WO2020184535A1 - IoT APPARATUS CREDIBILITY CALCULATION DEVICE, IoT APPARATUS CREDIBILITY CALCULATION SYSTEM, METHOD OF USING IoT APPARATUS CREDIBILITY CALCULATION DEVICE, IoT APPARATUS CREDIBILITY CALCULATION METHOD, RECORDING MEDIUM, IoT DATA PLATFORM, AND IoT DATA PROCESSING METHOD BY IoT DATA PLATFORM - Google Patents

IoT APPARATUS CREDIBILITY CALCULATION DEVICE, IoT APPARATUS CREDIBILITY CALCULATION SYSTEM, METHOD OF USING IoT APPARATUS CREDIBILITY CALCULATION DEVICE, IoT APPARATUS CREDIBILITY CALCULATION METHOD, RECORDING MEDIUM, IoT DATA PLATFORM, AND IoT DATA PROCESSING METHOD BY IoT DATA PLATFORM Download PDF

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WO2020184535A1
WO2020184535A1 PCT/JP2020/010160 JP2020010160W WO2020184535A1 WO 2020184535 A1 WO2020184535 A1 WO 2020184535A1 JP 2020010160 W JP2020010160 W JP 2020010160W WO 2020184535 A1 WO2020184535 A1 WO 2020184535A1
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iot device
iot
data
information
unit
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PCT/JP2020/010160
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French (fr)
Japanese (ja)
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高橋　進
雄樹 小手川
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凸版印刷株式会社
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C15/00—Arrangements characterised by the use of multiplexing for the transmission of a plurality of signals over a common path
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/50—Safety; Security of things, users, data or systems

Definitions

  • the present invention relates to an IoT device credit calculation device, an IoT device credit calculation system, a method of using the IoT device credit calculation device, an IoT device credit calculation method, a storage medium, an IoT data platform, and a method of processing IoT data by the IoT data platform. ..
  • the present application claims priority based on Japanese Patent Application No. 2019-044917 filed in Japan on March 12, 2019, the contents of which are incorporated herein by reference.
  • the IoT Internet of Things
  • IoT Devices Internet of Things
  • terminal information leakage spoofing
  • eavesdropping eavesdropping
  • Spoofing cites guidance as a threat to major IoT devices, and explains countermeasures for it.
  • these are mainly measures against malicious third parties intervening via the network, and measures are not given when there is a problem with the IoT device itself, the owner or administrator of the IoT device, etc.
  • Open IoT is toward the ultimate IoT where multiple stakeholders are connected in various ways by meshing data distribution, multi-layering services, and virtualization between various layers of IoT regardless of domain or business owner. It shows that it is expanding.
  • the data user side and the data provider side are not vertically integrated, and the data user side is sufficient for the owner and manager of the IoT device on the data provider side, as well as the installation status of the IoT device. In some cases, it is not possible to determine the quality of the data transmitted from the IoT device.
  • FIG. 17 is a diagram showing an example of the current problems.
  • the IoT device manager maliciously sends fake data
  • the IoT device is not installed correctly
  • the IoT device is not properly calibrated
  • incorrect data is transmitted.
  • the administrator acts fraudulently after installing the device, the device is not properly maintained and the measured value shows an abnormal value, and the credibility of the information is damaged later, the data user It was difficult to grasp the creditworthiness of the data.
  • Patent Document 1 management devices and environmental sensing systems that ensure the reliability of environmental information detected by sensor devices have been conventionally known.
  • the sensor device transmits the first environmental information and the second environmental information to the management device, and the management device transmits the first environmental information and the second environmental information transmitted by the sensor device. Receives the environmental information of 2.
  • the management device executes control that limits the use of the first environmental information transmitted from the sensor device when it is determined that the environment around the sensor device has changed based on the second environmental information. ..
  • Patent Document 1 describes that the administrator confirms the environmental sensor (sensor device), in the technology described in Patent Document 1, which is the administrator of the environmental sensor (sensor device)?
  • Whether or not the environmental information detected by the sensor device is reliable is determined without considering whether or not the person is such a person. Further, in the technique described in Patent Document 1, attributes of the sensor device such as whether or not the sensor device is equipped with equipment for ensuring the reliability of the environmental information detected by the sensor device are taken into consideration. It is determined whether or not the environmental information detected by the sensor device is reliable. Therefore, depending on the technique described in Patent Document 1, there is a possibility that it is not possible to accurately determine whether or not the environmental information detected by the sensor device is reliable.
  • the present invention relates to an IoT device credit calculation device, an IoT device credit calculation system, and a method of using an IoT device credit calculation device that can calculate the credit rating of measurement data transmitted from an IoT device with high accuracy.
  • An object of the present invention is to provide a method for calculating IoT device creditworthiness and a storage medium.
  • Another object of the present invention is to provide an IoT data platform capable of improving the convenience of using IoT data, and a method of processing IoT data by the IoT data platform.
  • One aspect of the present invention is a first acquisition unit that acquires measurement data transmitted over time from an IoT device having a sensor, and non-measurement information that is information other than the measurement data transmitted from the IoT device.
  • the measurement transmitted from the IoT device based on the non-measurement information acquisition unit, the measurement data acquired by the first acquisition unit, and the non-measurement information acquired by the non-measurement information acquisition unit.
  • the non-measurement information includes owner information which is information on the owner and / or manager of the IoT device and attributes which are information on attributes of the IoT device.
  • the non-measurement information acquisition unit includes information, and includes a second acquisition unit for acquiring the owner information and a third acquisition unit for acquiring the attribute information, and the credit rating calculation unit includes the first. It was transmitted from the IoT device based on the measurement data acquired by the acquisition unit, the owner information acquired by the second acquisition unit, and the attribute information acquired by the third acquisition unit. The credit rating of the measurement data may be calculated.
  • the measurement data analysis unit that executes the analysis of the measurement data acquired by the first acquisition unit and the owner information acquired by the second acquisition unit are used. Based on the owner analysis unit that executes the analysis of the owner and / or administrator of the IoT device, and the attribute information acquired by the third acquisition unit, the attribute analysis of the IoT device is executed.
  • the IoT device attribute analysis unit is further provided, and the credit rating calculation unit is based on the analysis result of the measurement data analysis unit, the analysis result of the owner analysis unit, and the analysis result of the IoT device attribute analysis unit.
  • the credit rating of the measurement data transmitted from the IoT device may be calculated.
  • the measurement data analysis unit includes the measurement data acquired by the first acquisition unit and the past measurement data acquired in the past by the first acquisition unit.
  • the measurement data acquired by the first acquisition unit may be compared with the data transmitted from the IoT device other than the IoT device.
  • the measurement data analysis unit may perform analysis of the presence or absence of traces of artificial operations on the measurement data acquired by the first acquisition unit.
  • the measurement data analysis unit executes analysis of the measurement data by performing a data mining process after graphicizing the characteristics of changes in the measurement data. May be good.
  • the owner analysis unit uses the declaration contents of the owner and / or manager of the IoT device, past history, information obtained from other databases, and Based on at least one of the information obtained from the findings, analyze whether the owner and / or administrator of the IoT device is the legitimate owner and / or administrator of the IoT device. May be good.
  • the owner analysis unit determines whether or not the owner and / or manager of the IoT device transmits information about the IoT device, and owns the IoT device.
  • information on the credit of the owner and / or the administrator of the IoT device, and the owner of the IoT device and / Or an analysis of the owner and / or manager of the IoT device may be performed based on at least one of the information about the manager's management system.
  • the IoT device attribute analysis unit determines the manufacturer of the IoT device, the manufacturing time of the IoT device, and whether or not the calibration and / or maintenance of the IoT device has been executed.
  • the IoT device was calibrated and / or maintained, the content of the IoT device calibration and / or maintenance, the response of the IoT device, whether or not the IoT device is equipped with a secure chip, the IoT device.
  • Operation status authentication information about the IoT device, installation location of the IoT device, installation environment and / or measurement environment of the IoT device, difficulty of measuring the measurement data by the sensor of the IoT device, depending on the IoT device.
  • Analysis of the attributes of the IoT device may be performed based on at least one of the transmitted communication path of the measured data and the level of encryption in the IoT device.
  • the IoT device includes a monitoring unit that monitors the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and / or the open / closed state of the case of the IoT device. Further, the third acquisition unit acquires information on the power consumption of the IoT device monitored by the monitoring unit, the temperature of the calculation unit of the IoT device, and / or the open / closed state of the case of the IoT device.
  • the IoT device attribute analysis unit is based on the power consumption of the IoT device acquired by the third acquisition unit, the temperature of the calculation unit of the IoT device, and / or the open / closed state information of the case of the IoT device. An analysis of device attributes may be performed.
  • the credit rating calculation unit calculates the credit rating of the measurement data transmitted from the IoT device
  • the credit rating calculation unit is continuously performed by the measurement data analysis unit. Based on the results of the analysis performed on the IoT device, the results of the analysis continuously performed by the owner analysis unit, and the results of the analysis continuously performed by the IoT device attribute analysis unit. The creditworthiness of the measurement data transmitted from may be continuously calculated.
  • the credit rating calculation unit may calculate the credit score as the credit rating of the measurement data transmitted from the IoT device by using artificial intelligence.
  • One aspect of the present invention is an IoT device credit calculation system including an IoT device credit calculation device, the IoT device, and a network, wherein the IoT device credit calculation device owns, manages, and manufactures the IoT device. Owned or managed by a person who does neither, the IoT device transmits the measurement data to a location other than the first acquisition unit of the IoT device credit calculation device and the IoT device credit calculation device via the network. It is an IoT device credit rating calculation system that is configured to automatically make calls.
  • One aspect of the present invention is a method of using the IoT device credit rating device, in which the credit rating of the measurement data calculated by the credit rating calculation unit is used for issuing a certificate of the IoT device. This is how to use the calculation device.
  • One aspect of the present invention is a method of using the IoT device credit rating device, in which the credit rating of the measurement data calculated by the credit rating calculation unit is the rating of the owner and / or manager of the IoT device, and /.
  • it is a method of using the IoT device credit rating calculation device used for rating the installation area of the IoT device.
  • One aspect of the present invention is a first acquisition step of acquiring measurement data transmitted over time from an IoT device having a sensor, and acquisition of non-measurement information which is information other than the measurement data transmitted from the IoT device.
  • the measurement transmitted from the IoT device based on the non-measurement information acquisition step, the measurement data acquired in the first acquisition step, and the non-measurement information acquired in the non-measurement information acquisition step.
  • One aspect of the present invention is a first acquisition step of acquiring measurement data transmitted over time from an IoT device having a sensor on a computer, and non-measurement which is information other than the measurement data transmitted from the IoT device. It is transmitted from the IoT device based on the non-measurement information acquisition step for acquiring information, the measurement data acquired in the first acquisition step, and the non-measurement information acquired in the non-measurement information acquisition step.
  • a storage medium in which a program for executing a credit rating calculation step for calculating the credit rating of the measured data is recorded and can be read by the computer.
  • One aspect of the present invention is a first method of calculating a first consideration, which is a basis for consideration for the IoT data receiving unit that receives IoT data transmitted from the IoT device and the IoT data received by the IoT data receiving unit.
  • the consideration calculation unit, the IoT data transmission unit that transmits the IoT data received by the IoT data reception unit, and the second consideration that is the basis of the consideration for the IoT data transmitted by the IoT data transmission unit are calculated.
  • a second consideration calculation unit and a credit information receiving unit for receiving information on the credit rating of the IoT data received by the IoT data receiving unit are provided, and the first consideration calculation unit is received by the credit information receiving unit.
  • the first consideration is calculated based on the information on the creditworthiness of the IoT data
  • the second consideration calculation unit calculates the second consideration based on the information on the creditworthiness of the IoT data received by the creditworthiness information receiving unit.
  • the IoT data receiving unit is from the IoT device in a state where the information of the IoT data utilization unit that uses the IoT data transmitted by the IoT data transmitting unit is concealed.
  • the IoT data is received from the IoT data possessing unit that possesses the transmitted IoT data, and the IoT data transmitting unit conceals the information of the IoT data possessing unit to the IoT data utilization unit.
  • the IoT data may be transmitted.
  • One aspect of the present invention is the first consideration, which is the basis for the IoT data receiving step in which the IoT data platform receives the IoT data transmitted from the IoT device, and the consideration for the IoT data received in the IoT data receiving step.
  • the second consideration calculation step for calculating the two consideration and the credit rating information receiving step for receiving the information regarding the credit rating of the IoT data received in the IoT data receiving step are provided.
  • the credit rating information is provided.
  • the first consideration is calculated based on the information on the creditworthiness of the IoT data received in the receiving step
  • the second consideration calculation step is based on the information on the creditworthiness of the IoT data received in the creditworthiness information receiving step. This is a method of processing IoT data by the IoT data platform, which calculates the second consideration.
  • an IoT device credit calculation device an IoT device credit calculation system, a method of using the IoT device credit calculation device, and an IoT device credit calculation that can calculate the credit rating of measurement data transmitted from an IoT device with high accuracy.
  • Methods and storage media can be provided. Further, according to the present invention, it is possible to provide an IoT data platform that can improve the convenience of using IoT data, and a method of processing IoT data by the IoT data platform.
  • FIG. 1 It is a figure which shows an example of the schematic structure of the IoT device credit rating calculation device of 1st Embodiment. It is a flowchart for demonstrating an example of the process executed in the IoT apparatus credit degree calculation apparatus of 1st Embodiment. It is a figure which shows an example of the process in the IoT device credit degree calculation apparatus when the measurement data analysis unit shown in FIG. 1 executes the data mining process, and the credit degree calculation unit uses artificial intelligence and the like. It is a figure which shows an example of the data mining processing executed by the measurement data analysis unit. It is a figure which shows an example of the neural network which executes a data mining process.
  • FIG. 1 is a diagram showing an example of a schematic configuration of the IoT device credit rating calculation device 1 of the first embodiment.
  • FIG. 1 (A) shows an example of a schematic configuration of the IoT device credit rating device 1 of the first embodiment
  • FIG. 1 (B) shows the IoT device credit rating calculation device 1 and IoT of the first embodiment.
  • An example of the relationship with the device D is shown.
  • the IoT device credit rating device 1 calculates the credit rating of the data measured by the sensor D1 of the IoT device D.
  • the IoT device D includes a sensor D1, a monitoring unit D2, a check unit D3, and a storage unit D4.
  • the sensor D1 measures data such as meteorological data such as water level, ground, temperature, humidity, wind speed, and atmospheric pressure.
  • the monitoring unit D2 monitors, for example, the power consumption of the IoT device D, the temperature of the calculation unit of the IoT device D, the open / closed state of the case of the IoT device D, and the like.
  • the check unit D3 checks whether or not the IoT device D has been modified in terms of software and / or hardware, the history of the IoT device D, the log data of the IoT device D, and the like.
  • the storage unit D4 stores the owner information which is the information of the owner and / or the administrator of the IoT device D and the attribute information which is the information about the attribute of the IoT device D.
  • the owner and / or manager of the IoT device D includes not only an individual but also an organization and the like.
  • the attributes of the IoT device D include, for example, the degree of secure capability (secure level) of the IoT device D, whether or not the device (IoT device D) is authenticated, and the like.
  • the IoT device D includes a monitoring unit D2, a check unit D3, and a storage unit D4, but in another example, the IoT device D is a monitoring unit D2, a check unit D3, and a storage unit. It does not have to have at least one of D4.
  • the IoT device credit rating calculation device 1 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13, a measurement data analysis unit 14, and an owner analysis unit 15. It includes an IoT device attribute analysis unit 16, a credit rating calculation unit 17, and a storage unit 18.
  • the first acquisition unit 11 acquires the measurement data of the sensor D1 transmitted over time from the IoT device D.
  • the second acquisition unit 12 acquires the above-mentioned owner information from the IoT device D. Specifically, the second acquisition unit 12 acquires the owner information stored in the storage unit D4 of the IoT device D.
  • the second acquisition unit 12 functions as a non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device D.
  • the second acquisition unit 12 has, for example, the contents of the declaration of the owner and / or administrator of the IoT device D, the past history, the information obtained from other databases (not shown), the information obtained from the survey results, and the like. Is acquired as owner information. Further, in the second acquisition unit 12, for example, whether or not the owner and / or administrator of the IoT device D has transmitted information regarding the IoT device D, the owner and / or the administrator of the IoT device D has in the past.
  • the third acquisition unit 13 acquires the above-mentioned attribute information from the IoT device D. Specifically, the third acquisition unit 13 acquires the attribute information stored in the storage unit D4 of the IoT device D.
  • the third acquisition unit 13 also functions as a non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device D.
  • the third acquisition unit 13 is, for example, the manufacturer of the IoT device D, the manufacturing time of the IoT device D, whether or not the IoT device D has been calibrated and / or maintained, and the IoT device D has been calibrated and / or maintained.
  • IoT device D Time, IoT device D calibration and / or maintenance details, IoT device response, whether IoT device D is equipped with a secure chip (not shown), IoT device operating status, IoT device D certification Information, installation location of IoT device D, installation environment and / or measurement environment of IoT device D, difficulty of measuring data by sensor D1 of IoT device D, communication path of measurement data transmitted by IoT device D, IoT device The level of encryption in D is acquired as attribute information.
  • the third acquisition unit 13 obtains, for example, the power consumption of the IoT device D monitored by the monitoring unit D2 of the IoT device D, the temperature of the calculation unit of the IoT device D, information on the open / closed state of the case of the IoT device D, and the like. , Get as attribute information. Further, the third acquisition unit 13 determines, for example, whether or not the IoT device D checked by the check unit D3 of the IoT device D has been modified in software and / or hardware, the history of the IoT device D, and the history of the IoT device D. Acquire log data etc. as attribute information. That is, the attribute information acquired by the third acquisition unit 13 includes information obtained by the sensor (detection information) and information that can be obtained without the need to provide a sensor (non-detection information).
  • the IoT device credit rating device 1 includes both the second acquisition section 12 and the third acquisition section 13 as the non-measurement information acquisition section, but in another example, the IoT device credit rating calculation The device 1 may include only one of the second acquisition unit 12 and the third acquisition unit 13 as the non-measurement information acquisition unit. In yet another example, the owner information described above may be treated as being included in the attribute information. In this example, the IoT device credit rating calculation device 1 includes a third acquisition unit 13 as a non-measurement information acquisition unit (that is, does not include a second acquisition unit 12), and the third acquisition unit 13 is The owner information included in the attribute information is also acquired.
  • the measurement data analysis unit 14 analyzes the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11.
  • the measurement data analysis unit 14 may, for example, measure the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the past measurement data (sensor of the IoT device D) acquired in the past by the first acquisition unit 11. A comparison with the data previously measured by D1) is performed. Further, the measurement data analysis unit 14 has, for example, the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the data transmitted from the IoT device other than the IoT device D (IoT other than the IoT device D). Perform a comparison with the data measured by the sensor of the instrument.
  • the measurement data analysis unit 14 analyzes, for example, the presence or absence of traces of artificial operation on the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. Further, for example, as will be described later, the measurement data analysis unit 14 performs a data mining process after graphicizing the characteristics of changes in the measurement data of the sensor D1 of the IoT device D, so that the sensor D1 of the IoT device D Perform an analysis of the measurement data.
  • the owner analysis unit 15 analyzes the owner and / or manager of the IoT device D based on the owner information of the IoT device D acquired from the IoT device D by the second acquisition unit 12.
  • the owner analysis unit 15 is based on, for example, the contents of the declaration of the owner and / or manager of the IoT device D, the past history, the information obtained from other databases, the information obtained from the survey results, and the like. Analyze whether the owner and / or administrator of device D is the authorized owner and / or administrator of IoT device D.
  • the owner analysis unit 15 for example, whether or not the owner and / or the manager of the IoT device D has transmitted information about the IoT device D, the owner and / or the manager of the IoT device D has in the past.
  • the content of the transmitted information about IoT device D, the management status of other IoT devices owned or managed by the owner and / or administrator of IoT device D, and the owner and / or administrator of IoT device D are other Based on information on the credit of the owner and / or manager of IoT device D, information on the management system of the owner and / or manager of IoT device D, etc., whether or not the disposal of IoT device is properly executed. And perform an analysis of the owner and / or administrator of the IoT device D.
  • the IoT device attribute analysis unit 16 analyzes the attributes of the IoT device D based on the attribute information (information about the attributes of the IoT device D) acquired by the third acquisition unit 13.
  • the IoT device attribute analysis unit 16 determines, for example, the manufacturer of the IoT device D, the manufacturing time of the IoT device D, whether or not the IoT device D has been calibrated and / or maintained, and the IoT device D has been calibrated and / or maintained. Regarding the time when the IoT device D was calibrated and / or the contents of maintenance, the response of the IoT device, whether or not the IoT device D is equipped with a secure chip (not shown), the operating status of the IoT device, and the IoT device D.
  • the attribute analysis of the IoT device D is performed based on the level of encryption in the device D and the like. Further, the IoT device attribute analysis unit 16 is based on, for example, the power consumption of the IoT device D acquired by the third acquisition unit 13, the temperature of the calculation unit of the IoT device D, the information on the open / closed state of the case of the IoT device D, and the like. Then, the attribute of the IoT device D is analyzed.
  • the IoT device attribute analysis unit 16 determines, for example, whether or not the IoT device D acquired by the third acquisition unit 13 has been modified in terms of software and / or hardware, the history of the IoT device D, and the log of the IoT device D. Analyze the attributes of the IoT device D based on the data and the like.
