WO2018073900A1

WO2018073900A1 - Computer system, object diagnosis method, and program

Info

Publication number: WO2018073900A1
Application number: PCT/JP2016/080872
Authority: WO
Inventors: 俊二菅谷
Original assignee: 株式会社オプティム
Priority date: 2016-10-18
Filing date: 2016-10-18
Publication date: 2018-04-26

Abstract

[Problem] To provide: a computer system which improves the measurement accuracy of the temperature of each region of an object; an object diagnosis method; and a program. [Solution] This computer system acquires a visible light image and an infrared image which are captured by a camera, identifies an area, in the visible light image, which corresponds to a prescribed region of an object imaged by the camera, identifies an area, in the infrared image, which corresponds to the identified area in the visible light image, and diagnoses the object on the basis of the temperature of the identified area in the infrared image.

Description

Computer system, object diagnostic method and program

The present invention relates to a computer system that performs imaging by imaging an object, an object diagnostic method, and a program.

In recent years, computer systems have been proposed that are capable of diagnosing faults in an object by taking an image of the object.

In such a computer system, a configuration for diagnosing an object by measuring the temperature of the object using a spectrometer or infrared thermography is disclosed (see Non-Patent Document 1).

However, in the configuration of Non-Patent Document 1, there is a temperature difference for each part of the object, and in order to accurately grasp the state of the object, the temperature of each part to be measured should be accurately measured. Is required. However, although it is possible to identify the outline and approximate part of an object from a thermographic image, it is difficult to specify the exact position of each part depending on the distance from the camera to the subject and other factors. As a result, there was a limit to improving the accuracy of the temperature measured for each part.

It is an object of the present invention to provide a computer system, an object diagnosis method, and a program that improve the temperature measurement accuracy of each target part.

The present invention provides the following solutions.

The present invention includes first acquisition means for acquiring a visible light image and an infrared image captured by a camera;
First image processing means for specifying a region corresponding to a predetermined part of an object imaged by the camera in the visible light image;
Second image processing means for identifying a region in the infrared image corresponding to the identified region in the visible light image;
Diagnostic means for diagnosing the object based on the temperature of the region in the identified infrared image;
A computer system is provided.

According to the present invention, the computer system acquires a visible light image and an infrared image captured by a camera, and in the visible light image, an area corresponding to a predetermined part of an object captured by the camera is obtained. The region in the infrared image corresponding to the identified region in the visible light image is identified, and the object is diagnosed based on the temperature of the region in the identified infrared image.

The present invention is a category of a computer system, but also in other categories such as an object diagnosis method and program, the same actions and effects corresponding to the category are exhibited.

According to the present invention, it is possible to provide a computer system, an object diagnosis method, and a program which improve the temperature measurement accuracy of each target part.

FIG. 1 is a diagram showing an outline of the object diagnostic system 1. FIG. 2 is an overall configuration diagram of the object diagnostic system 1. FIG. 3 is a functional block diagram of the computer 10. FIG. 4 is a flowchart showing object diagnosis processing executed by the computer 10. FIG. 5 is a flowchart illustrating object diagnosis processing executed by the computer 10. FIG. 6 is a diagram schematically showing an example of visible light image data acquired by the computer 10. FIG. 7 is a diagram schematically showing an example of infrared image data acquired by the computer 10. FIG. 8 is a diagram illustrating an example schematically showing a state in which the computer 10 specifies a predetermined part in a visible light image. FIG. 9 is a diagram schematically illustrating an example in which the computer 10 specifies a region in an infrared image. FIG. 10 is a diagram illustrating an example of a reference temperature database stored in the computer 10.

Hereinafter, the best mode for carrying out the present invention will be described with reference to the drawings. This is merely an example, and the technical scope of the present invention is not limited to this.

[Outline of Object Diagnosis System 1]
The outline of the present invention will be described with reference to FIG. FIG. 1 is a diagram for explaining an outline of an object diagnosis system 1 which is a preferred embodiment of the present invention. The object diagnosis system 1 includes a computer 10 and includes IoT devices such as computers, terminals, sensors, and robots, factory equipment such as factory pipes and piping, moving objects such as cars, airplanes, and buses, buildings, and houses. An image obtained by imaging an object such as a building such as a factory itself is acquired, and this object is diagnosed. In the following description, the object is described as being a computer.

