the object, and the information on an azimuth of the sun relative to the object. Alternatively, a light source incidence angle relative to an object is determined from the information on the altitude of the light source relative to the object and the information on the azimuth of the light source relative to the object. Then, the controller 210 may learn a model using the sun incidence angle or the light source incidence angle and generate a learned model. That is, the controller 210 inputs the optical properties of the material 130 and the sun incidence angle or the light source incidence angle relative to the material 130 into the learned model. Here, the learned model is a learned model learned using the optical properties of the material 130 and the sun incidence angle or the light source incidence angle relative to the material 130 as input data, and the moisture content of the material 130 as output data. The controller 210 acquires the moisture content output from the learned model as an estimation value.

[0063] Furthermore, the controller 210 may acquire information relating to the control of the amount of light of the imaging portion 120 from the imaging portion 120 or the aerial vehicle 110, etc., use the information relating to the control of the amount of light to learn a model, and generate a learned model. Examples of the information relating to the control of the amount of light include the exposure time of the imaging portion 120 or the like. For the sake of simplicity of explanation, the following description will be given using the exposure time of the imaging portion 120 as information relating to the control of the amount of light. The controller 210 inputs the optical properties of the material 130, the sun incidence angle or the light source incidence angle relative to the material 130, and the exposure time into the learned model. Here, the learned model is a learned model learned using the optical properties of the material 130, the sun incidence angle or the light source incidence angle relative to the material 130, and the exposure time as input data, and the moisture content of the material 130 as output data. The controller 210 acquires the moisture content output from the learned model as an estimation value.

[0064] Furthermore, the controller 210 may acquire the amount of global solar radiation at the location where the material 130 is placed, learn a model using this amount of global solar radiation, and generate a learned model. For example, the controller 210 transmits the position information and the capturing date and time to a predetermined other system, and the controller 210 acquires the amount of global solar radiation at the position indicated by the position information and at the date and time indicated by the capturing date and time from the other system. The controller 210 inputs the optical properties of the material 130, the sun incidence angle or the light source incidence angle relative to the material 130, the exposure time, and the amount of global solar radiation into the learned model. Here, the learned model is a learned model learned using the optical properties of the material 130, the sun incidence angle or the light source incidence angle relative to the material 130, the exposure time, and the amount of global solar radiation as input data, and the moisture content of the material 130 as output data. The controller 210 acquires the moisture content output from the learned model as an estimation value.

[0065] The processing of the modified example 5 can also achieve the effects of the embodiment 1, etc. described above.

[0066] The above-described embodiments of the present disclosure are examples of the present invention, and various configurations other than the above-described configurations may be adopted. The present disclosure is not limited to the embodiments described above, and the present disclosure encompasses variations, improvements, etc. within the scope of the spirit of the present disclosure. Reference Signs List

[0067] 110 Aerial vehicle 120 Imaging portion 130 Material 150 Network 210 Controller 220 Storage unit 230 Input unit 240 Output unit 250 Communication unit 260 Communication unit 310 Controller 500 Sensor 1000 Information processing system

Claims

1. An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on an inclination of a surface of the object; and estimate a moisture content of the object based on the optical property and the information on the inclination.

2. The information processing system according to claim 1, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the inclination into a learned model learned using an optical property of an object and information on an inclination of a surface of the object as input data and a moisture content of an object as output data; and acquire a moisture content output from the learned model as a result of an estimation.

3. An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on an inclination of a surface of the object; and estimate presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the inclination.

4. The information processing system according to claim 3, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the inclination into a learned model learned using an optical property of an object and information on an inclination of a surface of the object as input data and presence or absence of a chemical agent or a content of a chemical agent contained in an object as output data; and acquire presence or absence of a chemical agent or a content of a chemical agent contained in the object, which is output from the learned model, as a result of an estimation.

5. The information processing system according to any one of claims 1 to 4, wherein: the information on the inclination is either or both an inclined surface azimuth of the object or / and an inclined surface angle of the object.

6. The information processing system according to claim 5, wherein: the processor is further configured to execute the program so as to: acquire the information on the inclination from a sensor.

7. An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on a position of a light source relative to the object; and estimate a moisture content of the object based on the optical property and the information on the position of the light source.

8. The information processing system according to claim 7, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the position of the light source into a learned model learned using an optical property of an object and information on a position of a light source relative to the object as input data and a moisture content of an object as output data; and acquire a moisture content output from the learned model as a result of an estimation.

9. An information processing system, comprising a processor configured to execute a program so as to: acquire an optical property of an object; acquire information on a position of a light source relative to the object; and estimate presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the position of the light source.

10. The information processing system according to claim 9, wherein: the processor is further configured to execute the program so as to: input the optical property and the information on the position of the light source into a learned model learned using an optical property of an object and information on a position of a light source relative to the object as input data and presence or absence of a chemical agent or a content of a chemical agent contained in an object as output data; and acquire presence or absence of a chemical agent or a content of a chemical agent contained in an object, which is output from the learned model, as a result of an estimation.

11. The information processing system according to any one of claims 7 to 10, wherein: the information on the position of the light source is either or both information on an altitude of the light source relative to the object or / and information on an azimuth of the light source relative to the object.

12. The information processing system according to any one of claims 1 to 11, wherein: the processor is further configured to execute the program so as to output a result of an estimation.

13. An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on an inclination of a surface of the object; and estimating a moisture content of the object based on the optical property and the information on the inclination.

14. An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on an inclination of a surface of the object; and estimating presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the inclination.

15. An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on a position of a light source relative to the object; and estimating a moisture content of the object based on the optical property and the information on the position of the light source.

16. An information processing method executed by an information processing system, comprising: acquiring an optical property of an object; acquiring information on a position of a light source relative to the object; and estimating presence or absence of a chemical agent or a content of a chemical agent contained in the object based on the optical property and the information on the position of the light source.

17. A program configured to allow a computer to function as the information processing system according to any one of claims 1 to claim 12.

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