  • the credit rating calculation unit 17 is based on the analysis result of the measurement data analysis unit 14, the analysis result of the owner analysis unit 15, and the analysis result of the IoT device attribute analysis unit 16, and the measurement data of the sensor D1 transmitted from the IoT device D. Calculate the creditworthiness of. That is, the credit rating calculation unit 17 has the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the owner information (owner and / or of the IoT device D) acquired by the second acquisition unit 12. The credit rating of the measurement data of the sensor D1 transmitted from the IoT device D is calculated based on the information of the administrator) and the attribute information (information about the attributes of the IoT device D) acquired by the third acquisition unit 13.
  • the credit calculation unit 17 is acquired by the first acquisition unit 11. Based on the measurement data of the sensor D1 of the IoT device D and the non-measurement information acquired by the non-measurement information acquisition unit, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated.
  • the credit rating calculation unit 17 calculates a numerical value or rank as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D. Even after the credit rating calculation unit 17 calculates the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D, the result of the analysis continuously executed by the measurement data analysis unit 14 and the owner analysis unit 15 Based on the result of the continuously executed analysis and the result of the analysis continuously executed by the IoT device attribute analysis unit 16, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is continuously determined. calculate. In the example shown in FIG.
  • monitoring and / or checking of IoT device D has been described as part of the function of IoT device D, but is not limited to this, and monitoring and / or checking of IoT device D by other devices.
  • the check may be performed and the information may be input to the IoT device credit rating calculation device.
  • the owner information of the IoT device D and the attribute data of the IoT device are acquired directly from the IoT device, but this is not the case, and even if the separately saved data is acquired from the second acquisition unit and the third acquisition unit. Good.
  • the credit rating calculation unit 17 calculates the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by using artificial intelligence. In another example, the credit rating calculation unit 17 may calculate the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by executing the multivariate analysis described later. In yet another example, the credit rating calculation unit 17 may calculate the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by executing the state point calculation described later.
  • the storage unit 18 has the measurement data acquired by the first acquisition unit 11, the owner information acquired by the second acquisition unit 12, and the attribute information acquired by the third acquisition unit 13.
  • the analysis result of the measurement data analysis unit 14 the analysis result of the owner analysis unit 15, the analysis result of the IoT device attribute analysis unit 16, and the measurement data of the sensor D1 of the IoT device D calculated by the credit rating calculation unit 17. Memorize the creditworthiness of.
  • FIG. 2 is a flowchart for explaining an example of processing executed by the IoT device credit rating calculation device 1 of the first embodiment.
  • the first acquisition unit 11 acquires the measurement data of the sensor D1 transmitted from the IoT device D over time.
  • the second acquisition unit 12 acquires the owner information, which is the information of the owner and / or the manager of the IoT device D, from the IoT device D.
  • the third acquisition unit 13 acquires the attribute information which is the information regarding the attribute of the IoT device D from the IoT device D.
  • step S14 the measurement data analysis unit 14 analyzes the measurement data of the sensor D1 of the IoT device D acquired in step S11. Further, in step S15, the owner analysis unit 15 owns the IoT device D based on the owner information of the IoT device D (information on the owner and / or manager of the IoT device D) acquired in step S12. Perform a person and / or administrator analysis. Further, in step S16, the IoT device attribute analysis unit 16 analyzes the attributes of the IoT device D based on the attribute information (information about the attributes of the IoT device D) acquired in step S13.
  • step S17 the credit rating calculation unit 17 obtains the measurement data of the sensor D1 transmitted from the IoT device D based on the analysis result in step S14, the analysis result in step S15, and the analysis result in step S16. Calculate credit. That is, in step S17, the credit rating calculation unit 17 adds the measurement data of the sensor D1 of the IoT device D acquired in step S11, the owner information acquired in step S12, and the attribute information acquired in step S13. Based on this, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated. It is not necessary to perform steps S11 to S17 in this order, and they may be replaced as appropriate.
  • the measurement data of the sensor D1 of the IoT device D is taken into consideration when calculating the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D.
  • information about the owner and / or administrator of the IoT device D and information about the attributes of the IoT device D are also considered. Therefore, in the IoT device credit rating calculation device 1 of the first embodiment, the sensor D1 transmitted from the IoT device D is not based on the information of the owner and / or the manager of the IoT device D and the information regarding the attributes of the IoT device D.
  • the device credit rating calculation device 1 can appropriately determine the use of the data according to the credit rating of the measurement data of the sensor D1 calculated by the credit rating calculation unit 17.
  • the IoT device credit rating calculation device 1 of the first embodiment when calculating the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D, the analysis of the measurement data of the sensor D1 of the IoT device D is executed. Not only that, the analysis of the owner and / or administrator of the IoT device D and the analysis of the attributes of the IoT device D are also performed. Therefore, in the IoT device credit rating calculation device 1 of the first embodiment, the analysis of the owner and / or the manager of the IoT device D and the analysis of the attributes of the IoT device D are not executed, and the data is transmitted from the IoT device D. It is possible to calculate the reliability of the measurement data of the sensor D1 transmitted from the IoT device D with higher accuracy than when the reliability of the measurement data of the sensor D1 is calculated.
  • the measurement data analysis unit 14 continues. Based on the results of the analysis performed in, the results of the analysis continuously performed by the owner analysis unit 15, and the results of the analysis continuously performed by the IoT device attribute analysis unit 16, the IoT device D The credit rating of the measurement data of the sensor D1 transmitted from is continuously calculated. Therefore, in the IoT device credit rating calculation device 1 of the first embodiment, when any of the analysis result of the measurement data analysis unit 14, the analysis result of the owner analysis unit 15, and the analysis result of the IoT device attribute analysis unit 16 changes. Even if there is, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D can be calculated by reflecting the change.
  • the degree of trust of the IoT device D changes due to deterioration of the IoT device D, inadequate maintenance of the IoT device D, and the like.
  • the degree of credibility of the IoT device D may change due to the modification of the IoT device D or the like.
  • the management system of the manager of IoT device D is inadequate, and the manager of IoT device D loses financial temptation and measures. It cannot be denied that fake information may be transmitted from the IoT device D for various reasons such as malicious guidance to data users.
  • FIG. 3 is a diagram showing an example of processing in the IoT device credit rating device 1 when the measurement data analysis unit 14 shown in FIG. 1 executes a data mining process and the credit rating calculation unit 17 uses artificial intelligence or the like.
  • "IoT device data” (measurement data of the sensor D1 transmitted from the IoT device D over time) and "owner / administrator information” (owner and / or of the IoT device D).
  • (Administrator information) and "device information” information about the attributes of the IoT device D (attribute information)) are input to the IoT device credit rating calculation device 1.
  • the “IoT device data” is, for example, time-series data distributed from the IoT device D.
  • "Owner / administrator information” is, for example, registration information, management system, past data distribution information, etc., and corresponds to a positive point described later.
  • "Device information” includes, for example, registration information, communication path security, manufacturer, date of manufacture, calibration status, and the like.
  • the "state analysis by data mining or the like” is executed by the measurement data analysis unit 14 (see FIG. 1).
  • “credit score calculation by AI (artificial intelligence)” and “credit degree determination” are executed by the credit degree calculation unit 17 (see FIG. 1).
  • the certainty of the measurement data of the sensor D1 transmitted from the IoT device D (analysis result of the measurement data analysis unit 14) and the information of the person or tissue that owns and / or manages the IoT device D.
  • the credit score (degree of credit rating) is calculated using AI or the like from the information about the IoT device D itself.
  • the certainty of the measurement data of the sensor D1 transmitted from the IoT device D can be obtained by executing a process such as data mining.
  • Information on the owner and / or administrator of IoT device D includes the contents of the declaration of the owner and / or administrator of IoT device D, past history, information obtained from other databases and survey results, etc. Is D an authorized owner and / or administrator? Are you disseminating information about the IoT device D that you own and / or manage? Is the disposal process of IoT device D properly executed? It includes the content of information transmitted in the past, the management status of other IoT devices owned and / or managed, qualifications, credit, management system, etc. That is, the owner analysis unit 15 (see FIG.
  • Contents of information about IoT device D, management status of other IoT devices owned or managed by the owner and / or administrator of IoT device D, owner and / or administrator of IoT device D is another IoT device Based on information on the credit of the owner and / or manager of IoT device D, information on the management system of the owner and / or manager of IoT device D, etc., whether or not the disposal process is properly executed. Perform an analysis of the owner and / or administrator of IoT device D.
  • Information on the IoT device D includes the manufacturer and time of manufacture of the IoT device D, the presence / absence and timing of calibration of the IoT device D, the response of the IoT device D, the presence / absence of a secure chip, the operating status, various authentication information, the installation location, and the installation.
  • the environment, the difficulty of the data measured by the sensor D1 of the IoT device D, the communication path, the encryption level, etc., these information are passed through the model of the IoT device D, the type of device used, and the communication line. It can be obtained from reactions and the like. That is, the IoT device attribute analysis unit 16 (see FIG.
  • the measurement data analysis unit 14 As the information of the measurement data of the sensor D1 of the IoT device D, there are data including the past data transmitted from the target IoT device D, comparison with the transmission data of other IoT devices, and the like. That is, the measurement data analysis unit 14 (see FIG. 1) has acquired the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 (see FIG. 1) and the measurement data acquired by the first acquisition unit 11 in the past. Perform a comparison with past measurement data. Further, the measurement data analysis unit 14 compares the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 with the data transmitted from the IoT device other than the IoT device D.
  • registration information by the administrator, confirmation by the investigator, etc. can be considered. Further, a part or all of this information may be registered in the IoT device D and referred to through a communication line. In that case, it is desirable to store it in an area such as a chip protected by security so that it cannot be tampered with, and refer to it by encrypted communication. It is desirable that the writing of such information requires authority according to the content of the information. Further, although the information written on the chip can be added but cannot be deleted, it is desirable to also store information such as the date when the data was written, the person, and the organization.
  • the IoT device D itself is provided with a function for monitoring its own power consumption, or the IoT device D itself is provided with a function for self-checking whether or not it has been modified in terms of software or hardware. Often, such information may also be stored and made available for reference in a secure area.
  • the results of interviews and on-site confirmations may be used as a basis for judgment, if necessary.
  • interview and confirmations at the site where it is difficult to falsify the data are effective.
  • interviews and on-site confirmations are labor-intensive, so they are not suitable for checking a large number of IoT devices. Therefore, depending on the case, a method such as reducing the frequency by unannounced investigation or remote confirmation by a remote check by a camera or the like may be taken.
  • the degree of credibility and the degree of credibility of the measurement data of the sensor D1 are used interchangeably, and the degree of probability that the measurement data of the sensor D1 transmitted by the target IoT device D is correct information is probable. It is indicated by numerical values such as, or ranks such as A, B, and C, and does not detect or judge whether the data is correct. In other words, "high credit rating” indicates that the possibility of transmitting erroneous measurement data is low, and “low credit rating” indicates that the possibility of transmitting erroneous measurement data is high. Poor credibility does not mean that the measurement data is incorrect.
  • an aging temperature sensor that has been installed for a long time due to insufficient management and maintenance may result in transmitting correct measurement data, but it is judged that there is a high possibility that it will transmit incorrect measurement data. It becomes "low credit”.
  • the IoT device D owned by the owner who has transmitted malicious fake measurement data many times in the past, even if the correct information is transmitted, the data is "low credibility”. There is a possibility of becoming.
  • Judgment of the degree of overall credit may be made by an experienced person at the initial stage, but the credit score can be calculated automatically by having AI learn the experience. It will be possible. Scoring includes, for example, multivariate analysis, judgment-based processing, and a method using logistic regression, but the scoring is not limited to this, and algorithms that can be used for various scoring can be used.
  • FIG. 4 is a diagram showing an example of data mining processing executed by the measurement data analysis unit 14.
  • FIG. 4A shows the measurement data (time series data) of the sensor D1 of the IoT device D acquired by the first acquisition unit 11.
  • FIG. 4B shows a graphic representation of the characteristics of changes in the measurement data shown in FIG. 4A.
  • FIG. 4C shows an example of a method of executing data mining processing on the data shown in FIG. 4B.
  • FIG. 5 is a diagram showing an example of a neural network that executes data mining processing.
  • Data mining may be performed directly on the time-series data obtained from the IoT device D (measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11) (see FIG. 4 (A)).
  • FIG. 4B if the preprocessing in which the characteristics of the change can be visualized can be performed, more accurate data mining can be performed.
  • a method of analyzing the measurement data of the sensor D1 by data mining after forming a converted figure such as a frequency or chaos of a change with time can be considered.
  • a data mining method a method using a neural network, a method using logistic regression, a method using a support vector machine, a method using a K-nearest neighbor method, etc.
  • feature extraction is first performed by convolution processing, then pixel reduction is performed by pooling in the pooling layer, and then features are classified using a neural network.
  • a method such as a general convolutional neural network can be used, so that classification with high accuracy and a small amount of calculation becomes possible.
  • the probability that normal, measurement target abnormality, sensor state abnormality, installation environment defect, data falsification, device modification, etc. may occur is set.
  • the data obtained from the IoT device D may be data that senses the state of the IoT device D itself, in addition to the measurement data of the sensor D1 that is acquired by sensing the environment in which the IoT device D is installed. ..
  • the power consumption of the IoT device D itself the temperature of the calculation unit of the IoT device D, the open / closed state and history of the case of the IoT device D, log data, and the like.
  • some examples such as preprocessing, data mining, machine learning, and output are given, but the present invention is not limited to these, and any data that can estimate the reliability of the measurement data of the sensor D1 is electronic. , Mechanical, human judgment, etc. may be used.
  • FIG. 6 is a diagram showing an example of processing executed by the credit rating calculation unit 17 of the IoT device credit rating calculation device 1 of the first embodiment.
  • FIG. 6 is a diagram showing an example of the process executed in step S17 of FIG.
  • step S101 parameters related to the reliability of the measurement data of the sensor D1 of the IoT device D (for example, those output from the output layer shown in FIG. 5) are input.
  • the data input in step S101 for example, data obtained as the reliability of the data using the neural network shown in FIG. 5 or the like is used.
  • the data input in step S101 is classified, for example, by analyzing the tendency of past data, whether the data is intentionally processed, or whether an abnormal value is shown due to a failure of the IoT device D, or the like. However, the certainty (credibility) of the data is shown by, for example, a percentage.
  • step S102 the attribute of the device (IoT device D) is input.
  • the attributes of the device may be the degree of secure ability (secure level) of the device, whether the device is authenticated, and the like.
  • the secure level of the device is obtained through the degree of reliability as a device obtained from the manufacturer and model name of IoT device D, the degree of vulnerability due to device settings, the calibration history of the sensor of this device, and the communication line. It can be obtained from the reaction of the device.
  • step S103 information on the owner and / or manager (human, organization) of the IoT device D is input.
  • the owner and / or administrator of the IoT device D has been authenticated and exists, or has transmitted incorrect data in the past. If not, the frequency, if any, the degree of trust of the owner and / or administrator of IoT device D (human score), the degree of trust of the management system (management system score), the score of the installation environment of IoT device D, etc. Can be considered.
  • the human score indicates the score data of a person or organization obtained from past and present information such as income, debt, performance, and behavior.
  • step S104 a credit score, which is a measure of the data quality of the IoT device D, is calculated.
  • the credit rating unit 17 of the IoT device credit rating calculation device 1 of the first embodiment performs multivariate analysis to calculate the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D.
  • the credit rating calculation unit 17 calculated the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by executing the multivariate analysis using the discriminant function.
  • the coefficients of the discriminant function were obtained by multivariate analysis for discriminating the result of correctness of data for various input parameter information of 41 IoT devices (indicated by reference numerals A to AP in FIG. 7). ..
  • the result of the discriminant function was converted into a credit score (certainty) of the data so that it would be a positive number from the viewpoint of legibility.
  • the larger the credit score the better the quality of the data.
  • the accuracy can be further improved by newly updating the coefficient of the discriminant function using the result for the input data.
  • a linear discriminant function is used in the multivariate analysis, but in other examples, a quadratic discriminant function or a non-linear discriminant function may be used.
  • a technique such as artificial intelligence such as a neural network may be used.
  • FIG. 8 shows the credit as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by the credit rating calculation unit 17 of the IoT device credit rating calculation device 1 of the first embodiment executing the state point calculation (judgment base). It is a flowchart for demonstrating an example of the process of calculating a score.
  • FIG. 9 shows the credit as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by the credit rating calculation unit 17 of the IoT device credit rating calculation device 1 of the first embodiment executing the state point calculation (judgment base). It is a figure which shows an example which calculated the score.
  • step S201 the credit rating calculation unit 17 executes the calculation of the state points. Specifically, in step S201, the security strength (secure level) of the IoT device D to be determined, the person / organization (human score) that manages the IoT device D, and the current state of management in which the IoT device D is installed ( Management system score) and the state of the installation environment of IoT device D (environmental score) are scored. In the example shown in FIG. 9, the good condition of the IoT device D corresponds to a high point, and the point normalized to a value of 1 to 100 was used.
  • step S202 the credit rating calculation unit 17 executes positive point addition.
  • step S202 points are assigned (addition of positive points) to past achievements such as properly managed and transmitted normal data.
  • the positive point addition is to add the current point to the previous point, and gives a high evaluation to the IoT device D that has transmitted normal data for a long period of time.
  • the points given to such past actions are called "cumulative positive points" (see FIG. 9).
  • the data score obtained by analyzing the human score, the management system score, the environmental score and the measurement data of the sensor D1 transmitted by the IoT device D used in the previous state points is normalized to 1 to 5. There was.
  • the same score for human / organization, management system, and environment was used for the status point and the cumulative positive point, but in other examples, different scores such as changing the weight between the current state and the past were used. You may.
  • step S203 the credit rating calculation unit 17 executes a determination of the presence / absence of a negative event (whether or not a negative event has occurred), which will be described later. For example, if there is a negative event (a negative event has occurred) such as the IoT device D transmitting erroneous data or the registered data lying, the process proceeds to step S204. On the other hand, if there is no negative event (no negative event has occurred), the process proceeds to step S205. In step S204, the credit rating calculation unit 17 resets or deducts points, and proceeds to step S205. In step S204, a penalty is given to a target who has caused an accident that greatly impairs reliability by greatly reducing the accumulated positive points.
  • a negative event a negative event has occurred
  • the points to be reset or deducted may be given only to the cumulative positive points, or may be given to the state points and the cumulative positive points.
  • points related to people / tissues / environment and negative events points are calculated in common for all IoT devices included in the people / tissues / environment.
  • step S205 the credit rating calculation unit 17 calculates the credit score from the points calculated in steps S201 to S204.
  • step S206 the credit rating calculation unit 17 executes point update (storage of points calculated in steps S201 to S204).
  • step S207 the credit rating calculation unit 17 determines whether or not to continue the process shown in FIG. When continuing the process shown in FIG. 8, the process returns to step S202. On the other hand, when the process shown in FIG. 8 is not continued, the process shown in FIG. 8 is terminated.
  • the information and input order for calculating the creditworthiness of the IoT device D shown in the above example is an example and is not limited thereto.
  • the following are conceivable fields in which the data user needs the reliability of the measurement data of the sensor D1 of the IoT device D.
  • Data from public institutions such as local governments, such as meteorological data such as water level, ground, temperature, humidity, wind speed, and atmospheric pressure, data related to nature-related businesses such as disasters, agriculture, and fisheries, traffic volume, and data on the movement of people. Conceivable. This is because these data are installed in nature, difficult to manage, and exposed to wind and rain, so that abnormalities are likely to occur due to various causes.
  • measurement data related to product quality at production sites and construction sites can be considered.
  • data related to quality such as weight, size, thickness, hardness, color, image, speed, density, and quantity are measured. These are fields where it is necessary to prove that the administrator or the like is not acting as a data spoofing, and there is a high need for a third party to check the data.
  • data sent by businesses such as agriculture, forestry, and fisheries can be mentioned. By interusing the data, these can improve the accuracy of climate variability, pests and signs of diseases. Specifically, water level, water temperature, temperature, humidity, wind speed, amount of solar radiation, CO 2 concentration, pests, diseases, images, growing conditions, fertilizer concentration, soil, water quality, etc. are assumed.
  • dangerous substances such as measurement data of causative bacteria and viruses of infectious diseases, measurement data of radiation dose and radioactive substances, and measurement data of poisons and dangerous substances.
  • dangerous substances such as measurement data of causative bacteria and viruses of infectious diseases, measurement data of radiation dose and radioactive substances, and measurement data of poisons and dangerous substances.
  • the amount of substances, oxygen concentration, carbon monoxide concentration, number of infectious diseases, and amount of infectious disease mediators are assumed.
  • a large amount of data is required, but if the reliability of the input data is low, correct analysis cannot be performed.
  • High reliability of data is required for information from devices related to life and health such as medical devices used for remote diagnosis. Specifically, body temperature, pulse rate, blood sugar level, triglyceride, skin color, blood pressure, blood oxygen concentration, amount of exercise, calories burned, dietary content, voice, blood component, exhaled component, urine component, saliva component, Sweat components, mucosal secretion components, etc. are assumed.
  • Energy-related information such as crude oil, gas, and coal, information related to the mining industry such as ores, precious metals, rare metals, and gems, and trading volume of agricultural products, etc., where prices in the trading market depend on the environment, etc.
  • data such as images that can estimate the yield and yield, data related to the content of the target substance, quality, and the like are assumed.
  • some fields that require the creditworthiness of the data of the IoT device have been raised, but the fields that require the confirmation of the creditworthiness of the data of the IoT device are not limited to this.
  • FIG. 10 is a diagram showing a first application example of the IoT device credit rating calculation device 1 of the first embodiment.