The computer 10 is a computing device that is communicably connected to a visible light camera, an infrared camera, a sensor, an environment adjustment device, etc. (not shown). The object diagnosis system 1 obtains a visible light image from a visible light camera, obtains an infrared image from an infrared camera, and provides an environment for an object installation location such as illuminance, wind direction, wind speed, temperature, temperature, humidity, and atmospheric pressure from a sensor. Information is acquired and the instruction | indication which adjusts environmental information mentioned above is transmitted to illuminating devices, such as various lights, air conditioners, such as an air blower, and environmental control apparatuses, such as a watering apparatus.

First, the computer 10 acquires a visible light image and an infrared image captured by a camera (not shown) (step S01). The computer 10 acquires a visible light image such as a moving image or a still image of an object captured by the visible light camera. In addition, the computer 10 acquires an infrared image such as a moving image or a still image of an object captured by the infrared camera. The visible light camera and the infrared camera are installed side by side or in the vicinity, and the visible light camera and the infrared camera image the same object. That is, the visible light camera and the infrared camera image the same object from substantially the same imaging point.

The computer 10 specifies a visible light image region that is a region corresponding to a predetermined part of the object imaged by the camera in the visible light image (step S02). The computer 10 specifies, for example, a part of an object such as a housing, a display, and a keyboard, a preset part, and the like as the predetermined part of the object. For example, the computer 10 specifies an area corresponding to the predetermined part in the visible light image by image analysis. The computer 10 extracts a feature amount existing in the visible light image, and specifies a predetermined part based on the feature amount. In addition, the computer 10 extracts the color of the visible light image and identifies a predetermined part based on this color.

The computer 10 specifies an infrared image region that is a region in the infrared image corresponding to the region in the visible light image described above in the infrared image (step S03). The computer 10 compares the visible light image and the infrared image, and identifies the region that matches the visible light image region in the infrared image as the infrared image region. The computer 10 identifies an infrared image area at the same position as the visible light image area as the infrared image area.

The computer 10 diagnoses an object based on the temperature of the infrared image area (step S04). The computer 10 diagnoses whether or not a failure has occurred in the object, for example, by comparing the temperature of the infrared image region with the temperature at the time of the failure of the object.

The above is the outline of the object diagnosis system 1.

[System configuration of object diagnosis system 1]
The system configuration of the object diagnostic system 1 will be described with reference to FIG. FIG. 2 is a diagram showing a system configuration of the object diagnostic system 1 which is a preferred embodiment of the present invention. The object diagnosis system 1 includes a computer 10 and a public network (Internet network, third generation, fourth generation communication network, etc.) 5, acquires an image obtained by imaging an object, and diagnoses the object.

The object diagnosis system 1 includes a camera such as a visible light camera that captures a visible light image of an object and an infrared camera that captures an infrared image of the object, and an installation environment of the object such as illuminance, wind light, wind speed, temperature, temperature, humidity, and atmospheric pressure. Are connected to an environment adjustment device that adjusts an installation environment of an object such as an illumination device such as various lights, an illumination device such as various lights, an air conditioner such as a blower or an air conditioner, or a watering device. The computer 10 obtains various information from these and transmits various instructions.

The computer 10 is the above-described computing device having the functions described later.

[Description of each function]
Based on FIG. 3, the function of the object diagnostic system 1 is demonstrated. FIG. 3 is a functional block diagram of the computer 10.

The computer 10 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and the like as the control unit 11, and other devices (cameras, various sensors, environmental adjustment devices) as the communication unit 12. Etc.), for example, a WiFi (Wireless Fidelity) compatible device compliant with IEEE 802.11. The computer 10 also includes a data storage unit such as a hard disk, a semiconductor memory, a recording medium, or a memory card as the storage unit 13. Further, the computer 10 includes, as the processing unit 14, a device for executing various processes such as image processing and failure diagnosis.