  • FIG. 10 shows the IoT device credit rating system S to which the IoT device credit rating calculation device 1 of the first embodiment is applied.
  • the IoT device credit rating system S includes an IoT device credit rating calculation device 1, an IoT device D, and a network NW.
  • the IoT device credit rating device 1 is owned or managed by a person who does not own, manage, or manufacture the IoT device D.
  • the IoT device D transmits the measurement data of the sensor D1 (see FIG. 1) to a location other than the first acquisition unit 11 (see FIG.
  • the IoT device credit rating device 1 is used for the rating of the IoT device D.
  • the IoT device credit rating calculation device 1 of the IoT rating agency confirms the credit rating of the IoT device D through a network NW such as the Internet, and ranks the IoT device D according to the obtained degree of credit rating. Next, the result of the rating or the result and the basis thereof are provided to the data user side and the compensation is obtained.
  • the data user analyzes and utilizes the data based on the credit rating of the IoT device D obtained from the IoT rating agency.
  • a service is also conceivable in which a data user requests a rating agency for a target whose credit rating is to be confirmed, and the rating agency reports the result.
  • the consideration for these services may be money, set sales with data, etc., and these may be considered alone or in combination.
  • the service itself may be free of charge as it is used for public acts and improvement of corporate value.
  • the target of rating may be the IoT device D itself, or the unit of a person or corporation such as the owner or manager of the IoT device D, or the unit of a place such as an installation place or an area. You may go to the target. By doing so, the data user can obtain a more accurate data analysis result by using the certainty of the data to be used as a premise of the data analysis.
  • FIG. 11 is a diagram showing a second application example of the IoT device credit rating calculation device 1 of the first embodiment.
  • FIG. 11 shows the IoT device credit rating system S to which the IoT device credit rating calculation device 1 of the first embodiment is applied.
  • the IoT device credit rating system S includes an IoT device credit rating calculation device 1, an IoT device D, and a network NW.
  • the IoT device credit rating device 1 is owned or managed by a person who does not own, manage, or manufacture the IoT device D.
  • the IoT device D transmits the measurement data of the sensor D1 (see FIG. 1) to a location other than the first acquisition unit 11 (see FIG.
  • the IoT device credit rating device 1 is the authentication of the IoT device D (electronic certificate). Used for issuance).
  • the IoT device authentication information is registered in the IoT device at the manufacturing stage or installation stage of the IoT device, and the received data is definitely the data transmitted from the target IoT device. It is confirmed whether or not.
  • this authentication is effective for measures such as falsification and replacement of data in communication paths, it was not possible to confirm the creditworthiness of the target IoT device and the creditworthiness of the owner or administrator.
  • the IoT device is registered based on the registration information from the owner and / or manager of the IoT device.
  • Is it managed normally is the administrator's qualifications and qualifications appropriate, is the sensor calibrated properly, is the measurement data likely to be correct, is the equipment setting appropriate, is the installation method and installation location appropriate? If there is no problem, a certificate will be issued. Guidance will be provided if there is a problem. Investigators perform things that are difficult to judge from digital information alone, such as the installation environment and management system, on a regular basis, unannounced, or both. If the installation location of the IoT device can be monitored by a camera or the like, it may be confirmed remotely. From this information, the degree of credit is calculated by the method described above.
  • FIG. 12 is a flowchart showing an example of processing executed in the IoT device credit rating calculation system S of the second application example of the IoT device credit rating calculation device 1 of the first embodiment.
  • step S301 the reliability of the IoT device D is confirmed, and it is determined whether or not the IoT device D is reliable. If it is unreliable, the process proceeds to step S302. On the other hand, if it is reliable, the process proceeds to step S303. In step S302, guidance / correction is performed, and then the process returns to step S301. In step S303, a certificate is issued. Next, in step S304, it is determined whether or not the certificate is within the expiration date.
  • step S305 it is determined whether or not the IoT device D is normal. If the IoT device D is normal, the process returns to step S301. On the other hand, if the IoT device D is not normal, the process proceeds to step S302.
  • the certificate has an expiration date, the expiration date is checked periodically, and the certificate is reissued as needed.
  • the IoT device D for which the certificate has been issued is monitored via a communication line such as the Internet. If it is determined that the reliability of the measurement data is not maintained, guidance and correction will be provided. In other cases, when strong fairness is required, it may be required not to provide consulting services such as guidance. In yet another example, the certificate may be revoked if it is determined that the credibility of the measured data is likely to be unreliable.
  • the IoT device whose degree of trust in the data of the IoT device as described above, or the individual or group who manages the IoT device, or both, can transmit the data proved to be reliable, and the value of the data. Can be increased.
  • money, the right to secondary use of data, the use of a specific secure chip, etc. can be considered, and these are considered alone or in combination.
  • FIG. 13 is a diagram showing a third application example of the IoT device credit rating calculation device 1 of the first embodiment.
  • FIG. 13 shows the IoT device credit rating calculation system S to which the IoT device credit rating calculation device 1 of the first embodiment is applied.
  • the data user receives the data directly from the IoT device D.
  • a data user receives data stored in a database such as a cloud. Collecting information in the database DB has advantages such as preventing the concentration of data access to the IoT device D and utilizing the cloud security system.
  • FIG. 14 is a diagram showing an example of a schematic configuration of the IoT data platform P of the second embodiment.
  • the IoT data platform P includes an IoT data receiving unit P1, a first consideration calculation unit P2, an IoT data transmission unit P3, a second consideration calculation unit P4, and a credit rating information receiving unit P5.
  • the IoT data receiving unit P1 receives IoT data (for example, measurement data of the sensor D1 of the IoT device D) transmitted from the IoT device D (see FIG. 1).
  • the first consideration calculation unit P2 calculates the first consideration which is the basis of the consideration for the IoT data received by the IoT data receiving unit P1.
  • the IoT data transmission unit P3 transmits the IoT data received by the IoT data reception unit P1.
  • the second consideration calculation unit P4 calculates the second consideration which is the basis of the consideration for the IoT data transmitted by the IoT data transmission unit P3.
  • the credit rating information receiving unit P5 receives information regarding the credit rating of the IoT data received by the IoT data receiving unit P1.
  • the first consideration calculation unit P2 calculates the first consideration based on the information regarding the credit rating of the IoT data received by the credit rating information receiving unit P5.
  • the second consideration calculation unit P4 calculates the second consideration based on the information regarding the credit rating of the IoT data received by the credit rating information receiving unit P5.
  • FIG. 15 is a flowchart for explaining an example of processing executed in the IoT data platform P of the second embodiment.
  • the IoT data receiving unit P1 receives the IoT data transmitted from the IoT device D (see FIG. 1).
  • the credit rating information receiving unit P5 receives information regarding the credit rating of the IoT data received by the IoT data receiving unit P1.
  • the first consideration calculation unit P2 calculates the first consideration, which is the basis of the consideration for the IoT data received in step S1, based on the information regarding the creditworthiness of the IoT data received in step S2. ..
  • step S4 the IoT data transmission unit P3 transmits the IoT data received in step S1.
  • step S5 the second consideration calculation unit P4 calculates the second consideration, which is the basis of the consideration for the IoT data transmitted in step S4, based on the information regarding the creditworthiness of the IoT data received in step S2. ..
  • the basis of consideration for the transmitted / received IoT data is calculated based on the information on the creditworthiness of the transmitted / received IoT data, so that the convenience of using the IoT data is improved. Can be done.
  • FIG. 16 is a diagram showing an example of an IoT data distribution system DS to which the IoT data platform P of the second embodiment is applied.
  • the IoT data is received from the IoT data possessing unit W that possesses the IoT data transmitted from the IoT device D (see FIG. 1) in a concealed state.
  • the IoT data transmission unit P3 transmits IoT data to the IoT data utilization unit U in a state where the information of the IoT data possession unit W is concealed.
  • the credit rating information receiving unit P5 receives information on the credit rating of the IoT data from the rating unit R.
  • the IoT data platform P has a data pricing function. That is, in the IoT data platform P, the usage price is determined according to the data sender (IoT data possession unit W) in response to the request (demand) from the data user (IoT data utilization unit U). Information will be exchanged when mutual agreement is reached. At this time, the reliability of the IoT data can be reflected in the price by referring to and adding the information of the rating agency (Rating Department R). Reliability can also be displayed as the basis for pricing. Further, in the IoT data platform P, a process may be executed in which a premium or a discount is intentionally added when information is traded.
  • the information of the data owner (IoT data owner W) and the user (IoT data user U) is anonymized, and the distribution of IoT data is managed and operated without personal information. Is desirable.
  • the rating agency (Rating Department R) can also use it as one of the information for evaluating and rating the guarantee of anonymity.
  • IoT data platform P it is possible to manage the history of IoT data owner (IoT data owner W), flow path, movement, etc. by using not only technology such as blockchain but also existing cloud security system. , Can be used as a strong means against data tampering.
  • the data user IoT data utilization unit U
  • IoT data utilization unit U can easily search for the IoT data that he / she wants to use, and can easily obtain the data according to the necessary conditions.
  • a request is made to the IoT data platform P to receive a service that makes it easy to use highly reliable information.
  • the data owner By utilizing the IoT data platform P, the data owner (IoT data owner unit W) can easily find the data user (IoT data user unit U), and it becomes easier to obtain the compensation for the data. In addition, it is possible to obtain the information (data with large demand) that the data user (IoT data utilization unit U) wants.
  • the range of use and period of data to be distributed can be freely set by the provider (IoT data ownership unit W), and the value provided is also guaranteed.
  • the rating agency (Rating Department R) audits the security, management status, and usage status of the IoT data platform P, and performs rating, authentication, and correction. User convenience such as real-time and searchability can also be used as evaluation information.
  • This IoT data platform P can be used as a platform for widely handling and distributing data as well as information from an IoT device (IoT device D).
  • a computer-readable recording medium with a program for realizing these functions.
  • the program recorded on this recording medium may be read by a computer system and executed.
  • the term "computer system” as used herein includes hardware such as an OS and peripheral devices.
  • the "computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, or a CD-ROM, or a storage unit such as a hard disk built in a computer system.
  • a "computer-readable recording medium” is a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, and dynamically holds the program for a short period of time.
  • a volatile memory inside the computer system that serves as a server or a client in that case may hold a program for a certain period of time.
  • the above-mentioned program may be a program for realizing a part of the above-mentioned functions, and may be a program for realizing the above-mentioned functions in combination with a program already recorded in the computer system.
  • the IoT device is the history of the IoT device and / or the log data of the IoT device, whether or not the IoT device has been modified in terms of software and / or hardware.
  • the third acquisition unit further includes a check unit for checking whether or not the IoT device checked by the check unit has been modified in a soft and / or hard manner, a history of the IoT device and / or the above.
  • the log data of the IoT device is acquired, and the IoT device attribute analysis unit determines whether or not the IoT device acquired by the third acquisition unit has been modified in terms of software and / or hardware, the history of the IoT device, and the history of the IoT device. / Or, the attribute analysis of the IoT device may be performed based on the log data of the IoT device.
  • the credit rating calculation unit calculates the credit score as the credit rating of the measurement data transmitted from the IoT device by executing multivariate analysis or state point calculation. You may.
  • the credit rating calculation unit may calculate a numerical value or rank as the credit rating of the measurement data transmitted from the IoT device.
  • the IoT device further includes a storage unit for storing the owner information and the attribute information, and the second acquisition unit is stored in the storage unit.
  • the owner information is acquired, and the third acquisition unit may acquire the attribute information stored in the storage unit.
  • the certificate may be an electronic certificate.
  • One aspect of the present invention is a method of using the IoT device credit rating device, wherein the credit rating of the measurement data calculated by the credit rating calculation unit is used for rating the IoT device. How to use.
  • the credit rating information receiving unit may receive information on the credit rating of the IoT data from the rating unit.

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Abstract

This IoT apparatus credibility calculation device is provided with: a first acquisition unit which acquires measurement data emitted over time from an IoT apparatus having a sensor; a non-measurement information acquisition unit which acquires non-measurement information that is information other than the measurement data emitted from the IoT apparatus; and a credibility calculation unit which calculates the credibility of the measurement data emitted from the IoT apparatus on the basis of the measurement data acquired by the first acquisition unit and the non-measurement information acquired by the non-measurement information acquisition unit.

Description

ＩｏＴ機器信用度算出装置、ＩｏＴ機器信用度算出システム、ＩｏＴ機器信用度算出装置の利用方法、ＩｏＴ機器信用度算出方法、記憶媒体、ＩｏＴデータプラットフォーム、および、ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法IoT device credit calculation device, IoT device credit calculation system, usage method of IoT device credit calculation device, IoT device credit calculation method, storage medium, IoT data platform, and IoT data processing method by IoT data platform
　本発明は、ＩｏＴ機器信用度算出装置、ＩｏＴ機器信用度算出システム、ＩｏＴ機器信用度算出装置の利用方法、ＩｏＴ機器信用度算出方法、記憶媒体、ＩｏＴデータプラットフォーム、および、ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法に関する。
　本願は、２０１９年３月１２日に、日本に出願された特願２０１９－０４４９１７号に基づき優先権を主張し、その内容をここに援用する。 The present invention relates to an IoT device credit calculation device, an IoT device credit calculation system, a method of using the IoT device credit calculation device, an IoT device credit calculation method, a storage medium, an IoT data platform, and a method of processing IoT data by the IoT data platform. ..
The present application claims priority based on Japanese Patent Application No. 2019-044917 filed in Japan on March 12, 2019, the contents of which are incorporated herein by reference.
　全てのモノがインターネットに繋がるＩｏＴ（Internet of Things）が注目され、急速に普及している。また、ＩｏＴ機器に関する様々なセキュリティ対策が行われている。例えば、一般社団法人　情報通信ネットワーク産業協会が作成した「ＩｏＴ機器のセキュリティ対策について」（http://www.soumu.go.jp/main_content/000538611.pdf）では、端末情報の漏えい、なりすまし、盗聴、のっとり、誘導を主なＩｏＴ機器への脅威としてあげ、それについての対策について説明をしている。しかしながら、これらは主に悪意のある第三者がネットワークを介して介入することに対する対策であり、ＩｏＴ機器そのものやＩｏＴ機器の所有者、管理者などに問題がある場合の対策は挙げられていない。
　一方、横浜国立大学の松本勉教授らは、現在、比較的閉じた形のＩｏＴアーキテクチャーが、２０３０年ごろにはオープンなアーキテクチャーに変化するとしている（https://www.nedo.go.jp/content/100867110.pdf）。 The IoT (Internet of Things), in which all things are connected to the Internet, is attracting attention and is rapidly spreading. In addition, various security measures for IoT devices are being taken. For example, in "Security Measures for IoT Devices" (http://www.soumu.go.jp/main_content/000538611.pdf) created by the Information and Communication Network Industry Association, terminal information leakage, spoofing, and eavesdropping , Spoofing, cites guidance as a threat to major IoT devices, and explains countermeasures for it. However, these are mainly measures against malicious third parties intervening via the network, and measures are not given when there is a problem with the IoT device itself, the owner or administrator of the IoT device, etc. ..
On the other hand, Professor Tsutomu Matsumoto of Yokohama National University and others say that the relatively closed IoT architecture will change to an open architecture around 2030 (https://www.nedo.go. jp / content / 108067110.pdf).
　オープンなＩｏＴとは、ドメイン、事業主を問わず、ＩｏＴの様々なレイヤ間でデータ流通のメッシュ化、サービスの多層化、仮想化が進み、複数のステークホルダーが多様に繋がる究極のＩｏＴに向かって展開していることを示している。オープンなＩｏＴが実現した場合、データの利用側と提供側が垂直統合されておらず、データの利用側はデータ提供側のＩｏＴ機器の所有者や管理者、さらにはＩｏＴ機器の設置状態などを十分に把握できないケースが想定され、その場合、ＩｏＴ機器から発信されたデータの品質の判断できなくなる。 Open IoT is toward the ultimate IoT where multiple stakeholders are connected in various ways by meshing data distribution, multi-layering services, and virtualization between various layers of IoT regardless of domain or business owner. It shows that it is expanding. When open IoT is realized, the data user side and the data provider side are not vertically integrated, and the data user side is sufficient for the owner and manager of the IoT device on the data provider side, as well as the installation status of the IoT device. In some cases, it is not possible to determine the quality of the data transmitted from the IoT device.
　図１７は現状の問題点の一例を示す図である。
　図１７に示すように、例えば、ＩｏＴ機器管理者が、悪意をもって偽のデータを流す、ＩｏＴ機器が正しく設置されていない、ＩｏＴ機器がきちんと校正されておらず誤ったデータが発信されてしまうなど、所有者や管理者など、第三者以外からの脅威が生じる可能性がある。特に機器を設置した後、管理者が不正をはたらく、機器がきちんとメンテナンスされておらず測定値が異常な値をしめすなど、後々になって情報の信用度が損なわれるような場合、データの利用者はデータの信用度を把握することは困難であった。 FIG. 17 is a diagram showing an example of the current problems.
As shown in FIG. 17, for example, the IoT device manager maliciously sends fake data, the IoT device is not installed correctly, the IoT device is not properly calibrated, and incorrect data is transmitted. , There is a possibility of threats from non-third parties such as owners and managers. Especially when the administrator acts fraudulently after installing the device, the device is not properly maintained and the measured value shows an abnormal value, and the credibility of the information is damaged later, the data user It was difficult to grasp the creditworthiness of the data.
　また、従来から、センサ機器により検知される環境情報の信頼性を担保する管理装置および環境センシングシステムが知られている。（例えば、特許文献１参照）。
　特許文献１に記載された技術では、センサ機器が、第１の環境情報と第２の環境情報とを管理装置に送信し、管理装置は、センサ機器によって送信された第１の環境情報と第２の環境情報とを受信する。また、管理装置は、第２の環境情報に基づいてセンサ機器の周囲の環境が変化したと判断された場合に、センサ機器から送信された第１の環境情報の利用を制限する制御を実行する。
　ところで、特許文献１には、管理者が環境センサ（センサ機器）の確認を行う旨が記載されているものの、特許文献１に記載された技術では、環境センサ（センサ機器）の管理者がどのような者であるかが考慮されることなく、センサ機器により検知される環境情報の信頼性の有無が判断される。
　また、特許文献１に記載された技術では、例えばセンサ機器により検知される環境情報の信頼性を担保する設備がセンサ機器に備えられているか否か等のような、センサ機器の属性が考慮されることなく、センサ機器により検知される環境情報の信頼性の有無が判断される。
　そのため、特許文献１に記載された技術によっては、センサ機器により検知される環境情報の信頼性の有無を高精度に判断できないおそれがある。 In addition, management devices and environmental sensing systems that ensure the reliability of environmental information detected by sensor devices have been conventionally known. (See, for example, Patent Document 1).
In the technique described in Patent Document 1, the sensor device transmits the first environmental information and the second environmental information to the management device, and the management device transmits the first environmental information and the second environmental information transmitted by the sensor device. Receives the environmental information of 2. In addition, the management device executes control that limits the use of the first environmental information transmitted from the sensor device when it is determined that the environment around the sensor device has changed based on the second environmental information. ..
By the way, although Patent Document 1 describes that the administrator confirms the environmental sensor (sensor device), in the technology described in Patent Document 1, which is the administrator of the environmental sensor (sensor device)? Whether or not the environmental information detected by the sensor device is reliable is determined without considering whether or not the person is such a person.
Further, in the technique described in Patent Document 1, attributes of the sensor device such as whether or not the sensor device is equipped with equipment for ensuring the reliability of the environmental information detected by the sensor device are taken into consideration. It is determined whether or not the environmental information detected by the sensor device is reliable.
Therefore, depending on the technique described in Patent Document 1, there is a possibility that it is not possible to accurately determine whether or not the environmental information detected by the sensor device is reliable.
特開２０１８－１６０８３４号公報JP-A-2018-160834
　上述した問題点に鑑み、本発明は、ＩｏＴ機器から発信された測定データの信用度を高精度に算出することができるＩｏＴ機器信用度算出装置、ＩｏＴ機器信用度算出システム、ＩｏＴ機器信用度算出装置の利用方法、ＩｏＴ機器信用度算出方法および記憶媒体を提供することを目的とする。
　また、本発明は、ＩｏＴデータを利用する利便性を向上させることができるＩｏＴデータプラットフォーム、および、ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法を提供することを目的とする。 In view of the above-mentioned problems, the present invention relates to an IoT device credit calculation device, an IoT device credit calculation system, and a method of using an IoT device credit calculation device that can calculate the credit rating of measurement data transmitted from an IoT device with high accuracy. , An object of the present invention is to provide a method for calculating IoT device creditworthiness and a storage medium.
Another object of the present invention is to provide an IoT data platform capable of improving the convenience of using IoT data, and a method of processing IoT data by the IoT data platform.
　本発明の一態様は、センサを有するＩｏＴ機器から経時的に発信された測定データを取得する第１取得部と、前記ＩｏＴ機器から発信された前記測定データ以外の情報である非測定情報を取得する非測定情報取得部と、前記第１取得部によって取得された前記測定データと、前記非測定情報取得部によって取得された前記非測定情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する信用度算出部とを備えるＩｏＴ機器信用度算出装置である。 One aspect of the present invention is a first acquisition unit that acquires measurement data transmitted over time from an IoT device having a sensor, and non-measurement information that is information other than the measurement data transmitted from the IoT device. The measurement transmitted from the IoT device based on the non-measurement information acquisition unit, the measurement data acquired by the first acquisition unit, and the non-measurement information acquired by the non-measurement information acquisition unit. It is an IoT device credit rating calculation device including a credit rating calculation unit for calculating the credit rating of data.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記非測定情報には、前記ＩｏＴ機器の所有者および／または管理者の情報である所有者情報と、前記ＩｏＴ機器の属性に関する情報である属性情報とが含まれ、前記非測定情報取得部は、前記所有者情報を取得する第２取得部と、前記属性情報を取得する第３取得部とを備え、前記信用度算出部は、前記第１取得部によって取得された前記測定データと、前記第２取得部によって取得された前記所有者情報と、前記第３取得部によって取得された前記属性情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the non-measurement information includes owner information which is information on the owner and / or manager of the IoT device and attributes which are information on attributes of the IoT device. The non-measurement information acquisition unit includes information, and includes a second acquisition unit for acquiring the owner information and a third acquisition unit for acquiring the attribute information, and the credit rating calculation unit includes the first. It was transmitted from the IoT device based on the measurement data acquired by the acquisition unit, the owner information acquired by the second acquisition unit, and the attribute information acquired by the third acquisition unit. The credit rating of the measurement data may be calculated.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記第１取得部によって取得された前記測定データの分析を実行する測定データ分析部と、前記第２取得部によって取得された前記所有者情報に基づいて、前記ＩｏＴ機器の所有者および／または管理者の分析を実行する所有者分析部と、前記第３取得部によって取得された前記属性情報に基づいて、前記ＩｏＴ機器の属性の分析を実行するＩｏＴ機器属性分析部とを更に備え、前記信用度算出部は、前記測定データ分析部の分析結果と前記所有者分析部の分析結果と前記ＩｏＴ機器属性分析部の分析結果とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the measurement data analysis unit that executes the analysis of the measurement data acquired by the first acquisition unit and the owner information acquired by the second acquisition unit are used. Based on the owner analysis unit that executes the analysis of the owner and / or administrator of the IoT device, and the attribute information acquired by the third acquisition unit, the attribute analysis of the IoT device is executed. The IoT device attribute analysis unit is further provided, and the credit rating calculation unit is based on the analysis result of the measurement data analysis unit, the analysis result of the owner analysis unit, and the analysis result of the IoT device attribute analysis unit. The credit rating of the measurement data transmitted from the IoT device may be calculated.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記測定データ分析部は、前記第１取得部によって取得された前記測定データと、前記第１取得部によって過去に取得された過去分測定データとの比較を実行すると共に、前記第１取得部によって取得された前記測定データと、前記ＩｏＴ機器以外のＩｏＴ機器から発信されたデータとの比較を実行してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the measurement data analysis unit includes the measurement data acquired by the first acquisition unit and the past measurement data acquired in the past by the first acquisition unit. In addition to executing the comparison, the measurement data acquired by the first acquisition unit may be compared with the data transmitted from the IoT device other than the IoT device.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記測定データ分析部は、前記第１取得部によって取得された前記測定データに対する人為的な操作の痕跡の有無の分析を実行してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the measurement data analysis unit may perform analysis of the presence or absence of traces of artificial operations on the measurement data acquired by the first acquisition unit.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記測定データ分析部は、前記測定データの変化の特徴を図形化した後にデータマイニング処理を実行することによって、前記測定データの分析を実行してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the measurement data analysis unit executes analysis of the measurement data by performing a data mining process after graphicizing the characteristics of changes in the measurement data. May be good.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記所有者分析部は、前記ＩｏＴ機器の所有者および／または管理者の申告内容、過去の履歴、他のデータベースから得られた情報、および、調査結果から得られた情報の少なくともいずれかに基づいて、前記ＩｏＴ機器の所有者および／または管理者が、前記ＩｏＴ機器の正規の所有者および／または管理者であるか否かを分析してもよい。 In the IoT device credit rating device of one aspect of the present invention, the owner analysis unit uses the declaration contents of the owner and / or manager of the IoT device, past history, information obtained from other databases, and Based on at least one of the information obtained from the findings, analyze whether the owner and / or administrator of the IoT device is the legitimate owner and / or administrator of the IoT device. May be good.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記所有者分析部は、前記ＩｏＴ機器の所有者および／または管理者が前記ＩｏＴ機器に関する情報を発信しているか否か、前記ＩｏＴ機器の所有者および／または管理者が過去に発信した前記ＩｏＴ機器に関する情報の内容、前記ＩｏＴ機器の所有者および／または管理者が所有または管理している他のＩｏＴ機器の管理状況、前記ＩｏＴ機器の所有者および／または管理者が前記他のＩｏＴ機器の廃棄処理を適切に実行しているか否か、前記ＩｏＴ機器の所有者および／または管理者の与信に関する情報、および、前記ＩｏＴ機器の所有者および／または管理者の管理体制に関する情報の少なくともいずれかに基づいて、前記ＩｏＴ機器の所有者および／または管理者の分析を実行してもよい。 In the IoT device credit calculation device of one aspect of the present invention, the owner analysis unit determines whether or not the owner and / or manager of the IoT device transmits information about the IoT device, and owns the IoT device. The content of information about the IoT device transmitted by the person and / or the administrator in the past, the management status of other IoT devices owned or managed by the owner and / or the administrator of the IoT device, the possession of the IoT device. Whether or not the person and / or the administrator properly disposes of the other IoT device, information on the credit of the owner and / or the administrator of the IoT device, and the owner of the IoT device and / Or an analysis of the owner and / or manager of the IoT device may be performed based on at least one of the information about the manager's management system.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記ＩｏＴ機器属性分析部は、前記ＩｏＴ機器の製造元、前記ＩｏＴ機器の製造時期、前記ＩｏＴ機器の校正および／またはメンテナンスが実行されたか否か、前記ＩｏＴ機器の校正および／またはメンテナンスが実行された時期、前記ＩｏＴ機器の校正および／またはメンテナンスの内容、前記ＩｏＴ機器の応答、前記ＩｏＴ機器にセキュアチップが備えられているか否か、前記ＩｏＴ機器の動作状況、前記ＩｏＴ機器に関する認証情報、前記ＩｏＴ機器の設置場所、前記ＩｏＴ機器の設置環境および／または測定環境、前記ＩｏＴ機器の前記センサによる前記測定データの測定の難易度、前記ＩｏＴ機器によって発信された前記測定データの通信経路、および、前記ＩｏＴ機器における暗号化のレベルの少なくともいずれかに基づいて、前記ＩｏＴ機器の属性の分析を実行してもよい。 In the IoT device credit rating device of one aspect of the present invention, the IoT device attribute analysis unit determines the manufacturer of the IoT device, the manufacturing time of the IoT device, and whether or not the calibration and / or maintenance of the IoT device has been executed. When the IoT device was calibrated and / or maintained, the content of the IoT device calibration and / or maintenance, the response of the IoT device, whether or not the IoT device is equipped with a secure chip, the IoT device. Operation status, authentication information about the IoT device, installation location of the IoT device, installation environment and / or measurement environment of the IoT device, difficulty of measuring the measurement data by the sensor of the IoT device, depending on the IoT device. Analysis of the attributes of the IoT device may be performed based on at least one of the transmitted communication path of the measured data and the level of encryption in the IoT device.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記ＩｏＴ機器は、前記ＩｏＴ機器の消費電力、前記ＩｏＴ機器の演算部の温度および／または前記ＩｏＴ機器のケースの開閉状態をモニタリングするモニタリング部を更に備え、前記第３取得部は、前記モニタリング部によってモニタリングされた前記ＩｏＴ機器の消費電力、前記ＩｏＴ機器の演算部の温度および／または前記ＩｏＴ機器のケースの開閉状態の情報を取得し、前記ＩｏＴ機器属性分析部は、前記第３取得部によって取得された前記ＩｏＴ機器の消費電力、前記ＩｏＴ機器の演算部の温度および／または前記ＩｏＴ機器のケースの開閉状態の情報に基づいて、前記ＩｏＴ機器の属性の分析を実行してもよい。 In the IoT device credit calculation device of one aspect of the present invention, the IoT device includes a monitoring unit that monitors the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and / or the open / closed state of the case of the IoT device. Further, the third acquisition unit acquires information on the power consumption of the IoT device monitored by the monitoring unit, the temperature of the calculation unit of the IoT device, and / or the open / closed state of the case of the IoT device. The IoT device attribute analysis unit is based on the power consumption of the IoT device acquired by the third acquisition unit, the temperature of the calculation unit of the IoT device, and / or the open / closed state information of the case of the IoT device. An analysis of device attributes may be performed.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記信用度算出部が、前記ＩｏＴ機器から発信された前記測定データの信用度を算出した後に、前記信用度算出部は、前記測定データ分析部によって継続的に実行された分析の結果と、前記所有者分析部によって継続的に実行された分析の結果と、前記ＩｏＴ機器属性分析部によって継続的に実行された分析の結果とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を継続的に算出してもよい。 In the IoT device credit rating device of one aspect of the present invention, after the credit rating calculation unit calculates the credit rating of the measurement data transmitted from the IoT device, the credit rating calculation unit is continuously performed by the measurement data analysis unit. Based on the results of the analysis performed on the IoT device, the results of the analysis continuously performed by the owner analysis unit, and the results of the analysis continuously performed by the IoT device attribute analysis unit. The creditworthiness of the measurement data transmitted from may be continuously calculated.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記信用度算出部は、人工知能を利用することによって、前記ＩｏＴ機器から発信された前記測定データの信用度としての信用スコアを算出してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the credit rating calculation unit may calculate the credit score as the credit rating of the measurement data transmitted from the IoT device by using artificial intelligence.
　本発明の一態様は、ＩｏＴ機器信用度算出装置と、前記ＩｏＴ機器と、ネットワークとを備えるＩｏＴ機器信用度算出システムであって、前記ＩｏＴ機器信用度算出装置は、前記ＩｏＴ機器の所有、管理および製造のいずれも行っていない者によって所有または管理され、前記ＩｏＴ機器は、前記ネットワークを介して前記ＩｏＴ機器信用度算出装置の前記第１取得部と前記ＩｏＴ機器信用度算出装置以外の箇所とに前記測定データを自動的に発信可能に構成されている、ＩｏＴ機器信用度算出システムである。 One aspect of the present invention is an IoT device credit calculation system including an IoT device credit calculation device, the IoT device, and a network, wherein the IoT device credit calculation device owns, manages, and manufactures the IoT device. Owned or managed by a person who does neither, the IoT device transmits the measurement data to a location other than the first acquisition unit of the IoT device credit calculation device and the IoT device credit calculation device via the network. It is an IoT device credit rating calculation system that is configured to automatically make calls.
　本発明の一態様は、ＩｏＴ機器信用度算出装置の利用方法であって、前記信用度算出部によって算出された前記測定データの信用度が、前記ＩｏＴ機器の証明書の発行に利用される、ＩｏＴ機器信用度算出装置の利用方法である。 One aspect of the present invention is a method of using the IoT device credit rating device, in which the credit rating of the measurement data calculated by the credit rating calculation unit is used for issuing a certificate of the IoT device. This is how to use the calculation device.
　本発明の一態様は、ＩｏＴ機器信用度算出装置の利用方法であって、前記信用度算出部によって算出された前記測定データの信用度が、前記ＩｏＴ機器の所有者および／または管理者の格付け、および／または、前記ＩｏＴ機器の設置エリアの格付けに利用される、ＩｏＴ機器信用度算出装置の利用方法である。 One aspect of the present invention is a method of using the IoT device credit rating device, in which the credit rating of the measurement data calculated by the credit rating calculation unit is the rating of the owner and / or manager of the IoT device, and /. Alternatively, it is a method of using the IoT device credit rating calculation device used for rating the installation area of the IoT device.
　本発明の一態様は、センサを有するＩｏＴ機器から経時的に発信された測定データを取得する第１取得ステップと、前記ＩｏＴ機器から発信された前記測定データ以外の情報である非測定情報を取得する非測定情報取得ステップと、前記第１取得ステップにおいて取得された前記測定データと、前記非測定情報取得ステップにおいて取得された前記非測定情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する信用度算出ステップとを備えるＩｏＴ機器信用度算出方法である。 One aspect of the present invention is a first acquisition step of acquiring measurement data transmitted over time from an IoT device having a sensor, and acquisition of non-measurement information which is information other than the measurement data transmitted from the IoT device. The measurement transmitted from the IoT device based on the non-measurement information acquisition step, the measurement data acquired in the first acquisition step, and the non-measurement information acquired in the non-measurement information acquisition step. This is an IoT device credit rating calculation method including a credit rating calculation step for calculating the credit rating of data.
　本発明の一態様は、コンピュータに、センサを有するＩｏＴ機器から経時的に発信された測定データを取得する第１取得ステップと、前記ＩｏＴ機器から発信された前記測定データ以外の情報である非測定情報を取得する非測定情報取得ステップと、前記第１取得ステップにおいて取得された前記測定データと、前記非測定情報取得ステップにおいて取得された前記非測定情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する信用度算出ステップとを実行させるためのプログラムが記録され、前記コンピュータによって読み取り可能な記憶媒体である。 One aspect of the present invention is a first acquisition step of acquiring measurement data transmitted over time from an IoT device having a sensor on a computer, and non-measurement which is information other than the measurement data transmitted from the IoT device. It is transmitted from the IoT device based on the non-measurement information acquisition step for acquiring information, the measurement data acquired in the first acquisition step, and the non-measurement information acquired in the non-measurement information acquisition step. A storage medium in which a program for executing a credit rating calculation step for calculating the credit rating of the measured data is recorded and can be read by the computer.
　本発明の一態様は、ＩｏＴ機器から発信されたＩｏＴデータを受信するＩｏＴデータ受信部と、前記ＩｏＴデータ受信部によって受信された前記ＩｏＴデータに対する対価の根拠である第１対価を算出する第１対価算出部と、前記ＩｏＴデータ受信部によって受信された前記ＩｏＴデータを送信するＩｏＴデータ送信部と、前記ＩｏＴデータ送信部によって送信された前記ＩｏＴデータに対する対価の根拠である第２対価を算出する第２対価算出部と、前記ＩｏＴデータ受信部によって受信された前記ＩｏＴデータの信用度に関する情報を受信する信用度情報受信部とを備え、前記第１対価算出部は、前記信用度情報受信部によって受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第１対価を算出し、前記第２対価算出部は、前記信用度情報受信部によって受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第２対価を算出する、ＩｏＴデータプラットフォームである。 One aspect of the present invention is a first method of calculating a first consideration, which is a basis for consideration for the IoT data receiving unit that receives IoT data transmitted from the IoT device and the IoT data received by the IoT data receiving unit. The consideration calculation unit, the IoT data transmission unit that transmits the IoT data received by the IoT data reception unit, and the second consideration that is the basis of the consideration for the IoT data transmitted by the IoT data transmission unit are calculated. A second consideration calculation unit and a credit information receiving unit for receiving information on the credit rating of the IoT data received by the IoT data receiving unit are provided, and the first consideration calculation unit is received by the credit information receiving unit. The first consideration is calculated based on the information on the creditworthiness of the IoT data, and the second consideration calculation unit calculates the second consideration based on the information on the creditworthiness of the IoT data received by the creditworthiness information receiving unit. Is an IoT data platform that calculates.
　本発明の一態様のＩｏＴデータプラットフォームでは、前記ＩｏＴデータ受信部は、前記ＩｏＴデータ送信部によって送信された前記ＩｏＴデータを利用するＩｏＴデータ利用部の情報を秘匿化した状態で、前記ＩｏＴ機器から発信された前記ＩｏＴデータを所有していたＩｏＴデータ所有部から前記ＩｏＴデータを受信し、前記ＩｏＴデータ送信部は、前記ＩｏＴデータ所有部の情報を秘匿化した状態で、前記ＩｏＴデータ利用部に前記ＩｏＴデータを送信してもよい。 In the IoT data platform of one aspect of the present invention, the IoT data receiving unit is from the IoT device in a state where the information of the IoT data utilization unit that uses the IoT data transmitted by the IoT data transmitting unit is concealed. The IoT data is received from the IoT data possessing unit that possesses the transmitted IoT data, and the IoT data transmitting unit conceals the information of the IoT data possessing unit to the IoT data utilization unit. The IoT data may be transmitted.
　本発明の一態様は、ＩｏＴデータプラットフォームが、ＩｏＴ機器から発信されたＩｏＴデータを受信するＩｏＴデータ受信ステップと、前記ＩｏＴデータ受信ステップにおいて受信された前記ＩｏＴデータに対する対価の根拠である第１対価を算出する第１対価算出ステップと、前記ＩｏＴデータ受信ステップにおいて受信された前記ＩｏＴデータを送信するＩｏＴデータ送信ステップと、前記ＩｏＴデータ送信ステップにおいて送信された前記ＩｏＴデータに対する対価の根拠である第２対価を算出する第２対価算出ステップと、前記ＩｏＴデータ受信ステップにおいて受信された前記ＩｏＴデータの信用度に関する情報を受信する信用度情報受信ステップとを備え、前記第１対価算出ステップでは、前記信用度情報受信ステップにおいて受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第１対価を算出し、前記第２対価算出ステップでは、前記信用度情報受信ステップにおいて受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第２対価を算出する、ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法である。 One aspect of the present invention is the first consideration, which is the basis for the IoT data receiving step in which the IoT data platform receives the IoT data transmitted from the IoT device, and the consideration for the IoT data received in the IoT data receiving step. The first consideration calculation step for calculating the IoT data, the IoT data transmission step for transmitting the IoT data received in the IoT data reception step, and the basis for the consideration for the IoT data transmitted in the IoT data transmission step. The second consideration calculation step for calculating the two consideration and the credit rating information receiving step for receiving the information regarding the credit rating of the IoT data received in the IoT data receiving step are provided. In the first consideration calculation step, the credit rating information is provided. The first consideration is calculated based on the information on the creditworthiness of the IoT data received in the receiving step, and the second consideration calculation step is based on the information on the creditworthiness of the IoT data received in the creditworthiness information receiving step. This is a method of processing IoT data by the IoT data platform, which calculates the second consideration.
　本発明によれば、ＩｏＴ機器から発信された測定データの信用度を高精度に算出することができるＩｏＴ機器信用度算出装置、ＩｏＴ機器信用度算出システム、ＩｏＴ機器信用度算出装置の利用方法、ＩｏＴ機器信用度算出方法および記憶媒体を提供することができる。
　また、本発明によれば、ＩｏＴデータを利用する利便性を向上させることができるＩｏＴデータプラットフォーム、および、ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法を提供することができる。 According to the present invention, an IoT device credit calculation device, an IoT device credit calculation system, a method of using the IoT device credit calculation device, and an IoT device credit calculation that can calculate the credit rating of measurement data transmitted from an IoT device with high accuracy. Methods and storage media can be provided.
Further, according to the present invention, it is possible to provide an IoT data platform that can improve the convenience of using IoT data, and a method of processing IoT data by the IoT data platform.
第１実施形態のＩｏＴ機器信用度算出装置の概略構成の一例などを示す図である。It is a figure which shows an example of the schematic structure of the IoT device credit rating calculation device of 1st Embodiment. 第１実施形態のＩｏＴ機器信用度算出装置において実行される処理の一例を説明するためのフローチャートである。It is a flowchart for demonstrating an example of the process executed in the IoT apparatus credit degree calculation apparatus of 1st Embodiment. 図１に示す測定データ分析部がデータマイニング処理を実行し、信用度算出部が人工知能等を利用する場合におけるＩｏＴ機器信用度算出装置内の処理の一例を示す図である。It is a figure which shows an example of the process in the IoT device credit degree calculation apparatus when the measurement data analysis unit shown in FIG. 1 executes the data mining process, and the credit degree calculation unit uses artificial intelligence and the like. 測定データ分析部によって実行されるデータマイニング処理の一例を示す図である。It is a figure which shows an example of the data mining processing executed by the measurement data analysis unit. データマイニング処理を実行するニューラルネットワークの一例を示す図である。It is a figure which shows an example of the neural network which executes a data mining process. 第１実施形態のＩｏＴ機器信用度算出装置の信用度算出部において実行される処理の一例を示す図である。It is a figure which shows an example of the process executed in the credit degree calculation part of the IoT device credit degree calculation apparatus of 1st Embodiment. 第１実施形態のＩｏＴ機器信用度算出装置の信用度算出部が多変量解析を実行することによって信用スコアを算出した一例を示す図である。It is a figure which shows an example which calculated the credit score by the credit degree calculation part of the IoT device credit degree calculation apparatus of 1st Embodiment by performing a multivariate analysis. 第１実施形態のＩｏＴ機器信用度算出装置の信用度算出部が状態ポイント計算を実行することによって信用スコアを算出する処理の一例を説明するためのフローチャートである。It is a flowchart for demonstrating an example of the process of calculating the credit score by executing the state point calculation by the credit degree calculation part of the IoT device credit degree calculation apparatus of 1st Embodiment. 第１実施形態のＩｏＴ機器信用度算出装置の信用度算出部が状態ポイント計算を実行することによって信用スコアを算出した一例を示す図である。It is a figure which shows an example which calculated the credit score by the credit degree calculation part of the IoT device credit degree calculation apparatus of 1st Embodiment by executing the state point calculation. 第１実施形態のＩｏＴ機器信用度算出装置の第１適用例を示す図である。It is a figure which shows the 1st application example of the IoT device credit degree calculation apparatus of 1st Embodiment. 第１実施形態のＩｏＴ機器信用度算出装置の第２適用例を示す図である。It is a figure which shows the 2nd application example of the IoT device credit degree calculation apparatus of 1st Embodiment. 第１実施形態のＩｏＴ機器信用度算出装置の第２適用例のＩｏＴ機器信用度算出システムにおいて実行される処理の一例を示すフローチャートである。It is a flowchart which shows an example of the process executed in the IoT device credit degree calculation system of the 2nd application example of the IoT device credit degree calculation device of 1st Embodiment. 第１実施形態のＩｏＴ機器信用度算出装置の第３適用例を示す図である。It is a figure which shows the 3rd application example of the IoT device credit degree calculation apparatus of 1st Embodiment. 第２実施形態のＩｏＴデータプラットフォームの概略構成の一例などを示す図である。It is a figure which shows an example of the schematic structure of the IoT data platform of the 2nd Embodiment. 第２実施形態のＩｏＴデータプラットフォームにおいて実行される処理の一例を説明するためのフローチャートである。It is a flowchart for demonstrating an example of the process executed in the IoT data platform of 2nd Embodiment. 第２実施形態のＩｏＴデータプラットフォームが適用されたＩｏＴデータ流通システムの一例を示す図である。It is a figure which shows an example of the IoT data distribution system to which the IoT data platform of 2nd Embodiment is applied. 現状の問題点の一例を示す図である。It is a figure which shows an example of the present problem.