In the computer 10, when the control unit 11 reads a predetermined program, the image data acquisition module 20, the environment information acquisition module 21, and the adjustment instruction transmission module 22 are realized in cooperation with the communication unit 12. Further, in the computer 10, the control unit 11 reads a predetermined program, thereby realizing the storage module 30 in cooperation with the storage unit 13. Further, in the computer 10, the control unit 11 reads a predetermined program, so that the visible light image analysis module 40, the infrared image analysis module 41, the temperature analysis module 42, the diagnosis module 43, and the environment cooperate with the processing unit 14. The adjustment module 44 is realized.

[Object diagnosis processing]
Based on FIG.4 and FIG.5, the object diagnostic process which the object diagnostic system 1 performs is demonstrated. 4 and 5 are flowcharts showing object diagnosis processing executed by the computer 10. Processing executed by each module described above will be described together with this processing.

First, the image data acquisition module 20 acquires image data of a visible light image and an infrared image of an object (step S10). In step S10, the image data acquisition module 20 acquires visible light image data that is a visible light image captured by the visible light camera and infrared image data that is an infrared image captured by the infrared camera. The image data acquisition module 20 acquires visible light image data and infrared image data at a predetermined time interval or at a plurality of time points such as a preset time. The visible light image data and the infrared image data acquired by the image data acquisition module 20 are captured from the same imaging viewpoint and are the same target data. In the following description, it is assumed that the computer 10 executes diagnosis of an object based on visible light image data and infrared image data at a predetermined time.

The visible light image data of the object acquired by the image data acquisition module 20 will be described with reference to FIG. FIG. 6 is a diagram schematically illustrating an example of visible light image data acquired by the image data acquisition module 20. The image data acquisition module 20 acquires the visible light image 100 indicated by the visible light image data. In this visible light image 100, an object 110 is shown. The visible light image 100 may include scenery other than the object 110, natural objects, artificial objects, and the like, which are omitted for the sake of simplicity. The visible light image 100 may include a plurality of objects 110 and objects different from the objects 110.

The infrared image data of the object acquired by the image data acquisition module 20 will be described with reference to FIG. FIG. 7 is a diagram schematically showing an example of infrared image data acquired by the image data acquisition module 20. The image data acquisition module 20 acquires an infrared image 200 indicated by the infrared image data. An object 210 is shown in the infrared image 200. In addition, the infrared image 200 may include scenery, natural objects, artifacts, and the like other than the object 210, but are omitted for the sake of simplicity. In the infrared image 200, each temperature is indicated by hatching for convenience. The infrared image 200 may include a plurality of objects 210 or an object different from the object 210.

The environment information acquisition module 21 acquires environment information indicating the installation environment of the object (step S11). In step S11, the environment information acquisition module 21 acquires illuminance, wind light, wind speed, temperature, temperature, humidity, atmospheric pressure, and the like as environment information. The environment information acquisition module 21 acquires environment information from various sensors (not shown) such as an illuminance sensor, a wind direction / wind speed sensor, a temperature sensor, a humidity sensor, and a pressure sensor. The environment information acquisition module 21 acquires the visible light image data and the infrared image data at the same time as the acquisition timing. These various sensors are installed in the vicinity of the object or in the vicinity of the place where the object is installed.

The various sensors may be sensors that detect environmental information other than the examples described above. Moreover, the installation position of various sensors is not limited to the above-described example, and can be appropriately changed to a position where the installation environment of the object can be detected. Further, the process of step S11 may be omitted. In this case, what is necessary is just to perform the process of step S12 mentioned later after performing the process of step S10 mentioned above.

The visible light image analysis module 40 performs image analysis on the acquired visible light image data (step S12). In step S 12, the visible light image analysis module 40 extracts the feature amount and color of the visible light image data, and identifies an object existing in the visible light image data. In step S12, for example, the visible light image analysis module 40 compares the feature amount extracted from the visible light image data with the feature amount of the object stored in advance in the storage module 30, and extracts an object having a matching feature amount. The extracted object is identified as existing in the visible light image data. Further, for example, the visible light image analysis module 40 compares the RGB value extracted from the visible light image data with the RGB value of the object stored in advance in the storage module 30, and extracts an object having a matching or similar RGB value. The extracted object is identified as existing in the visible light image data.

The visible light image analysis module 40 determines whether there are a plurality of individuals in the visible light image data as a result of the image analysis (step S13). In step S13, the visible light image analysis module 40 determines whether there are a plurality of individuals by determining whether there are a plurality of objects in the visible light image data. The visible light image analysis module 40 determines whether there are a plurality of individuals of one object, whether there are individuals of a plurality of types of objects, and the like.