＜第１実施形態＞
　以下、添付図面を参照し、本発明のＩｏＴ機器信用度算出装置、ＩｏＴ機器信用度算出システム、ＩｏＴ機器信用度算出装置の利用方法、ＩｏＴ機器信用度算出方法および記憶媒体の実施形態について説明する。 <First Embodiment>
Hereinafter, with reference to the accompanying drawings, an IoT device credit calculation device, an IoT device credit calculation system, a method of using the IoT device credit calculation device, an IoT device credit calculation method, and an embodiment of a storage medium will be described.
　図１は第１実施形態のＩｏＴ機器信用度算出装置１の概略構成の一例などを示す図である。詳細には、図１（Ａ）は第１実施形態のＩｏＴ機器信用度算出装置１の概略構成の一例を示しており、図１（Ｂ）は第１実施形態のＩｏＴ機器信用度算出装置１とＩｏＴ機器Ｄとの関係の一例を示している。
　図１に示す例では、ＩｏＴ機器信用度算出装置１が、ＩｏＴ機器ＤのセンサＤ１によって測定されたデータの信用度を算出する。ＩｏＴ機器Ｄは、センサＤ１と、モニタリング部Ｄ２と、チェック部Ｄ３と、記憶部Ｄ４とを備えている。
　センサＤ１は、例えば水位、地盤、温度、湿度、風速、気圧などの気象データ等のようなデータを測定する。モニタリング部Ｄ２は、例えばＩｏＴ機器Ｄの消費電力、ＩｏＴ機器Ｄの演算部の温度、ＩｏＴ機器Ｄのケースの開閉状態などをモニタリングする。チェック部Ｄ３は、ＩｏＴ機器Ｄがソフト的および／またはハード的に改造されたか否か、ＩｏＴ機器Ｄの履歴、ＩｏＴ機器Ｄのログデータなどをチェックする。記憶部Ｄ４は、ＩｏＴ機器Ｄの所有者および／または管理者の情報である所有者情報と、ＩｏＴ機器Ｄの属性に関する情報である属性情報とを記憶する。ＩｏＴ機器Ｄの所有者および／または管理者には、個人のみならず、組織なども含まれる。ＩｏＴ機器Ｄの属性には、例えばＩｏＴ機器Ｄが持つセキュアの能力の度合い（セキュアレベル）、デバイス（ＩｏＴ機器Ｄ）が認証されているか否か等が含まれる。
　図１に示す例では、ＩｏＴ機器Ｄが、モニタリング部Ｄ２とチェック部Ｄ３と記憶部Ｄ４とを備えているが、他の例では、ＩｏＴ機器Ｄが、モニタリング部Ｄ２、チェック部Ｄ３および記憶部Ｄ４の少なくともいずれかを備えていなくてもよい。 FIG. 1 is a diagram showing an example of a schematic configuration of the IoT device credit rating calculation device 1 of the first embodiment. In detail, FIG. 1 (A) shows an example of a schematic configuration of the IoT device credit rating device 1 of the first embodiment, and FIG. 1 (B) shows the IoT device credit rating calculation device 1 and IoT of the first embodiment. An example of the relationship with the device D is shown.
In the example shown in FIG. 1, the IoT device credit rating device 1 calculates the credit rating of the data measured by the sensor D1 of the IoT device D. The IoT device D includes a sensor D1, a monitoring unit D2, a check unit D3, and a storage unit D4.
The sensor D1 measures data such as meteorological data such as water level, ground, temperature, humidity, wind speed, and atmospheric pressure. The monitoring unit D2 monitors, for example, the power consumption of the IoT device D, the temperature of the calculation unit of the IoT device D, the open / closed state of the case of the IoT device D, and the like. The check unit D3 checks whether or not the IoT device D has been modified in terms of software and / or hardware, the history of the IoT device D, the log data of the IoT device D, and the like. The storage unit D4 stores the owner information which is the information of the owner and / or the administrator of the IoT device D and the attribute information which is the information about the attribute of the IoT device D. The owner and / or manager of the IoT device D includes not only an individual but also an organization and the like. The attributes of the IoT device D include, for example, the degree of secure capability (secure level) of the IoT device D, whether or not the device (IoT device D) is authenticated, and the like.
In the example shown in FIG. 1, the IoT device D includes a monitoring unit D2, a check unit D3, and a storage unit D4, but in another example, the IoT device D is a monitoring unit D2, a check unit D3, and a storage unit. It does not have to have at least one of D4.
　図１に示す例では、ＩｏＴ機器信用度算出装置１が、第１取得部１１と、第２取得部１２と、第３取得部１３と、測定データ分析部１４と、所有者分析部１５と、ＩｏＴ機器属性分析部１６と、信用度算出部１７と、記憶部１８とを備えている。
　第１取得部１１は、ＩｏＴ機器Ｄから経時的に発信されたセンサＤ１の測定データを取得する。
　第２取得部１２は、上述した所有者情報をＩｏＴ機器Ｄから取得する。詳細には、第２取得部１２は、ＩｏＴ機器Ｄの記憶部Ｄ４に記憶されている所有者情報を取得する。第２取得部１２は、ＩｏＴ機器Ｄから発信された測定データ以外の情報である非測定情報を取得する非測定情報取得部として機能する。
　第２取得部１２は、例えば、ＩｏＴ機器Ｄの所有者および／または管理者の申告内容、過去の履歴、他のデータベース（図示せず）から得られた情報、調査結果から得られた情報などを、所有者情報として取得する。また、第２取得部１２は、例えば、ＩｏＴ機器Ｄの所有者および／または管理者がＩｏＴ機器Ｄに関する情報を発信しているか否か、ＩｏＴ機器Ｄの所有者および／または管理者が過去に発信したＩｏＴ機器Ｄに関する情報の内容、ＩｏＴ機器Ｄの所有者および／または管理者が所有または管理している他のＩｏＴ機器（図示せず）の管理状況、ＩｏＴ機器Ｄの所有者および／または管理者が他のＩｏＴ機器の廃棄処理を適切に実行しているか否か、ＩｏＴ機器Ｄの所有者および／または管理者の与信に関する情報、ＩｏＴ機器Ｄの所有者および／または管理者の管理体制に関する情報などを、所有者情報として取得する。 In the example shown in FIG. 1, the IoT device credit rating calculation device 1 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13, a measurement data analysis unit 14, and an owner analysis unit 15. It includes an IoT device attribute analysis unit 16, a credit rating calculation unit 17, and a storage unit 18.
The first acquisition unit 11 acquires the measurement data of the sensor D1 transmitted over time from the IoT device D.
The second acquisition unit 12 acquires the above-mentioned owner information from the IoT device D. Specifically, the second acquisition unit 12 acquires the owner information stored in the storage unit D4 of the IoT device D. The second acquisition unit 12 functions as a non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device D.
The second acquisition unit 12 has, for example, the contents of the declaration of the owner and / or administrator of the IoT device D, the past history, the information obtained from other databases (not shown), the information obtained from the survey results, and the like. Is acquired as owner information. Further, in the second acquisition unit 12, for example, whether or not the owner and / or administrator of the IoT device D has transmitted information regarding the IoT device D, the owner and / or the administrator of the IoT device D has in the past. The content of the transmitted information about the IoT device D, the management status of the owner and / or other IoT device (not shown) owned or managed by the owner of the IoT device D, the owner and / or the owner of the IoT device D. Whether or not the administrator is properly disposing of other IoT devices, information on the credit of the owner and / or administrator of IoT device D, the management system of the owner and / or administrator of IoT device D Get information about, etc. as owner information.
　第３取得部１３は、上述した属性情報をＩｏＴ機器Ｄから取得する。詳細には、第３取得部１３は、ＩｏＴ機器Ｄの記憶部Ｄ４に記憶されている属性情報を取得する。第３取得部１３も、ＩｏＴ機器Ｄから発信された測定データ以外の情報である非測定情報を取得する非測定情報取得部として機能する。
　第３取得部１３は、例えば、ＩｏＴ機器Ｄの製造元、ＩｏＴ機器Ｄの製造時期、ＩｏＴ機器Ｄの校正および／またはメンテナンスが実行されたか否か、ＩｏＴ機器Ｄの校正および／またはメンテナンスが実行された時期、ＩｏＴ機器Ｄの校正および／またはメンテナンスの内容、ＩｏＴ機器の応答、ＩｏＴ機器Ｄにセキュアチップ（図示せず）が備えられているか否か、ＩｏＴ機器の動作状況、ＩｏＴ機器Ｄに関する認証情報、ＩｏＴ機器Ｄの設置場所、ＩｏＴ機器Ｄの設置環境および／または測定環境、ＩｏＴ機器ＤのセンサＤ１によるデータの測定の難易度、ＩｏＴ機器Ｄによって発信された測定データの通信経路、ＩｏＴ機器Ｄにおける暗号化のレベルなどを、属性情報として取得する。
　また、第３取得部１３は、例えば、ＩｏＴ機器Ｄのモニタリング部Ｄ２によってモニタリングされたＩｏＴ機器Ｄの消費電力、ＩｏＴ機器Ｄの演算部の温度、ＩｏＴ機器Ｄのケースの開閉状態の情報などを、属性情報として取得する。
　また、第３取得部１３は、例えば、ＩｏＴ機器Ｄのチェック部Ｄ３によってチェックされたＩｏＴ機器Ｄがソフト的および／またはハード的に改造されたか否か、ＩｏＴ機器Ｄの履歴、ＩｏＴ機器Ｄのログデータなどを、属性情報として取得する。
　つまり、第３取得部１３によって取得される属性情報には、センサによって得られる情報（検出情報）と、センサを設ける必要なく得ることができる情報（非検出情報）とが含まれる。 The third acquisition unit 13 acquires the above-mentioned attribute information from the IoT device D. Specifically, the third acquisition unit 13 acquires the attribute information stored in the storage unit D4 of the IoT device D. The third acquisition unit 13 also functions as a non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device D.
The third acquisition unit 13 is, for example, the manufacturer of the IoT device D, the manufacturing time of the IoT device D, whether or not the IoT device D has been calibrated and / or maintained, and the IoT device D has been calibrated and / or maintained. Time, IoT device D calibration and / or maintenance details, IoT device response, whether IoT device D is equipped with a secure chip (not shown), IoT device operating status, IoT device D certification Information, installation location of IoT device D, installation environment and / or measurement environment of IoT device D, difficulty of measuring data by sensor D1 of IoT device D, communication path of measurement data transmitted by IoT device D, IoT device The level of encryption in D is acquired as attribute information.
Further, the third acquisition unit 13 obtains, for example, the power consumption of the IoT device D monitored by the monitoring unit D2 of the IoT device D, the temperature of the calculation unit of the IoT device D, information on the open / closed state of the case of the IoT device D, and the like. , Get as attribute information.
Further, the third acquisition unit 13 determines, for example, whether or not the IoT device D checked by the check unit D3 of the IoT device D has been modified in software and / or hardware, the history of the IoT device D, and the history of the IoT device D. Acquire log data etc. as attribute information.
That is, the attribute information acquired by the third acquisition unit 13 includes information obtained by the sensor (detection information) and information that can be obtained without the need to provide a sensor (non-detection information).
　図１に示す例では、ＩｏＴ機器信用度算出装置１が、非測定情報取得部として、第２取得部１２および第３取得部１３の両方を備えているが、他の例では、ＩｏＴ機器信用度算出装置１が、非測定情報取得部として、第２取得部１２および第３取得部１３の一方のみを備えていてもよい。
　更に他の例では、上述した所有者情報が、属性情報に含まれるものとして扱われてもよい。この例では、ＩｏＴ機器信用度算出装置１が、非測定情報取得部として、第３取得部１３を備えており（つまり、第２取得部１２を備えておらず）、第３取得部１３は、属性情報に含まれる所有者情報も取得する。 In the example shown in FIG. 1, the IoT device credit rating device 1 includes both the second acquisition section 12 and the third acquisition section 13 as the non-measurement information acquisition section, but in another example, the IoT device credit rating calculation The device 1 may include only one of the second acquisition unit 12 and the third acquisition unit 13 as the non-measurement information acquisition unit.
In yet another example, the owner information described above may be treated as being included in the attribute information. In this example, the IoT device credit rating calculation device 1 includes a third acquisition unit 13 as a non-measurement information acquisition unit (that is, does not include a second acquisition unit 12), and the third acquisition unit 13 is The owner information included in the attribute information is also acquired.
　図１に示す例では、測定データ分析部１４は、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データの分析を実行する。測定データ分析部１４は、例えば、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、第１取得部１１によって過去に取得された過去分測定データ（ＩｏＴ機器ＤのセンサＤ１によって過去に測定されたデータ）との比較を実行する。また、測定データ分析部１４は、例えば、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、ＩｏＴ機器Ｄ以外のＩｏＴ機器から発信されたデータ（ＩｏＴ機器Ｄ以外のＩｏＴ機器のセンサによって測定されたデータ）との比較を実行する。また、測定データ分析部１４は、例えば、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データに対する人為的な操作の痕跡の有無の分析を実行する。また、測定データ分析部１４は、例えば、後述するように、ＩｏＴ機器ＤのセンサＤ１の測定データの変化の特徴を図形化した後にデータマイニング処理を実行することによって、ＩｏＴ機器ＤのセンサＤ１の測定データの分析を実行する。 In the example shown in FIG. 1, the measurement data analysis unit 14 analyzes the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. The measurement data analysis unit 14 may, for example, measure the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the past measurement data (sensor of the IoT device D) acquired in the past by the first acquisition unit 11. A comparison with the data previously measured by D1) is performed. Further, the measurement data analysis unit 14 has, for example, the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the data transmitted from the IoT device other than the IoT device D (IoT other than the IoT device D). Perform a comparison with the data measured by the sensor of the instrument. Further, the measurement data analysis unit 14 analyzes, for example, the presence or absence of traces of artificial operation on the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. Further, for example, as will be described later, the measurement data analysis unit 14 performs a data mining process after graphicizing the characteristics of changes in the measurement data of the sensor D1 of the IoT device D, so that the sensor D1 of the IoT device D Perform an analysis of the measurement data.
　所有者分析部１５は、第２取得部１２によってＩｏＴ機器Ｄから取得されたＩｏＴ機器Ｄの所有者情報に基づいて、ＩｏＴ機器Ｄの所有者および／または管理者の分析を実行する。
　所有者分析部１５は、例えば、ＩｏＴ機器Ｄの所有者および／または管理者の申告内容、過去の履歴、他のデータベースから得られた情報、調査結果から得られた情報などに基づいて、ＩｏＴ機器Ｄの所有者および／または管理者が、ＩｏＴ機器Ｄの正規の所有者および／または管理者であるか否かを分析する。
　また、所有者分析部１５は、例えば、ＩｏＴ機器Ｄの所有者および／または管理者がＩｏＴ機器Ｄに関する情報を発信しているか否か、ＩｏＴ機器Ｄの所有者および／または管理者が過去に発信したＩｏＴ機器Ｄに関する情報の内容、ＩｏＴ機器Ｄの所有者および／または管理者が所有または管理している他のＩｏＴ機器の管理状況、ＩｏＴ機器Ｄの所有者および／または管理者が他のＩｏＴ機器の廃棄処理を適切に実行しているか否か、ＩｏＴ機器Ｄの所有者および／または管理者の与信に関する情報、ＩｏＴ機器Ｄの所有者および／または管理者の管理体制に関する情報などに基づいて、ＩｏＴ機器Ｄの所有者および／または管理者の分析を実行する。 The owner analysis unit 15 analyzes the owner and / or manager of the IoT device D based on the owner information of the IoT device D acquired from the IoT device D by the second acquisition unit 12.
The owner analysis unit 15 is based on, for example, the contents of the declaration of the owner and / or manager of the IoT device D, the past history, the information obtained from other databases, the information obtained from the survey results, and the like. Analyze whether the owner and / or administrator of device D is the authorized owner and / or administrator of IoT device D.
Further, in the owner analysis unit 15, for example, whether or not the owner and / or the manager of the IoT device D has transmitted information about the IoT device D, the owner and / or the manager of the IoT device D has in the past. The content of the transmitted information about IoT device D, the management status of other IoT devices owned or managed by the owner and / or administrator of IoT device D, and the owner and / or administrator of IoT device D are other Based on information on the credit of the owner and / or manager of IoT device D, information on the management system of the owner and / or manager of IoT device D, etc., whether or not the disposal of IoT device is properly executed. And perform an analysis of the owner and / or administrator of the IoT device D.
　ＩｏＴ機器属性分析部１６は、第３取得部１３によって取得された属性情報（ＩｏＴ機器Ｄの属性に関する情報）に基づいて、ＩｏＴ機器Ｄの属性の分析を実行する。ＩｏＴ機器属性分析部１６は、例えば、ＩｏＴ機器Ｄの製造元、ＩｏＴ機器Ｄの製造時期、ＩｏＴ機器Ｄの校正および／またはメンテナンスが実行されたか否か、ＩｏＴ機器Ｄの校正および／またはメンテナンスが実行された時期、ＩｏＴ機器Ｄの校正および／またはメンテナンスの内容、ＩｏＴ機器の応答、ＩｏＴ機器Ｄにセキュアチップ（図示せず）が備えられているか否か、ＩｏＴ機器の動作状況、ＩｏＴ機器Ｄに関する認証情報、ＩｏＴ機器Ｄの設置場所、ＩｏＴ機器Ｄの設置環境および／または測定環境、ＩｏＴ機器ＤのセンサＤ１によるデータの測定の難易度、ＩｏＴ機器Ｄによって発信された測定データの通信経路、ＩｏＴ機器Ｄにおける暗号化のレベルなどに基づいて、ＩｏＴ機器Ｄの属性の分析を実行する。
　また、ＩｏＴ機器属性分析部１６は、例えば、第３取得部１３によって取得されたＩｏＴ機器Ｄの消費電力、ＩｏＴ機器Ｄの演算部の温度、ＩｏＴ機器Ｄのケースの開閉状態の情報などに基づいて、ＩｏＴ機器Ｄの属性の分析を実行する。
　また、ＩｏＴ機器属性分析部１６は、例えば、第３取得部１３によって取得されたＩｏＴ機器Ｄがソフト的および／またはハード的に改造されたか否か、ＩｏＴ機器Ｄの履歴、ＩｏＴ機器Ｄのログデータなどに基づいて、ＩｏＴ機器Ｄの属性の分析を実行する。 The IoT device attribute analysis unit 16 analyzes the attributes of the IoT device D based on the attribute information (information about the attributes of the IoT device D) acquired by the third acquisition unit 13. The IoT device attribute analysis unit 16 determines, for example, the manufacturer of the IoT device D, the manufacturing time of the IoT device D, whether or not the IoT device D has been calibrated and / or maintained, and the IoT device D has been calibrated and / or maintained. Regarding the time when the IoT device D was calibrated and / or the contents of maintenance, the response of the IoT device, whether or not the IoT device D is equipped with a secure chip (not shown), the operating status of the IoT device, and the IoT device D. Authentication information, installation location of IoT device D, installation environment and / or measurement environment of IoT device D, difficulty of measuring data by sensor D1 of IoT device D, communication path of measurement data transmitted by IoT device D, IoT The attribute analysis of the IoT device D is performed based on the level of encryption in the device D and the like.
Further, the IoT device attribute analysis unit 16 is based on, for example, the power consumption of the IoT device D acquired by the third acquisition unit 13, the temperature of the calculation unit of the IoT device D, the information on the open / closed state of the case of the IoT device D, and the like. Then, the attribute of the IoT device D is analyzed.
Further, the IoT device attribute analysis unit 16 determines, for example, whether or not the IoT device D acquired by the third acquisition unit 13 has been modified in terms of software and / or hardware, the history of the IoT device D, and the log of the IoT device D. Analyze the attributes of the IoT device D based on the data and the like.
　信用度算出部１７は、測定データ分析部１４の分析結果と所有者分析部１５の分析結果とＩｏＴ機器属性分析部１６の分析結果とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する。つまり、信用度算出部１７は、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、第２取得部１２によって取得された所有者情報（ＩｏＴ機器Ｄの所有者および／または管理者の情報）と、第３取得部１３によって取得された属性情報（ＩｏＴ機器Ｄの属性に関する情報）とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する。
　ＩｏＴ機器信用度算出装置１が、非測定情報取得部として、第２取得部１２および第３取得部１３の一方のみを備えている例では、信用度算出部１７が、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、非測定情報取得部によって取得された非測定情報とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する。 The credit rating calculation unit 17 is based on the analysis result of the measurement data analysis unit 14, the analysis result of the owner analysis unit 15, and the analysis result of the IoT device attribute analysis unit 16, and the measurement data of the sensor D1 transmitted from the IoT device D. Calculate the creditworthiness of. That is, the credit rating calculation unit 17 has the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 and the owner information (owner and / or of the IoT device D) acquired by the second acquisition unit 12. The credit rating of the measurement data of the sensor D1 transmitted from the IoT device D is calculated based on the information of the administrator) and the attribute information (information about the attributes of the IoT device D) acquired by the third acquisition unit 13.
In the example in which the IoT device credit calculation device 1 includes only one of the second acquisition unit 12 and the third acquisition unit 13 as the non-measurement information acquisition unit, the credit calculation unit 17 is acquired by the first acquisition unit 11. Based on the measurement data of the sensor D1 of the IoT device D and the non-measurement information acquired by the non-measurement information acquisition unit, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated.
　図１に示す例では、信用度算出部１７は、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての数値またはランクを算出する。
　信用度算出部１７は、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出した後においても、測定データ分析部１４によって継続的に実行された分析の結果と、所有者分析部１５によって継続的に実行された分析の結果と、ＩｏＴ機器属性分析部１６によって継続的に実行された分析の結果とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を継続的に算出する。
　図１に示した例ではＩｏＴ機器Ｄのモニタリングおよび／またはチェックは、ＩｏＴ機器Ｄの機能の一部として説明を行なったが、その限りではなく、他の装置によってＩｏＴ機器Ｄのモニタリングおよび／またはチェックを行い、その情報をＩｏＴ機器信用度算出装置に入力してもよい。
　また、ＩｏＴ機器Ｄの所有者情報やＩｏＴ機器の属性データをＩｏＴ機器から直接取得するとしたが、その限りではなく、別途保存されたデータを第２取得部及び第３取得部から取得してもよい。 In the example shown in FIG. 1, the credit rating calculation unit 17 calculates a numerical value or rank as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D.
Even after the credit rating calculation unit 17 calculates the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D, the result of the analysis continuously executed by the measurement data analysis unit 14 and the owner analysis unit 15 Based on the result of the continuously executed analysis and the result of the analysis continuously executed by the IoT device attribute analysis unit 16, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is continuously determined. calculate.
In the example shown in FIG. 1, monitoring and / or checking of IoT device D has been described as part of the function of IoT device D, but is not limited to this, and monitoring and / or checking of IoT device D by other devices. The check may be performed and the information may be input to the IoT device credit rating calculation device.
Further, the owner information of the IoT device D and the attribute data of the IoT device are acquired directly from the IoT device, but this is not the case, and even if the separately saved data is acquired from the second acquisition unit and the third acquisition unit. Good.
　図１に示す例では、信用度算出部１７が、人工知能を利用することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出する。
　他の例では、信用度算出部１７が、後述する多変量解析を実行することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出してもよい。
　更に他の例では、信用度算出部１７が、後述する状態ポイント計算を実行することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出してもよい。 In the example shown in FIG. 1, the credit rating calculation unit 17 calculates the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by using artificial intelligence.
In another example, the credit rating calculation unit 17 may calculate the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by executing the multivariate analysis described later.
In yet another example, the credit rating calculation unit 17 may calculate the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by executing the state point calculation described later.
　図１に示す例では、記憶部１８が、第１取得部１１によって取得された測定データと、第２取得部１２によって取得された所有者情報と、第３取得部１３によって取得された属性情報と、測定データ分析部１４の分析結果と、所有者分析部１５の分析結果と、ＩｏＴ機器属性分析部１６の分析結果と、信用度算出部１７によって算出されたＩｏＴ機器ＤのセンサＤ１の測定データの信用度とを記憶する。 In the example shown in FIG. 1, the storage unit 18 has the measurement data acquired by the first acquisition unit 11, the owner information acquired by the second acquisition unit 12, and the attribute information acquired by the third acquisition unit 13. , The analysis result of the measurement data analysis unit 14, the analysis result of the owner analysis unit 15, the analysis result of the IoT device attribute analysis unit 16, and the measurement data of the sensor D1 of the IoT device D calculated by the credit rating calculation unit 17. Memorize the creditworthiness of.
　図２は第１実施形態のＩｏＴ機器信用度算出装置１において実行される処理の一例を説明するためのフローチャートである。
　図２に示す例では、ステップＳ１１において、第１取得部１１が、ＩｏＴ機器Ｄから経時的に発信されたセンサＤ１の測定データを取得する。
　また、ステップＳ１２において、第２取得部１２が、ＩｏＴ機器Ｄの所有者および／または管理者の情報である所有者情報をＩｏＴ機器Ｄから取得する。
　また、ステップＳ１３において、第３取得部１３が、ＩｏＴ機器Ｄの属性に関する情報である属性情報をＩｏＴ機器Ｄから取得する。 FIG. 2 is a flowchart for explaining an example of processing executed by the IoT device credit rating calculation device 1 of the first embodiment.
In the example shown in FIG. 2, in step S11, the first acquisition unit 11 acquires the measurement data of the sensor D1 transmitted from the IoT device D over time.
Further, in step S12, the second acquisition unit 12 acquires the owner information, which is the information of the owner and / or the manager of the IoT device D, from the IoT device D.
Further, in step S13, the third acquisition unit 13 acquires the attribute information which is the information regarding the attribute of the IoT device D from the IoT device D.
　次いで、ステップＳ１４では、測定データ分析部１４が、ステップＳ１１において取得されたＩｏＴ機器ＤのセンサＤ１の測定データの分析を実行する。
　また、ステップＳ１５では、所有者分析部１５が、ステップＳ１２において取得されたＩｏＴ機器Ｄの所有者情報（ＩｏＴ機器Ｄの所有者および／または管理者の情報）に基づいて、ＩｏＴ機器Ｄの所有者および／または管理者の分析を実行する。
　また、ステップＳ１６では、ＩｏＴ機器属性分析部１６が、ステップＳ１３において取得された属性情報（ＩｏＴ機器Ｄの属性に関する情報）に基づいて、ＩｏＴ機器Ｄの属性の分析を実行する。 Next, in step S14, the measurement data analysis unit 14 analyzes the measurement data of the sensor D1 of the IoT device D acquired in step S11.
Further, in step S15, the owner analysis unit 15 owns the IoT device D based on the owner information of the IoT device D (information on the owner and / or manager of the IoT device D) acquired in step S12. Perform a person and / or administrator analysis.
Further, in step S16, the IoT device attribute analysis unit 16 analyzes the attributes of the IoT device D based on the attribute information (information about the attributes of the IoT device D) acquired in step S13.
　次いで、ステップＳ１７では、信用度算出部１７が、ステップＳ１４における分析の結果とステップＳ１５における分析の結果とステップＳ１６における分析の結果とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する。
　つまり、ステップＳ１７では、信用度算出部１７が、ステップＳ１１において取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、ステップＳ１２において取得された所有者情報と、ステップＳ１３において取得された属性情報とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する。
　なお、ステップＳ１１からステップＳ１７は、この順で行う必要はなく、適宜入れ替えておこなってよい。 Next, in step S17, the credit rating calculation unit 17 obtains the measurement data of the sensor D1 transmitted from the IoT device D based on the analysis result in step S14, the analysis result in step S15, and the analysis result in step S16. Calculate credit.
That is, in step S17, the credit rating calculation unit 17 adds the measurement data of the sensor D1 of the IoT device D acquired in step S11, the owner information acquired in step S12, and the attribute information acquired in step S13. Based on this, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D is calculated.
It is not necessary to perform steps S11 to S17 in this order, and they may be replaced as appropriate.
　上述したように、第１実施形態のＩｏＴ機器信用度算出装置１では、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する場合に、ＩｏＴ機器ＤのセンサＤ１の測定データが考慮されるのみならず、ＩｏＴ機器Ｄの所有者および／または管理者の情報およびＩｏＴ機器Ｄの属性に関する情報も考慮される。
　そのため、第１実施形態のＩｏＴ機器信用度算出装置１では、ＩｏＴ機器Ｄの所有者および／または管理者の情報およびＩｏＴ機器Ｄの属性に関する情報に基づくことなく、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度が算出される場合よりも、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を高精度に算出することができる。
　詳細には、特許文献１に記載された技術では、第二センサによって第一センサの挙動が止められるために、第一センサの情報を利用できなくなってしまうのに対し、第１実施形態のＩｏＴ機器信用度算出装置１では、信用度算出部１７によって算出されるセンサＤ１の測定データの信用度に応じてデータの利用を適当に判断することができる。 As described above, in the IoT device credit rating calculation device 1 of the first embodiment, the measurement data of the sensor D1 of the IoT device D is taken into consideration when calculating the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D. Not only that, information about the owner and / or administrator of the IoT device D and information about the attributes of the IoT device D are also considered.
Therefore, in the IoT device credit rating calculation device 1 of the first embodiment, the sensor D1 transmitted from the IoT device D is not based on the information of the owner and / or the manager of the IoT device D and the information regarding the attributes of the IoT device D. It is possible to calculate the reliability of the measurement data of the sensor D1 transmitted from the IoT device D with higher accuracy than the case where the reliability of the measurement data of is calculated.
Specifically, in the technique described in Patent Document 1, since the behavior of the first sensor is stopped by the second sensor, the information of the first sensor cannot be used, whereas the IoT of the first embodiment cannot be used. The device credit rating calculation device 1 can appropriately determine the use of the data according to the credit rating of the measurement data of the sensor D1 calculated by the credit rating calculation unit 17.
　また、第１実施形態のＩｏＴ機器信用度算出装置１では、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出する場合に、ＩｏＴ機器ＤのセンサＤ１の測定データの分析が実行されるのみならず、ＩｏＴ機器Ｄの所有者および／または管理者の分析およびＩｏＴ機器Ｄの属性の分析も実行される。
　そのため、第１実施形態のＩｏＴ機器信用度算出装置１では、ＩｏＴ機器Ｄの所有者および／または管理者の分析とＩｏＴ機器Ｄの属性の分析とが実行されることなく、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度が算出される場合よりも、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を高精度に算出することができる。 Further, in the IoT device credit rating calculation device 1 of the first embodiment, when calculating the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D, the analysis of the measurement data of the sensor D1 of the IoT device D is executed. Not only that, the analysis of the owner and / or administrator of the IoT device D and the analysis of the attributes of the IoT device D are also performed.
Therefore, in the IoT device credit rating calculation device 1 of the first embodiment, the analysis of the owner and / or the manager of the IoT device D and the analysis of the attributes of the IoT device D are not executed, and the data is transmitted from the IoT device D. It is possible to calculate the reliability of the measurement data of the sensor D1 transmitted from the IoT device D with higher accuracy than when the reliability of the measurement data of the sensor D1 is calculated.
　また、第１実施形態のＩｏＴ機器信用度算出装置１では、信用度算出部１７が、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出した後においても、測定データ分析部１４によって継続的に実行された分析の結果と、所有者分析部１５によって継続的に実行された分析の結果と、ＩｏＴ機器属性分析部１６によって継続的に実行された分析の結果とに基づいて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を継続的に算出する。
　そのため、第１実施形態のＩｏＴ機器信用度算出装置１では、測定データ分析部１４の分析結果、所有者分析部１５の分析結果およびＩｏＴ機器属性分析部１６の分析結果のいずれかが変化した場合であっても、その変化を反映させて、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度を算出することができる。 Further, in the IoT device credit rating calculation device 1 of the first embodiment, even after the credit rating calculation unit 17 calculates the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D, the measurement data analysis unit 14 continues. Based on the results of the analysis performed in, the results of the analysis continuously performed by the owner analysis unit 15, and the results of the analysis continuously performed by the IoT device attribute analysis unit 16, the IoT device D The credit rating of the measurement data of the sensor D1 transmitted from is continuously calculated.
Therefore, in the IoT device credit rating calculation device 1 of the first embodiment, when any of the analysis result of the measurement data analysis unit 14, the analysis result of the owner analysis unit 15, and the analysis result of the IoT device attribute analysis unit 16 changes. Even if there is, the reliability of the measurement data of the sensor D1 transmitted from the IoT device D can be calculated by reflecting the change.
　次に、上述した第１実施形態のＩｏＴ機器信用度算出装置１を適用する場合に考慮すべき点について説明する。
　ＩｏＴ機器ＤのセンサＤ１の測定データの利用者が、ＩｏＴ機器Ｄから発信されるセンサＤ１の測定データの信用度合いを決定するためには、悪意がある第三者によるネットワーク等を介した攻撃以外に、（１）ＩｏＴ機器Ｄそのものが信用に値する否かというモノに関する確認、（２）ＩｏＴ機器Ｄを管理するもしくは所有する人が信用に値する否かの確認、（３）ＩｏＴ機器Ｄが正しく設置されているかどうかの確認を行って判断することが必要となる。
　これらの事柄は、一度、ＩｏＴ機器Ｄの信用の度合いを確認すればよいものではない。例えば、上記（１）については、ＩｏＴ機器Ｄの劣化、ＩｏＴ機器Ｄのメンテナンスの不備などによって、ＩｏＴ機器Ｄの信用の度合いが変化することが考えられる。ＩｏＴ機器Ｄの改造などが行われ、ＩｏＴ機器Ｄの信用の度合いが変化する可能性もある。上記（２）については、ＩｏＴ機器Ｄの管理者に、当初問題がなくても、ＩｏＴ機器Ｄの管理者による管理体制の不備や、ＩｏＴ機器Ｄの管理者が金銭的な誘惑に負け、測定データの利用者へ悪意を持った誘導を行うなどの様々な理由で、ＩｏＴ機器Ｄから偽の情報が発信される可能性は否定できない。上記（３）については、ＩｏＴ機器ＤのセンサＤ１自体が正常であっても、センサＤ１の設置環境周辺の変化（例えば、センサＤ１が温度センサである場合、センサＤ１に直射日光が当たらない状況から、センサＤ１に直射日光が当たる状況への変化など）によって、ＩｏＴ機器Ｄから正しいとは言えないセンサＤ１の測定データが発信されてしまうことも考えられる。そのため、信用度合いの確認は、継続的に繰り返して行うことが望ましい。 Next, points to be considered when applying the IoT device credit rating calculation device 1 of the first embodiment described above will be described.
In order for the user of the measurement data of the sensor D1 of the IoT device D to determine the degree of credibility of the measurement data of the sensor D1 transmitted from the IoT device D, other than an attack by a malicious third party via a network or the like. In addition, (1) confirmation of whether the IoT device D itself is worthy of credit, (2) confirmation of whether the person who manages or owns the IoT device D is worthy of credit, (3) confirmation of whether the IoT device D is correct It is necessary to confirm whether it is installed and make a judgment.
For these matters, it is not enough to confirm the degree of credit of the IoT device D once. For example, with respect to the above (1), it is conceivable that the degree of trust of the IoT device D changes due to deterioration of the IoT device D, inadequate maintenance of the IoT device D, and the like. There is a possibility that the degree of credibility of the IoT device D may change due to the modification of the IoT device D or the like. Regarding (2) above, even if the manager of IoT device D has no problem at the beginning, the management system of the manager of IoT device D is inadequate, and the manager of IoT device D loses financial temptation and measures. It cannot be denied that fake information may be transmitted from the IoT device D for various reasons such as malicious guidance to data users. Regarding (3) above, even if the sensor D1 of the IoT device D itself is normal, changes around the installation environment of the sensor D1 (for example, when the sensor D1 is a temperature sensor, the sensor D1 is not exposed to direct sunlight). Therefore, it is conceivable that the measurement data of the sensor D1 that cannot be said to be correct may be transmitted from the IoT device D due to a change in the situation where the sensor D1 is exposed to direct sunlight. Therefore, it is desirable to check the credit rating continuously and repeatedly.
　図３は図１に示す測定データ分析部１４がデータマイニング処理を実行し、信用度算出部１７が人工知能等を利用する場合におけるＩｏＴ機器信用度算出装置１内の処理の一例を示す図である。
　図３に示す例では、「ＩｏＴ機器のデータ」（ＩｏＴ機器Ｄから経時的に発信されたセンサＤ１の測定データ）と、「所有者・管理者情報」（ＩｏＴ機器Ｄの所有者および／または管理者の情報）と、「デバイス情報」（ＩｏＴ機器Ｄの属性に関する情報（属性情報））とがＩｏＴ機器信用度算出装置１に入力される。
　「ＩｏＴ機器のデータ」は、例えばＩｏＴ機器Ｄから配信される時系列データなどである。「所有者・管理者情報」は、例えば登録情報、管理体制、過去のデータ配信情報などであり、後述するポジティブポイントに相当する。「デバイス情報」は、例えば登録情報、通信経路の安全度、製造元、製造日、校正の状況などである。
　「データマイニング等による状態分析」は、測定データ分析部１４（図１参照）によって実行される。また、「ＡＩ（人工知能）等による信用スコア算出」および「信用度判断」が、信用度算出部１７（図１参照）によって実行される。 FIG. 3 is a diagram showing an example of processing in the IoT device credit rating device 1 when the measurement data analysis unit 14 shown in FIG. 1 executes a data mining process and the credit rating calculation unit 17 uses artificial intelligence or the like.
In the example shown in FIG. 3, "IoT device data" (measurement data of the sensor D1 transmitted from the IoT device D over time) and "owner / administrator information" (owner and / or of the IoT device D). (Administrator information) and "device information" (information about the attributes of the IoT device D (attribute information)) are input to the IoT device credit rating calculation device 1.
The “IoT device data” is, for example, time-series data distributed from the IoT device D. "Owner / administrator information" is, for example, registration information, management system, past data distribution information, etc., and corresponds to a positive point described later. "Device information" includes, for example, registration information, communication path security, manufacturer, date of manufacture, calibration status, and the like.
The "state analysis by data mining or the like" is executed by the measurement data analysis unit 14 (see FIG. 1). In addition, "credit score calculation by AI (artificial intelligence)" and "credit degree determination" are executed by the credit degree calculation unit 17 (see FIG. 1).
　図３に示す例では、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの確からしさ（測定データ分析部１４の分析結果）と、ＩｏＴ機器Ｄを所有および／または管理するヒトや組織の情報と、ＩｏＴ機器Ｄそのものに関する情報とから、ＡＩなどを用いて信用スコア（信用度の度合い）が算出される。ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの確からしさは、データマイニングなどの処理を実行することによって得られる。 In the example shown in FIG. 3, the certainty of the measurement data of the sensor D1 transmitted from the IoT device D (analysis result of the measurement data analysis unit 14) and the information of the person or tissue that owns and / or manages the IoT device D. , The credit score (degree of credit rating) is calculated using AI or the like from the information about the IoT device D itself. The certainty of the measurement data of the sensor D1 transmitted from the IoT device D can be obtained by executing a process such as data mining.
　ＩｏＴ機器Ｄの所有者および／または管理者の情報としては、ＩｏＴ機器Ｄの所有者および／または管理者の申告内容、過去の履歴、他データベースや調査結果などから得られた情報から、ＩｏＴ機器Ｄの正規の所有者および／または管理者かどうか？　所有および／または管理するＩｏＴ機器Ｄに関して情報を発信しているか？　ＩｏＴ機器Ｄの廃棄処理を適切に実行しているか？　過去に発信した情報の内容、所有および／または管理している他のＩｏＴ機器の管理状況、資格、与信、管理体制などがある。
　つまり、所有者分析部１５（図１参照）は、ＩｏＴ機器Ｄの所有者および／または管理者の申告内容、過去の履歴、他のデータベースから得られた情報、調査結果から得られた情報などに基づいて、ＩｏＴ機器Ｄの所有者および／または管理者が、ＩｏＴ機器Ｄの正規の所有者および／または管理者であるか否かを分析する。
　また、所有者分析部１５は、ＩｏＴ機器Ｄの所有者および／または管理者がＩｏＴ機器Ｄに関する情報を発信しているか否か、ＩｏＴ機器Ｄの所有者および／または管理者が過去に発信したＩｏＴ機器Ｄに関する情報の内容、ＩｏＴ機器Ｄの所有者および／または管理者が所有または管理している他のＩｏＴ機器の管理状況、ＩｏＴ機器Ｄの所有者および／または管理者が他のＩｏＴ機器の廃棄処理を適切に実行しているか否か、ＩｏＴ機器Ｄの所有者および／または管理者の与信に関する情報、ＩｏＴ機器Ｄの所有者および／または管理者の管理体制に関する情報などに基づいて、ＩｏＴ機器Ｄの所有者および／または管理者の分析を実行する。 Information on the owner and / or administrator of IoT device D includes the contents of the declaration of the owner and / or administrator of IoT device D, past history, information obtained from other databases and survey results, etc. Is D an authorized owner and / or administrator? Are you disseminating information about the IoT device D that you own and / or manage? Is the disposal process of IoT device D properly executed? It includes the content of information transmitted in the past, the management status of other IoT devices owned and / or managed, qualifications, credit, management system, etc.
That is, the owner analysis unit 15 (see FIG. 1) has the contents of the declaration of the owner and / or the manager of the IoT device D, the past history, the information obtained from other databases, the information obtained from the survey results, and the like. Based on, it is analyzed whether the owner and / or administrator of the IoT device D is a legitimate owner and / or administrator of the IoT device D.