In step S13, when the visible light image analysis module 40 determines that there are not a plurality of individuals (step S13: NO), that is, when it is determined that only one individual is present in the visible light image data, visible light image analysis is performed. The module 40 identifies areas corresponding to a plurality of predetermined parts of one individual (step S14). In step S14, the predetermined part is, for example, a part of an object such as a housing, a display, or a keyboard, a part that is set in advance, or the like. The visible light image analysis module 40 specifies areas corresponding to, for example, a housing, a display, and a keyboard. The visible light image analysis module 40 extracts the casing, display, and keyboard present in the visible light image data from the feature amount, and specifies the extracted location as an area corresponding to a predetermined part. At this time, the visible light image analysis module 40 specifies a plurality of regions corresponding to each of the plurality of predetermined parts. The visible light image analysis module 40 extracts a housing, a display, and a keyboard that are present in the visible light image data from the RGB values, and specifies the extracted location as an area corresponding to a predetermined part.

Based on FIG. 8, the region corresponding to the predetermined part specified by the visible light image analysis module 40 will be described. FIG. 8 is a diagram illustrating an example schematically showing a state in which the visible light image analysis module 40 specifies a predetermined part. In FIG. 8, the visible light image analysis module 40 specifies a region in the visible light image 100 where a predetermined part such as a housing, a display, or a keyboard is located based on the feature amount and the color. In other words, the visible light image analysis module 40 identifies the region of the object 110 corresponding to the parts of the casings 300 to 302, the displays 310 to 312 and the keyboards 320 to 322. In FIG. 8, the specified area is indicated by hatching for convenience. This area indicates a part of each part, but may indicate the entire corresponding part. Note that the number, type, and position of the parts to be specified can be changed as appropriate.

On the other hand, in step S13, the visible light image analysis module 40 determines that there are a plurality of individuals (YES in step S13), that is, a plurality of individuals such as a first individual, a second individual, and a third individual. When the visible light image data is determined to be present in the visible light image data, the visible light image analysis module 40 identifies each of the plurality of individuals (step S15). In the following description, it will be assumed that the first individual and the second individual exist in the visible light image data.

The visible light image analysis module 40 identifies the positional relationship of each of the plurality of individuals (step S16). In step S 16, the visible light image analysis module 40 identifies the positional relationship between the first individual and the second individual based on the position in the visible light image. The visible light image analysis module 40 specifies a relative position or an absolute position between the first individual and the second individual. The positional relationship is, for example, which is closer to the shooting point, coordinates in the visible light image, or the like. Note that the processing in step S16 is not limited to the positional relationship between the first individual and the second individual, but may be the positional relationship with other individuals.

The visible light image analysis module 40 specifies a region corresponding to a predetermined part for each of the plurality of individuals (step S17). The process of step S17 performs the process of step S14 mentioned above with respect to each object which exists in visible light image data.

The infrared image analysis module 41 identifies a region in the infrared image corresponding to the region in the identified visible light image (step S18). In step S 18, the infrared image analysis module 41 compares the visible light image data with the infrared image data to identify the region of the infrared image data corresponding to the identified region of the object. The infrared image analysis module 41 acquires the position of the region in the visible light image as a coordinate, and specifies the position that matches the acquired coordinate as the region in the infrared image corresponding to the region in the identified visible light image.

Based on FIG. 9, the region in the infrared image corresponding to the region in the visible light image specified by the infrared image analysis module 41 will be described. FIG. 9 is a diagram illustrating an example schematically showing a state in which the infrared image analysis module 41 specifies a region in the infrared image. 9, in the visible light image 100 described above, regions in the infrared image 200 corresponding to the parts of the casings 300 to 302, the displays 310 to 312 and the keyboards 320 to 322 of the identified object 110 are identified. This is specified by comparing the position in the visible light image 100 and the position in the infrared image 200. The infrared image analysis module 41 acquires the position coordinates of each part in the visible light image 100, and specifies the position in the infrared image corresponding to the acquired position coordinates as the region in the infrared image corresponding to the region in the visible light image. . The infrared image analysis module 41 specifies the parts of the casings 400 to 402, the displays 410 to 412, and the keyboards 420 to 422 of the object 210. In FIG. 9, the specified area is indicated by hatching for convenience. This area refers to a part or the whole part depending on the part specified in the above-described visible light image. Note that the number of positions to be specified and their positions can be changed as appropriate in accordance with the visible light image.