Further, in the owner analysis unit 15, whether or not the owner and / or the manager of the IoT device D has transmitted information regarding the IoT device D has been transmitted by the owner and / or the manager of the IoT device D in the past. Contents of information about IoT device D, management status of other IoT devices owned or managed by the owner and / or administrator of IoT device D, owner and / or administrator of IoT device D is another IoT device Based on information on the credit of the owner and / or manager of IoT device D, information on the management system of the owner and / or manager of IoT device D, etc., whether or not the disposal process is properly executed. Perform an analysis of the owner and / or administrator of IoT device D.
　ＩｏＴ機器Ｄの情報としては、ＩｏＴ機器Ｄの製造元、製造時期、ＩｏＴ機器Ｄの校正の有無と時期、ＩｏＴ機器Ｄの応答、セキュアチップの有無、動作状況、各種の認証情報、設置場所、設置環境、ＩｏＴ機器ＤのセンサＤ１が測定するデータの難易度、通信経路、暗号化のレベルなどがある、これらの情報は、ＩｏＴ機器Ｄの機種、使っているデバイスの種類、通信回線を通した反応などから得ることができる。
　つまり、ＩｏＴ機器属性分析部１６（図１参照）は、ＩｏＴ機器Ｄの製造元、ＩｏＴ機器Ｄの製造時期、ＩｏＴ機器Ｄの校正および／またはメンテナンスが実行されたか否か、ＩｏＴ機器Ｄの校正および／またはメンテナンスが実行された時期、ＩｏＴ機器Ｄの校正および／またはメンテナンスの内容、ＩｏＴ機器の応答、ＩｏＴ機器Ｄにセキュアチップが備えられているか否か、ＩｏＴ機器の動作状況、ＩｏＴ機器Ｄに関する認証情報、ＩｏＴ機器Ｄの設置場所、ＩｏＴ機器Ｄの設置環境および／または測定環境、ＩｏＴ機器ＤのセンサＤ１によるデータの測定の難易度、ＩｏＴ機器Ｄによって発信されたセンサＤ１の測定データの通信経路、ＩｏＴ機器Ｄにおける暗号化のレベルなどに基づいて、ＩｏＴ機器Ｄの属性の分析を実行する。 Information on the IoT device D includes the manufacturer and time of manufacture of the IoT device D, the presence / absence and timing of calibration of the IoT device D, the response of the IoT device D, the presence / absence of a secure chip, the operating status, various authentication information, the installation location, and the installation. The environment, the difficulty of the data measured by the sensor D1 of the IoT device D, the communication path, the encryption level, etc., these information are passed through the model of the IoT device D, the type of device used, and the communication line. It can be obtained from reactions and the like.
That is, the IoT device attribute analysis unit 16 (see FIG. 1) determines the manufacturer of the IoT device D, the manufacturing time of the IoT device D, whether or not the IoT device D has been calibrated and / or maintained, and the IoT device D has been calibrated and maintained. / Or when maintenance was performed, IoT device D calibration and / or maintenance details, IoT device response, whether IoT device D is equipped with a secure chip, IoT device operating status, IoT device D Authentication information, installation location of IoT device D, installation environment and / or measurement environment of IoT device D, difficulty of measuring data by sensor D1 of IoT device D, communication of measurement data of sensor D1 transmitted by IoT device D The attribute analysis of the IoT device D is performed based on the route, the level of encryption in the IoT device D, and the like.
　ＩｏＴ機器ＤのセンサＤ１の測定データの情報として、対象とするＩｏＴ機器Ｄから発信された過去分も含めたデータ、他のＩｏＴ機器の発信データとの比較などがある。
　つまり、測定データ分析部１４（図１参照）は、第１取得部１１（図１参照）によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、第１取得部１１によって過去に取得された過去分測定データとの比較を実行する。
　また、測定データ分析部１４は、第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データと、ＩｏＴ機器Ｄ以外のＩｏＴ機器から発信されたデータとの比較を実行する。 As the information of the measurement data of the sensor D1 of the IoT device D, there are data including the past data transmitted from the target IoT device D, comparison with the transmission data of other IoT devices, and the like.
That is, the measurement data analysis unit 14 (see FIG. 1) has acquired the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 (see FIG. 1) and the measurement data acquired by the first acquisition unit 11 in the past. Perform a comparison with past measurement data.
Further, the measurement data analysis unit 14 compares the measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11 with the data transmitted from the IoT device other than the IoT device D.
　なお、これらの情報は、全て利用する必要はなく、一つもしくは複数の組合せによって総合的に判断する。判断は、人によって行ってもよいがＩｏＴ機器の設置台数は莫大なものとなるため、全部もしくは部分的には電子的に行うことが望ましく、電子的な判断には機械学習などのＡＩを利用してもよい。特に、調査時点だけの情報による分析だけではなく、過去のデータ、ＩｏＴ機器Ｄの所有者および／または管理者の履歴などの統計的な分析が重要となる。
　また、根拠の情報の入手には、主にインターネットや他回線などの通信系を用いて行う方法、ＩｏＴ機器メーカーなどからの情報、他のデータベースに記録された情報、ＩｏＴ機器Ｄの所有者および／または管理者による登録情報、調査員による確認などが考えられる。
　また、これらの情報の一部もしくは全部をＩｏＴ機器Ｄ内に登録しておき、通信回線を通して参照するようにしてもよい。その場合、改ざんなどができないようセキュリティ的に保護されたチップ等の領域に保存し、暗号化された通信によって参照することが望ましい。
　なお、これらの情報の書き込みには情報の内容に応じた権限を必要とすることが望ましい。また、チップに書き込まれる情報は追記可能ではあるが消去はできず、データを書き込んだ日付、人、組織などの情報も合わせて記憶することが望ましい。
　また、ＩｏＴ機器Ｄ自体に自らの消費電力をモニタリングする機能を付加したり、ＩｏＴ機器Ｄ自体にソフト的もしくはハード的に改造が行われていないかを自己チェックする機能を付加したりしてもよく、それらの情報もセキュリティ的に保護された領域に保存され、参照できるようにしてもよい。 It is not necessary to use all of this information, and a comprehensive judgment is made based on one or more combinations. The judgment may be made by a person, but since the number of IoT devices installed is enormous, it is desirable to make the judgment electronically in whole or in part, and AI such as machine learning is used for the electronic judgment. You may. In particular, not only the analysis based on the information only at the time of the survey, but also the statistical analysis such as the past data and the history of the owner and / or the manager of the IoT device D is important.
In addition, the method of obtaining the grounds information is mainly performed using a communication system such as the Internet or another line, information from IoT device manufacturers, information recorded in other databases, owners of IoT device D, and others. / Or registration information by the administrator, confirmation by the investigator, etc. can be considered.
Further, a part or all of this information may be registered in the IoT device D and referred to through a communication line. In that case, it is desirable to store it in an area such as a chip protected by security so that it cannot be tampered with, and refer to it by encrypted communication.
It is desirable that the writing of such information requires authority according to the content of the information. Further, although the information written on the chip can be added but cannot be deleted, it is desirable to also store information such as the date when the data was written, the person, and the organization.
In addition, even if the IoT device D itself is provided with a function for monitoring its own power consumption, or the IoT device D itself is provided with a function for self-checking whether or not it has been modified in terms of software or hardware. Often, such information may also be stored and made available for reference in a secure area.
　ＩｏＴ機器ＤのセンサＤ１の測定データの分析では、データの内容が書き換えられていないか？　ＩｏＴ機器Ｄが改造されていないか？　測定環境に変化がないか？　などを判断することが重要である。これらの判断には、人為的な加工や操作の痕跡の有無や、ＩｏＴ機器Ｄの消費電力の変化などがポイントとなる。これらの判断を人が行ってよいが、多数のＩｏＴ機器に対応するためには、電子的な処理によって一部もしくは全ての判断を行う方がよい。データの分析・分類などにはデータマイニングなどの電子的な処理を必要に応じて行うことが望まれる。
　このように、ＩｏＴ機器Ｄからの時系列の出力データを処理することで、異常及び異常の状態が生じている確率を求める。 Is the content of the data rewritten in the analysis of the measurement data of the sensor D1 of the IoT device D? Is the IoT device D modified? Is there any change in the measurement environment? It is important to judge such things. The points for these judgments are the presence or absence of traces of artificial processing or operation, and changes in the power consumption of the IoT device D. A person may make these judgments, but in order to support a large number of IoT devices, it is better to make some or all judgments by electronic processing. For data analysis and classification, it is desirable to perform electronic processing such as data mining as needed.
By processing the time-series output data from the IoT device D in this way, the probability that an abnormality or an abnormal state has occurred is obtained.
　人や組織、デバイスなどに関しては、必要に応じて、面談や現場確認を行った結果も判断の材料としてもよい。センサＤ１の測定データの信用度をより正確に把握する必要があるＩｏＴ機器Ｄに対しては、データでの改ざんが難しい現場での面談や確認が有効である。
　逆に面談や現場確認は人手がかかるため、多数のＩｏＴ機器をチェックするには適さない。そのため、場合によっては、抜き打ちでの調査で頻度を減らす、カメラ等によるリモートでのチェックで遠隔で確認するなどの方法をとってもよい。
　本明細書において、センサＤ１の測定データの信用の度合い、信用度は、同じ意味で用いられており、対象とするＩｏＴ機器Ｄが発信するセンサＤ１の測定データが正しい情報であることの度合いを確率などの数値やＡ、Ｂ、Ｃなどのランクで示すものであり、データが正しいかどうかの検出や判断をするものではない。
　つまり、「信用度が高い」は誤った測定データを発信する可能性が低いことを示し、「信用度が低い」は誤った測定データを発信してしまう可能性が高いことを示している。信用度が低いからと言って正しくない測定データであるとは限らない。例えば、管理やメンテナンスが不十分で、設置から時間が経過し老朽化した温度センサは結果として正しい測定データを発信する場合もあるが、正しくない測定データを発信する可能性が高いと判断され、「信用度が低い」になる。また、過去に何度も悪意のある偽の測定データを発信した経歴のある所有者が所有するＩｏＴ機器Ｄにおいては、たとえ正しい情報を発信していたとしても、そのデータの「信用度が低い」になる可能性がある。
　このような総合的な信用の度合いの判断は、初期の段階では経験のある人によって行われることもあるが、その経験をＡＩなどに学習させることで、自動的に信用スコアを算出することが可能になる。スコアリングには、例えば多変量解析、判断ベース処理、ロジスティック回帰を用いる方法などがあるが、これに限定されるものではなく様々なスコアリングに使用可能なアルゴリズムを用いることができる。 For people, organizations, devices, etc., the results of interviews and on-site confirmations may be used as a basis for judgment, if necessary. For the IoT device D, which needs to more accurately grasp the reliability of the measurement data of the sensor D1, interviews and confirmations at the site where it is difficult to falsify the data are effective.
On the contrary, interviews and on-site confirmations are labor-intensive, so they are not suitable for checking a large number of IoT devices. Therefore, depending on the case, a method such as reducing the frequency by unannounced investigation or remote confirmation by a remote check by a camera or the like may be taken.
In the present specification, the degree of credibility and the degree of credibility of the measurement data of the sensor D1 are used interchangeably, and the degree of probability that the measurement data of the sensor D1 transmitted by the target IoT device D is correct information is probable. It is indicated by numerical values such as, or ranks such as A, B, and C, and does not detect or judge whether the data is correct.
In other words, "high credit rating" indicates that the possibility of transmitting erroneous measurement data is low, and "low credit rating" indicates that the possibility of transmitting erroneous measurement data is high. Poor credibility does not mean that the measurement data is incorrect. For example, an aging temperature sensor that has been installed for a long time due to insufficient management and maintenance may result in transmitting correct measurement data, but it is judged that there is a high possibility that it will transmit incorrect measurement data. It becomes "low credit". In addition, in the IoT device D owned by the owner who has transmitted malicious fake measurement data many times in the past, even if the correct information is transmitted, the data is "low credibility". There is a possibility of becoming.
Judgment of the degree of overall credit may be made by an experienced person at the initial stage, but the credit score can be calculated automatically by having AI learn the experience. It will be possible. Scoring includes, for example, multivariate analysis, judgment-based processing, and a method using logistic regression, but the scoring is not limited to this, and algorithms that can be used for various scoring can be used.
　図４は測定データ分析部１４によって実行されるデータマイニング処理の一例を示す図である。詳細には、図４（Ａ）は第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データ（時系列データ）を示している。図４（Ｂ）は図４（Ａ）に示す測定データの変化の特徴を図形化したものを示している。図４（Ｃ）は図４（Ｂ）に示すデータに対してデータマイニング処理を実行する手法の例を示している。図５はデータマイニング処理を実行するニューラルネットワークの一例を示す図である。 FIG. 4 is a diagram showing an example of data mining processing executed by the measurement data analysis unit 14. In detail, FIG. 4A shows the measurement data (time series data) of the sensor D1 of the IoT device D acquired by the first acquisition unit 11. FIG. 4B shows a graphic representation of the characteristics of changes in the measurement data shown in FIG. 4A. FIG. 4C shows an example of a method of executing data mining processing on the data shown in FIG. 4B. FIG. 5 is a diagram showing an example of a neural network that executes data mining processing.
　ＩｏＴ機器Ｄから得られた時系列データ（第１取得部１１によって取得されたＩｏＴ機器ＤのセンサＤ１の測定データ）（図４（Ａ）参照）に対して直接データマイニングを行ってもよいが、図４（Ｂ）に示すように、その変化の特徴を図形化した前処理を行うことができれば、より精度の良いデータマイニングを行うことができる。例えば、センサＤ１の測定データの経時変化の周波数やカオスなど変換された図形を形成した後に、データマイニングによる分析する方法などが考えられる。また、データマイニングの手法として、ニューラルネットワークを用いる方法、ロジスティック回帰を用いる方法、サポートベクターマシンを用いる方法、Ｋ－近傍法を用いる方法などが考えられ、センサＤ１の測定データの内容に従って適している手法を選択・組み合わせるとよい。
　図４に示す例では、ＩｏＴ機器ＤのセンサＤ１の測定データの変化の特徴を図形化した後にデータマイニング処理を実行するため、測定データの変化の特徴の図形化が行われない場合よりも高精度で演算量が少ないデータマイニング処理を実行することができる。 Data mining may be performed directly on the time-series data obtained from the IoT device D (measurement data of the sensor D1 of the IoT device D acquired by the first acquisition unit 11) (see FIG. 4 (A)). , As shown in FIG. 4B, if the preprocessing in which the characteristics of the change can be visualized can be performed, more accurate data mining can be performed. For example, a method of analyzing the measurement data of the sensor D1 by data mining after forming a converted figure such as a frequency or chaos of a change with time can be considered. Further, as a data mining method, a method using a neural network, a method using logistic regression, a method using a support vector machine, a method using a K-nearest neighbor method, etc. can be considered, and are suitable according to the contents of the measurement data of the sensor D1. It is good to select and combine methods.
In the example shown in FIG. 4, since the data mining process is executed after the characteristics of the change in the measurement data of the sensor D1 of the IoT device D are visualized, it is higher than the case where the characteristics of the change in the measurement data are not visualized. It is possible to execute data mining processing with high accuracy and a small amount of calculation.
　図５に示す例では、まず、畳み込み処理によって特徴抽出を実行し、次いで、プーリング層におけるプーリングによって画素の低減を行い、次いで、ニューラルネットワークを用いて特徴の分類を行う。
　図４および図５に示す例のように、一旦図形化することによって、一般的な畳み込みニューラルネットワークなどの手法を用いることができるため、精度が良く、演算量の少ない分類が可能になる。
　また、図５に示す例では、出力層における分類のアウトプットとして、正常、測定対象異常、センサ状態異常、設置環境不備、データ改ざん、装置改造などが起き得る確率とした。
　なお、ＩｏＴ機器Ｄから得られるデータは、ＩｏＴ機器Ｄが設置されている環境をセンシングして獲得するセンサＤ１の測定データの他に、ＩｏＴ機器Ｄそのものの状態をセンシングするデータであってもよい。例えば、ＩｏＴ機器Ｄそのものの消費電力、ＩｏＴ機器Ｄの演算部の温度、ＩｏＴ機器Ｄのケースの開閉の状態や履歴、ログデータなどになる。
　なお、ここでは前処理、データマイニング、機械学習、アウトプットなどの例をいくつか挙げたが、これらに限定するものではなく、センサＤ１の測定データの信用度を推定できるものであれば、電子的、機械的、人による判断などいずれの手法を用いてもよい。 In the example shown in FIG. 5, feature extraction is first performed by convolution processing, then pixel reduction is performed by pooling in the pooling layer, and then features are classified using a neural network.
As shown in the examples shown in FIGS. 4 and 5, once the figure is formed, a method such as a general convolutional neural network can be used, so that classification with high accuracy and a small amount of calculation becomes possible.
Further, in the example shown in FIG. 5, as the output of the classification in the output layer, the probability that normal, measurement target abnormality, sensor state abnormality, installation environment defect, data falsification, device modification, etc. may occur is set.
The data obtained from the IoT device D may be data that senses the state of the IoT device D itself, in addition to the measurement data of the sensor D1 that is acquired by sensing the environment in which the IoT device D is installed. .. For example, the power consumption of the IoT device D itself, the temperature of the calculation unit of the IoT device D, the open / closed state and history of the case of the IoT device D, log data, and the like.
Here, some examples such as preprocessing, data mining, machine learning, and output are given, but the present invention is not limited to these, and any data that can estimate the reliability of the measurement data of the sensor D1 is electronic. , Mechanical, human judgment, etc. may be used.
　図６は第１実施形態のＩｏＴ機器信用度算出装置１の信用度算出部１７において実行される処理の一例を示す図である。詳細には、図６は図２のステップＳ１７において実行される処理の一例を示す図である。 FIG. 6 is a diagram showing an example of processing executed by the credit rating calculation unit 17 of the IoT device credit rating calculation device 1 of the first embodiment. In detail, FIG. 6 is a diagram showing an example of the process executed in step S17 of FIG.
　図６に示す例では、ステップＳ１０１において、ＩｏＴ機器ＤのセンサＤ１の測定データの信用度に関係するパラメータ（例えば図５に示す出力層から出力されたもの）が入力される。
　ステップＳ１０１において入力されるデータとしては、例えば、図５に示すニューラルネットワークなどを用いてデータの信用度などとして得られたものなどが用いられる。ステップＳ１０１において入力されるデータは、例えば過去のデータの傾向を分析することで、作為的にデータが加工されていないか、ＩｏＴ機器Ｄの故障などによって異常値が示されていないかなどを分類し、データの確からしさ（信用度）を例えばパーセントなどで示したものである。
　また、ステップＳ１０２では、デバイス（ＩｏＴ機器Ｄ）の属性が入力される。デバイスの属性とは、デバイスが持つセキュアの能力の度合い（セキュアレベル）や、デバイスが認証されているか、などが考えられる。デバイスのセキュアレベルは、ＩｏＴ機器Ｄのメーカー、機種名などから得られるデバイスとしての信頼の度合いや、デバイスの設定などによる脆弱性の度合い、本デバイスのセンサの校正の履歴、通信回線を通して得られたデバイスの反応などから求めることができる。 In the example shown in FIG. 6, in step S101, parameters related to the reliability of the measurement data of the sensor D1 of the IoT device D (for example, those output from the output layer shown in FIG. 5) are input.
As the data input in step S101, for example, data obtained as the reliability of the data using the neural network shown in FIG. 5 or the like is used. The data input in step S101 is classified, for example, by analyzing the tendency of past data, whether the data is intentionally processed, or whether an abnormal value is shown due to a failure of the IoT device D, or the like. However, the certainty (credibility) of the data is shown by, for example, a percentage.
Further, in step S102, the attribute of the device (IoT device D) is input. The attributes of the device may be the degree of secure ability (secure level) of the device, whether the device is authenticated, and the like. The secure level of the device is obtained through the degree of reliability as a device obtained from the manufacturer and model name of IoT device D, the degree of vulnerability due to device settings, the calibration history of the sensor of this device, and the communication line. It can be obtained from the reaction of the device.
　また、ステップＳ１０３では、ＩｏＴ機器Ｄの所有者および／または管理者（ヒト、組織）の情報が入力される。ステップＳ１０３において入力されるＩｏＴ機器Ｄの所有者および／または管理者の情報としては、ＩｏＴ機器Ｄの所有者および／または管理者が認証され実在しているか、過去に間違ったデータを発信したことはないか、ある場合はその頻度、ＩｏＴ機器Ｄの所有者および／または管理者の信頼の度合い（ヒトスコア）、管理体制の信頼の度合い（管理体制スコア）、ＩｏＴ機器Ｄの設置環境のスコアなどが考えられる。
　ヒトスコアは例えば収入、負債、実績、行動など、過去および現在の情報から求められるヒトや組織のスコアデータを示す。これには、組織の場合は、組織の格付けなどのデータをもとに算出してもよいし、別途スコアリングされたヒトの情報から算出してもよい。管理体制スコアは、ヒトや組織が、本デバイスだけに限らず、ＩｏＴ機器Ｄに対して行っている管理の履歴、校正の履歴などから求めることができる。
　また、設置環境スコアは、設置場所を確認し、正しくセンサが設置されているかを、実際の環境の確認、映像やデータなどから判断することで求めることができる。
　ステップＳ１０１、ステップＳ１０２およびステップＳ１０３の順序は、図６に示す順序でなくてもよく、必要な情報が入力されればよい。また、データの品質に関するパラメータ情報は、上記に示したものを全て使う必要はなく、さらに存在しないデータがあってもよい。
　次いで、ステップＳ１０４では、ＩｏＴ機器Ｄのデータ品質の目安となる信用スコアが算出される。 Further, in step S103, information on the owner and / or manager (human, organization) of the IoT device D is input. As the information of the owner and / or administrator of the IoT device D input in step S103, the owner and / or administrator of the IoT device D has been authenticated and exists, or has transmitted incorrect data in the past. If not, the frequency, if any, the degree of trust of the owner and / or administrator of IoT device D (human score), the degree of trust of the management system (management system score), the score of the installation environment of IoT device D, etc. Can be considered.