The temperature analysis module 42 analyzes the temperature of the area in the specified infrared image data (step S19). In step S19, the temperature analysis module 42 acquires the temperature of each region based on the infrared image data.

The temperature analysis module 42 acquires a plurality of reference temperatures corresponding to each of a plurality of parts of the object from the reference temperature database stored in the storage module 30 (step S20). In step S20, the storage module 30 stores a plurality of reference temperatures corresponding to the respective parts in advance, and the temperature analysis module 42 acquires the stored reference temperatures. At this time, the reference temperature of the part corresponding to the region in the specified infrared image data is acquired.

[Reference temperature database]
A reference temperature database stored in the storage module 30 will be described with reference to FIG. FIG. 10 is a diagram illustrating an example of a reference temperature database stored in the storage module 30. In FIG. 10, the storage module 30 stores the name of the part and the reference temperature of the part in association with each other. That is, the storage module 30 stores “housing” and “40” in association with each other, “display” and “30” in association with each other, and “keyboard” and “20” in association with each other. The storage module 30 stores this reference temperature database for each type of object. Note that the storage module 30 may store a reference temperature database for each object, not for each object type. In this case, the storage module 30 may acquire a reference temperature for each part of each individual in advance and store the part and the reference temperature in association with each other.

The diagnosis module 43 diagnoses an object based on the temperature of the region in the specified infrared image (step S21). In step S 21, the diagnosis module 43 performs object diagnosis on the acquired temperature, the reference temperature, the temperature of another individual different from the one individual, the positional relationship between the first individual and the second individual, the environmental information It is executed by any one or a plurality of combinations.

The case where the diagnosis module 43 executes object diagnosis based on the acquired temperature will be described. The diagnosis module 43 determines whether or not the temperature of the region in the identified infrared image is an abnormal value, and determines that no failure has occurred if the temperature is not an abnormal value. On the other hand, if the diagnosis module 43 determines that the value is an abnormal value, it determines that a failure has occurred.

The case where the diagnosis module 43 executes object diagnosis based on the reference temperature will be described. The diagnosis module 43 compares the temperature of the region in the identified infrared image with the reference temperature stored in the acquired storage module 30, and calculates the temperature difference between the temperature of the region and the reference temperature. The diagnosis module 43 determines whether or not the calculated temperature difference is within a predetermined range (for example, within 0.5 ° C, within 1 ° C, within 2 ° C, etc.). Is determined not to occur. On the other hand, the diagnostic module 43 determines that a failure has occurred when the calculated temperature difference is not within the predetermined range.

A case will be described in which the diagnosis module 43 executes object diagnosis based on one individual and the temperature of another individual different from the one individual. The diagnostic module 43 compares the temperature of the region in the infrared image of the acquired one individual with the temperature of the corresponding region of the same part in the infrared image of another individual different from the one individual. Calculate the temperature difference. The diagnosis module 43 determines whether or not the calculated temperature difference is within a predetermined range (for example, within 0.5 ° C, within 1 ° C, within 2 ° C, etc.). Is determined not to occur. On the other hand, the diagnostic module 43 determines that a failure has occurred when the calculated temperature difference is not within the predetermined range. In this case, the diagnostic module 43 compares the reference temperature described above with one individual or another individual, and calculates a temperature difference between the one individual and the other individual, thereby It may be determined whether or not the above has occurred. In other words, the diagnosis module 43 may determine whether a failure has occurred in one or both of the one individual and the other individual based on the reference temperature and the temperature difference.

The case where the diagnosis module 43 executes diagnosis of an object based on the positional relationship between the first individual and the second individual will be described. The diagnosis module 43 compares the positional relationship between the position of the first individual and the second individual different from the first individual among the plurality of acquired individuals, and any one of them is an air conditioner, illumination, etc. Determine if you are more affected by This specifies whether either the first individual or the second individual is lower or higher in temperature due to the influence of an air conditioner, illumination, or the like. The diagnosis module 43 corrects the temperatures of the first and second individuals by acquiring environmental information such as air temperature and illuminance on the influence of the air conditioner and lighting. The diagnosis module 43 compares the corrected temperatures of the first individual and the second individual with the reference temperature described above, and calculates the temperature difference between them. The diagnosis module 43 determines whether or not the temperature difference is within a predetermined range (for example, within 0.5 ° C., within 1 ° C., within 2 ° C., etc.). Judge that it does not occur. On the other hand, if the diagnostic module 43 determines that the calculated temperature difference is not within the predetermined range, it determines that a failure has occurred. The diagnosis module 43 may determine whether or not a failure has occurred without using the reference temperature. For example, it may be determined whether or not a failure has occurred based on whether or not the corrected temperature of the first individual and the second individual is a predetermined temperature.

The case where the diagnosis module 43 executes diagnosis of an object based on environmental information acquired from a sensor will be described. The diagnosis module 43 corrects the acquired individual temperature based on the environmental information. For example, the diagnosis module 43 acquires humidity, air temperature, atmospheric pressure, and the like as environment information, and corrects the acquired individual temperature based on the acquired environment information. The diagnosis module 43 diagnoses an object based on the corrected individual temperature. The diagnosis module 43 may determine whether or not a failure has occurred in the object based on the corrected individual temperature and the reference temperature.

The diagnosis module 43 outputs a diagnosis result (step S22). In step S22, the diagnosis module 43 outputs the content of the failure (for example, the name of the failure, a countermeasure method, etc.) as the diagnosis result. When only one individual exists in the visible light image data and the infrared image data, the diagnosis module 43 outputs the diagnosis result of the one individual. Further, when there are a plurality of individuals in the visible light image data and the infrared image data, the diagnosis module 43 outputs a diagnosis result for each individual. At this time, the diagnosis module 43 outputs a diagnosis result together with various information capable of uniquely specifying the individual such as the name, identifier, and position information of each individual.

In the above description, the diagnosis module 43 performs diagnosis of an object using one visible light image data and one infrared image data, but a plurality of visible light image data acquired within a predetermined period. The diagnosis of the object may be executed based on the infrared image data. In this case, the diagnosis of the object may be executed based on the change amount, change width, and change itself of the individual temperature acquired from each infrared image data. The diagnosis module 43 may perform diagnosis of this object based on the average value of the individual temperatures acquired from the plurality of infrared image data. For example, the diagnosis module 43 calculates a temperature difference between the average value of the individual and the reference temperature by comparing the average value of the temperature of the individual with the reference temperature, and the temperature difference is a predetermined value. The diagnosis of the object may be executed based on whether or not it is within the range.

The diagnosis module 43 determines whether a failure has occurred in the individual based on the output diagnosis result (step S23).

In step S23, when the diagnosis module 43 determines that no failure has occurred in the individual (NO in step S23), the process ends. At this time, the diagnosis module 43 may transmit a notification to the effect that no failure has occurred in the individual to an external terminal device (not shown).

In step S23, when the diagnosis module 43 determines that a failure has occurred in the individual (YES in step S23), the environment adjustment module 44 adjusts the installation environment based on information representing the result of the diagnosis. An instruction is created (step S24). In step S 24, the environment adjustment module 44 acquires necessary processing based on the diagnosed failure content based on an adjustment database that stores the failure content and the processing in association with each other. The environment adjustment module 44 creates an adjustment instruction for executing the acquired processing. This adjustment instruction includes necessary processing and information that can uniquely identify the environmental adjustment device such as an identifier or device ID of the environmental adjustment device that executes this processing.

The adjustment instruction transmission module 22 transmits the adjustment instruction created by the environment adjustment module 44 in step S24 described above to the environment adjustment apparatus (step S25). The adjustment instruction transmission module 22 transmits the environmental adjustment device included in the adjustment instruction to the target environmental adjustment device based on information that can uniquely identify the environmental adjustment device.

The environment adjustment device receives this adjustment instruction and executes necessary processing included in this adjustment instruction. For example, the environment adjustment device executes lighting on / off, humidity and temperature control, watering, medicine spraying, and the like.

The above is the object diagnosis process.

In the above-described embodiment, the object is described as being a computer. However, the object is not limited to this example, and may be another article. For example, in addition to mobile phones, personal digital assistants, tablet terminals, electronic products such as netbook terminals, slate terminals, electronic book terminals, portable music players, wearable terminals such as smart glasses and head mounted displays, various sensors, IoT (Internet of Things) equipment such as a robot may be used. Also, factory equipment such as factory pipes, piping equipment, drainage equipment, power receiving / transforming equipment, power transmission equipment, pump equipment, fire fighting equipment, boiler equipment, high pressure gas equipment, high pressure air equipment may be used. Furthermore, it may be a moving body such as a vehicle, an airplane, a ship, a bus, a house, a hospital, a clinic, a station, an airport, a building, a government office, a police station, a fire station, a police box, a stadium, a stadium, a hotel. It may be a building such as a warehouse, a school, a public toilet, a store or a restaurant.

The means and functions described above are realized by a computer (including a CPU, an information processing apparatus, and various terminals) reading and executing a predetermined program. The program is provided, for example, in a form (SaaS: Software as a Service) provided from a computer via a network. The program is provided in a form recorded on a computer-readable recording medium such as a flexible disk, CD (CD-ROM, etc.), DVD (DVD-ROM, DVD-RAM, etc.). In this case, the computer reads the program from the recording medium, transfers it to the internal storage device or the external storage device, stores it, and executes it. The program may be recorded in advance in a storage device (recording medium) such as a magnetic disk, an optical disk, or a magneto-optical disk, and provided from the storage device to a computer via a communication line.

As mentioned above, although embodiment of this invention was described, this invention is not limited to these embodiment mentioned above. The effects described in the embodiments of the present invention are only the most preferable effects resulting from the present invention, and the effects of the present invention are limited to those described in the embodiments of the present invention. is not.

1. Object diagnosis system, 10 computers

Claims

First acquisition means for acquiring a visible light image and an infrared image captured by a camera;
First image processing means for specifying a region corresponding to a predetermined part of an object imaged by the camera in the visible light image;
Second image processing means for identifying a region in the infrared image corresponding to the identified region in the visible light image;
Diagnostic means for diagnosing the object based on the temperature of the region in the identified infrared image;
A computer system comprising:
Storage means for storing a plurality of reference temperatures corresponding to each of a plurality of locations of the object;
Further comprising
The first image processing means identifies a plurality of regions corresponding to each of a plurality of predetermined parts,
The diagnostic means compares temperatures in a plurality of regions specified by the second image processing means with each reference temperature,
The computer system according to claim 1.
The first image processing means identifies each of a plurality of individuals imaged by the camera,
The diagnosis means outputs a diagnosis result for each individual;
The computer system according to claim 1.
The diagnostic means uses the temperature of another individual when performing diagnosis on one of the plurality of individuals.
The computer system according to claim 3.
The first image processing means identifies a positional relationship between the first individual and the second individual,
The diagnostic means uses the positional relationship.
The computer system according to claim 3.
Second acquisition means for acquiring environment information indicating an installation environment of the object;
Further comprising
The diagnostic means uses the environmental information.
The computer system according to claim 1.
The first acquisition means acquires the visible light image and the infrared image at a plurality of time points,
The diagnostic means uses a plurality of the visible light images and a plurality of the infrared images acquired within a predetermined period.
The computer system according to claim 1.
Adjusting means for adjusting the installation environment based on information representing the result of diagnosis output by the diagnostic means;
The computer system according to claim 1, further comprising:
The object is an IoT device or a factory facility.
The computer system according to claim 1.
Acquiring a visible light image and an infrared image captured by a camera;
In the visible light image, specifying a region corresponding to a predetermined part of the object imaged by the camera;
Identifying a region in the infrared image corresponding to the identified region in the visible light image;
Diagnosing the object based on the temperature of the region in the identified infrared image;
An object diagnostic method comprising:
Computer system,
Acquiring a visible light image and an infrared image captured by a camera;
Identifying a region corresponding to a predetermined part of an object imaged by the camera in the visible light image;
Identifying a region in the infrared image corresponding to the identified region in the visible light image;
Diagnosing the object based on the temperature of the region in the identified infrared image;
A program for running