The human score indicates the score data of a person or organization obtained from past and present information such as income, debt, performance, and behavior. In the case of an organization, this may be calculated based on data such as an organization rating, or may be calculated from separately scored human information. The management system score can be obtained not only from this device but also from the history of management performed on the IoT device D, the history of calibration, and the like by humans and organizations.
In addition, the installation environment score can be obtained by confirming the installation location and determining whether the sensor is installed correctly from the confirmation of the actual environment, images, data, and the like.
The order of steps S101, S102, and S103 does not have to be the order shown in FIG. 6, and necessary information may be input. Further, it is not necessary to use all the parameter information related to the data quality shown above, and there may be data that does not exist.
Next, in step S104, a credit score, which is a measure of the data quality of the IoT device D, is calculated.
　図７は第１実施形態のＩｏＴ機器信用度算出装置１の信用度算出部１７が多変量解析を実行することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出した一例を示す図である。
　図７に示す例では、信用度算出部１７が、判別関数を用いて、多変量解析を実行することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出した。
　詳細には、４１台のＩｏＴ機器（図７に符号Ａ～ＡＰで示す）の、各種入力されたパラメータ情報に対して、データの正誤の結果判別する多変量解析によって判別関数の係数を求めた。さらに判別関数の結果から、見やすさの観点から正の数になるようにデータの信用スコア（確からしさ）に変換した。
　図７に示す例では、信用スコアが大きければ、そのデータの品質がよいことを示している。このようにして求められた判別関数を用いることで、新規のＩｏＴ機器に対して、そのデータの信用スコアを求めることができる。さらに、入力データに対する結果を用いて、新たに判別関数の係数を更新していくことで、より精度を向上させることができる。
　図７に示す例では、多変量解析の中で、線形の判別関数を用いたが、他の例では、二次判別関数や非線形判別関数を用いてもよい。更に他の例では、ニューラルネットワーク等の人工知能などの手法を用いてもよい。 In FIG. 7, the credit rating unit 17 of the IoT device credit rating calculation device 1 of the first embodiment performs multivariate analysis to calculate the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D. It is a figure which shows an example.
In the example shown in FIG. 7, the credit rating calculation unit 17 calculated the credit score as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by executing the multivariate analysis using the discriminant function.
Specifically, the coefficients of the discriminant function were obtained by multivariate analysis for discriminating the result of correctness of data for various input parameter information of 41 IoT devices (indicated by reference numerals A to AP in FIG. 7). .. Furthermore, the result of the discriminant function was converted into a credit score (certainty) of the data so that it would be a positive number from the viewpoint of legibility.
In the example shown in FIG. 7, the larger the credit score, the better the quality of the data. By using the discriminant function obtained in this way, the credit score of the data can be obtained for the new IoT device. Furthermore, the accuracy can be further improved by newly updating the coefficient of the discriminant function using the result for the input data.
In the example shown in FIG. 7, a linear discriminant function is used in the multivariate analysis, but in other examples, a quadratic discriminant function or a non-linear discriminant function may be used. In yet another example, a technique such as artificial intelligence such as a neural network may be used.
　図８は第１実施形態のＩｏＴ機器信用度算出装置１の信用度算出部１７が状態ポイント計算（判断ベース）を実行することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出する処理の一例を説明するためのフローチャートである。
　図９は第１実施形態のＩｏＴ機器信用度算出装置１の信用度算出部１７が状態ポイント計算（判断ベース）を実行することによって、ＩｏＴ機器Ｄから発信されたセンサＤ１の測定データの信用度としての信用スコアを算出した一例を示す図である。 FIG. 8 shows the credit as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by the credit rating calculation unit 17 of the IoT device credit rating calculation device 1 of the first embodiment executing the state point calculation (judgment base). It is a flowchart for demonstrating an example of the process of calculating a score.
FIG. 9 shows the credit as the credit rating of the measurement data of the sensor D1 transmitted from the IoT device D by the credit rating calculation unit 17 of the IoT device credit rating calculation device 1 of the first embodiment executing the state point calculation (judgment base). It is a figure which shows an example which calculated the score.
　図８に示す例では、ステップＳ２０１において、信用度算出部１７が、状態ポイントの計算を実行する。詳細には、ステップＳ２０１では、判定するＩｏＴ機器Ｄのセキュリティの強さ（セキュアレベル）、ＩｏＴ機器Ｄを管理するヒト・組織（ヒトスコア）、ＩｏＴ機器Ｄが設置してある管理の現状の状態（管理体制スコア）、ＩｏＴ機器Ｄの設置環境の状態（環境スコア）に対してポイントづけが行われる。図９に示す例では、ＩｏＴ機器Ｄの良い状態が高いポイントに相当し、ポイントとして、１～１００の値に正規化したものを用いた。
　次いで、ステップＳ２０２では、信用度算出部１７が、ポジティブポイント加算を実行する。詳細には、ステップＳ２０２では、きちんと管理し正常なデータを発信したなどの過去の実績に対するポイントづけ（ポジティブポイント加算）が行われる。ポジティブポイント加算は、前回のポイントに今回のポイントを加算するものであり、長期間正常なデータを発信してきたＩｏＴ機器Ｄに対して高い評価を与えるものである。このような過去の行動に対して付与するポイントを「累積ポジティブポイント」（図９参照）と呼ぶ。図９に示す例では、先の状態ポイントで用いたヒトスコア、管理体制スコア、環境スコアとＩｏＴ機器Ｄが発信したセンサＤ１の測定データを分析したデータスコアを１～５に正規化したものを用いた。
　図９に示す例では、ヒト・組織、管理体制、環境に関するスコアとして状態ポイントと累積ポジティブポイントとで同じものを用いたが、他の例では、現状と過去で重みを変えるなど異なるものを用いてもよい。 In the example shown in FIG. 8, in step S201, the credit rating calculation unit 17 executes the calculation of the state points. Specifically, in step S201, the security strength (secure level) of the IoT device D to be determined, the person / organization (human score) that manages the IoT device D, and the current state of management in which the IoT device D is installed ( Management system score) and the state of the installation environment of IoT device D (environmental score) are scored. In the example shown in FIG. 9, the good condition of the IoT device D corresponds to a high point, and the point normalized to a value of 1 to 100 was used.
Next, in step S202, the credit rating calculation unit 17 executes positive point addition. In detail, in step S202, points are assigned (addition of positive points) to past achievements such as properly managed and transmitted normal data. The positive point addition is to add the current point to the previous point, and gives a high evaluation to the IoT device D that has transmitted normal data for a long period of time. The points given to such past actions are called "cumulative positive points" (see FIG. 9). In the example shown in FIG. 9, the data score obtained by analyzing the human score, the management system score, the environmental score and the measurement data of the sensor D1 transmitted by the IoT device D used in the previous state points is normalized to 1 to 5. There was.
In the example shown in FIG. 9, the same score for human / organization, management system, and environment was used for the status point and the cumulative positive point, but in other examples, different scores such as changing the weight between the current state and the past were used. You may.
　次いで、ステップＳ２０３では、信用度算出部１７は、後述するネガティブイベントの有無（ネガティブなイベントが発生したか否か）の判定を実行する。例えばＩｏＴ機器Ｄが誤データを発信してしまった、登録データに嘘があったなどのような、ネガティブイベントが有った（ネガティブなイベントが発生した）場合には、ステップＳ２０４に進む。一方、ネガティブイベントが無かった（ネガティブなイベントが発生していない）場合には、ステップＳ２０５に進む。
　ステップＳ２０４では、信用度算出部１７が、ポイントをリセットもしくは減点し、ステップＳ２０５に進む。ステップＳ２０４は、信頼性を大きく損なう事故を起こした対象に対して、積み上げてきたポジティブポイントを大きく減らすことでペナルティーを与えるものである。リセットもしくは減点するポイントは、累積ポジティブポイントのみに行ってもよいし、状態ポイントと累積ポジティブポイントに対して行ってもよい。
　なお、ヒト・組織や環境に関するポイントやネガティブイベントに関しては、そのヒト・組織や環境に含まれる全てのＩｏＴ機器に共通してポイントを計算する。 Next, in step S203, the credit rating calculation unit 17 executes a determination of the presence / absence of a negative event (whether or not a negative event has occurred), which will be described later. For example, if there is a negative event (a negative event has occurred) such as the IoT device D transmitting erroneous data or the registered data lying, the process proceeds to step S204. On the other hand, if there is no negative event (no negative event has occurred), the process proceeds to step S205.
In step S204, the credit rating calculation unit 17 resets or deducts points, and proceeds to step S205. In step S204, a penalty is given to a target who has caused an accident that greatly impairs reliability by greatly reducing the accumulated positive points. The points to be reset or deducted may be given only to the cumulative positive points, or may be given to the state points and the cumulative positive points.
Regarding points related to people / tissues / environment and negative events, points are calculated in common for all IoT devices included in the people / tissues / environment.
　ステップＳ２０５では、信用度算出部１７が、ステップＳ２０１～ステップＳ２０４において計算されたポイントから、信用スコアを算出する。
　次いで、ステップＳ２０６では、信用度算出部１７が、ポイント更新（ステップＳ２０１～ステップＳ２０４において計算されたポイントの記憶）を実行する。
　次いで、ステップＳ２０７では、信用度算出部１７が、図８に示す処理を継続するか否かを判定する。図８に示す処理を継続する場合には、ステップＳ２０２に戻る。一方、図８に示す処理を継続しない場合には、図８に示す処理を終了する。 In step S205, the credit rating calculation unit 17 calculates the credit score from the points calculated in steps S201 to S204.
Next, in step S206, the credit rating calculation unit 17 executes point update (storage of points calculated in steps S201 to S204).
Next, in step S207, the credit rating calculation unit 17 determines whether or not to continue the process shown in FIG. When continuing the process shown in FIG. 8, the process returns to step S202. On the other hand, when the process shown in FIG. 8 is not continued, the process shown in FIG. 8 is terminated.
　図９に示す例では、１４台のＩｏＴ機器（図９に符号Ａ～Ｎで示す）について、「状態ポイント」して、１～１００に正規化されたＩｏＴ機器のセキュアレベル、ヒトスコア、管理体制スコア、環境スコアの和を求めた。
　次に、「累計ポジティブポイント」として、１～５に正規化されたヒトスコア、データスコア、管理体制スコア、環境スコアを前回の累計ポジティブポイントの値に加えたものを求めた。「累計ポジティブポイント」は、過去のポイントに対して加算していくものであり、長期間安定して正しいデータを発信したデバイスなどに対して、高いポイントがつくことになる。
　次に、ネガティブイベントの例として、デバイス（ＩｏＴ機器）の設定や管理ミスによる脆弱性の発覚、悪意や人為的なミスによる誤データを発信してしまった、登録したデータに嘘があった、管理体制の不備が発覚したなど、事故が起きた場合、ポイントを０にリセットした。
　こうして得られたポイントを「ポイント」として集計したうえで、見やすい形の「信用スコア」に整形した。
　なお、図９に示したポイントの項目、加算もしくは減算するポイントの範囲などは、ここの示した限りでなく、他の例では、それらを適宜増減・選定してもよい。 In the example shown in FIG. 9, for 14 IoT devices (indicated by symbols A to N in FIG. 9), the secure level, human score, and management system of the IoT devices normalized to 1 to 100 by "state points". The sum of the score and the environmental score was calculated.
Next, as the "cumulative positive points", the human score, data score, management system score, and environmental score normalized to 1 to 5 were added to the values of the previous cumulative positive points. "Cumulative positive points" are added to past points, and high points will be given to devices that have stably transmitted correct data for a long period of time.
Next, as an example of a negative event, a vulnerability was discovered due to a device (IoT device) setting or management error, incorrect data was transmitted due to malicious or human error, and there was a lie in the registered data. In the event of an accident, such as when a deficiency in the management system was discovered, the points were reset to 0.
The points obtained in this way were aggregated as "points" and then shaped into an easy-to-read "credit score."
The items of points shown in FIG. 9, the range of points to be added or subtracted, and the like are not limited to those shown here, and in other examples, they may be increased or decreased or selected as appropriate.
　上述した例で示したＩｏＴ機器Ｄの信用度を算出するための情報及び入力順は、一例でありこれらに限定されるものではない。
　データ利用者がＩｏＴ機器ＤのセンサＤ１の測定データの信用度を必要とする分野としては、次のようなものが考えられる。
　自治体などの公的機関のデータ、例えば水位、地盤、温度、湿度、風速、気圧などの気象データなど災害や農業、漁業などの自然に関する事業に関するデータ、交通量、人の移動などに関するデータなどが考えられる。これらのデータは、自然の中に設置され管理が難しく、風雨にさらされるため、様々な原因で異常が生じやすいためである。
　他には、生産現場や建築建設現場等の製品品質に関わる測定データが考えられる。具体的には、重さ、サイズ、厚み、硬さ、色、画像、速度、密度、量など品質に関わるデータなどが測定対象になる。これらは、管理者などがデータ偽装をはたらいていないことを証明する必要があり、第三者によるデータのチェックを求めるニーズが高い分野である。
　また、農業、林業、水産業などの事業者が発信するデータもあげられる。これらは、データを相互利用することで、気候の変動、病害虫や病気の予兆などの精度を上げることができる。具体的には、水位、水温、気温、湿度、風速、日射量、ＣＯ２濃度、害虫、病気、画像、育成状態、肥料濃度、土壌、水質などが想定される。
　他には、感染症の原因菌やウイルスなどの測定データ、射線量や放射性物質の測定データ、毒物や危険物質などの測定データなど、危険物質の分布状況や予測に利用するケースも考えられる。具体的には、ウイルスの有無や量、病原菌の有無や量、毒物の有無や量、放射性物質の有無や量、危険物の有無や量、放射能の量、ＰＭ２．５の量、窒素酸化物の量、酸素濃度、一酸化炭素濃度、感染症の感染数、感染症の媒介物の量などが想定される。 The information and input order for calculating the creditworthiness of the IoT device D shown in the above example is an example and is not limited thereto.
The following are conceivable fields in which the data user needs the reliability of the measurement data of the sensor D1 of the IoT device D.
Data from public institutions such as local governments, such as meteorological data such as water level, ground, temperature, humidity, wind speed, and atmospheric pressure, data related to nature-related businesses such as disasters, agriculture, and fisheries, traffic volume, and data on the movement of people. Conceivable. This is because these data are installed in nature, difficult to manage, and exposed to wind and rain, so that abnormalities are likely to occur due to various causes.
In addition, measurement data related to product quality at production sites and construction sites can be considered. Specifically, data related to quality such as weight, size, thickness, hardness, color, image, speed, density, and quantity are measured. These are fields where it is necessary to prove that the administrator or the like is not acting as a data spoofing, and there is a high need for a third party to check the data.
In addition, data sent by businesses such as agriculture, forestry, and fisheries can be mentioned. By interusing the data, these can improve the accuracy of climate variability, pests and signs of diseases. Specifically, water level, water temperature, temperature, humidity, wind speed, amount of solar radiation, CO 2 concentration, pests, diseases, images, growing conditions, fertilizer concentration, soil, water quality, etc. are assumed.
In addition, it may be used for the distribution status and prediction of dangerous substances such as measurement data of causative bacteria and viruses of infectious diseases, measurement data of radiation dose and radioactive substances, and measurement data of poisons and dangerous substances. Specifically, the presence or absence or amount of virus, the presence or absence or amount of pathogens, the presence or absence or amount of toxic substances, the presence or absence or amount of radioactive substances, the presence or absence or amount of dangerous substances, the amount of radioactivity, the amount of PM2.5, nitrogen oxidation The amount of substances, oxygen concentration, carbon monoxide concentration, number of infectious diseases, and amount of infectious disease mediators are assumed.
　多量なデータを入手し解析する事で様々な予測をする分野では、多量のデータが必要であるが、入力したデータの信用度が低いと正しい解析をすることができなくなってしまう。
　遠隔診断等で用いる医療機器など生命や健康に関わる機器からの情報も高いデータの信用度が求められる。具体的には、体温、脈拍数、血糖値、中性脂肪、皮膚の色、血圧、血中酸素濃度、運動量、消費カロリー、食事内容、声、血液成分、呼気成分、尿成分、唾液成分、汗成分、粘膜分泌物成分などが想定される。
　原油、ガス、石炭などのエネルギー関連や鉱石、貴金属、レアメタル、宝石など鉱業に関わる情報、農産物などの取れ高など、取引市場での価格が、環境などに依存し、触れ幅が大きくなる分野などは、作為のあるデータ操作が行われるリスクがある。具体的には、取れ高、収量などを推測できる画像等のデータ、目的物質の含有量、品質に関わるデータなどが想定される。
　以上、ＩｏＴ機器のデータの信用度を必要とする分野をいくつか上げたが、ＩｏＴ機器のデータの信用度の確認を必要とする分野であれば、これに限定するものではない。 In the field of making various predictions by obtaining and analyzing a large amount of data, a large amount of data is required, but if the reliability of the input data is low, correct analysis cannot be performed.
High reliability of data is required for information from devices related to life and health such as medical devices used for remote diagnosis. Specifically, body temperature, pulse rate, blood sugar level, triglyceride, skin color, blood pressure, blood oxygen concentration, amount of exercise, calories burned, dietary content, voice, blood component, exhaled component, urine component, saliva component, Sweat components, mucosal secretion components, etc. are assumed.
Energy-related information such as crude oil, gas, and coal, information related to the mining industry such as ores, precious metals, rare metals, and gems, and trading volume of agricultural products, etc., where prices in the trading market depend on the environment, etc. There is a risk that artificial data manipulation will be performed. Specifically, data such as images that can estimate the yield and yield, data related to the content of the target substance, quality, and the like are assumed.
As mentioned above, some fields that require the creditworthiness of the data of the IoT device have been raised, but the fields that require the confirmation of the creditworthiness of the data of the IoT device are not limited to this.
＜適用例＞
　図１０は第１実施形態のＩｏＴ機器信用度算出装置１の第１適用例を示す図である。詳細には、図１０は第１実施形態のＩｏＴ機器信用度算出装置１が適用されたＩｏＴ機器信用度算出システムＳを示している。
　図１０に示す例では、ＩｏＴ機器信用度算出システムＳが、ＩｏＴ機器信用度算出装置１と、ＩｏＴ機器Ｄと、ネットワークＮＷとを備えている。ＩｏＴ機器信用度算出装置１は、ＩｏＴ機器Ｄの所有、管理および製造のいずれも行っていない者によって所有または管理される。ＩｏＴ機器Ｄは、ネットワークＮＷを介してＩｏＴ機器信用度算出装置１の第１取得部１１（図１参照）とＩｏＴ機器信用度算出装置１以外の箇所とにセンサＤ１（図１参照）の測定データを自動的に発信可能に構成されている。
　図１０に示す例では、ＩｏＴ機器信用度算出装置１の信用度算出部１７（図１参照）によって算出されたＩｏＴ機器ＤのセンサＤ１の測定データの信用度が、ＩｏＴ機器Ｄの格付けに利用される。 <Application example>
FIG. 10 is a diagram showing a first application example of the IoT device credit rating calculation device 1 of the first embodiment. In detail, FIG. 10 shows the IoT device credit rating system S to which the IoT device credit rating calculation device 1 of the first embodiment is applied.
In the example shown in FIG. 10, the IoT device credit rating system S includes an IoT device credit rating calculation device 1, an IoT device D, and a network NW. The IoT device credit rating device 1 is owned or managed by a person who does not own, manage, or manufacture the IoT device D. The IoT device D transmits the measurement data of the sensor D1 (see FIG. 1) to a location other than the first acquisition unit 11 (see FIG. 1) of the IoT device credit rating device 1 and the IoT device credit rating calculation device 1 via the network NW. It is configured to be able to make calls automatically.
In the example shown in FIG. 10, the credit rating of the measurement data of the sensor D1 of the IoT device D calculated by the credit rating calculation unit 17 (see FIG. 1) of the IoT device credit rating calculation device 1 is used for the rating of the IoT device D.
　ＩｏＴ格付け会社のＩｏＴ機器信用度算出装置１は、インターネットなどのネットワークＮＷを通じて、ＩｏＴ機器Ｄの信用度の確認を行い、得られた信用度の度合いに応じて格付け行う。次に、格付けの結果もしくは結果とその根拠を、データ利用者側に提供しその対価を得る。データ利用者は、ＩｏＴ格付け会社から入手したＩｏＴ機器Ｄの信用度などをもとにデータを解析し活用する。また、データ利用者が信用度を確認したい対象を格付け会社に要求し、格付け会社はその結果を報告するサービスも考えられる。
　これらのサービスの対価は、お金、データとのセット販売などが考えられ、これらを単独もしくは組み合わせて対価とする。また、公的な行為、企業価値の向上などに活用するとして、サービスそのものは無償としてもよい。
　なお、格付けする対象は、ＩｏＴ機器Ｄそのものに対して行ってもよいし、ＩｏＴ機器Ｄの所有者もしくは管理者など人もしくは法人の単位、または、設置場所、エリアなどの場所の単位などグループ化された対象に対して行ってもよい。こうすることによって、データ利用者は利用するデータの確からしさをデータ解析の前提として利用することによって、より正確なデータの解析結果を得ることが可能になる。 The IoT device credit rating calculation device 1 of the IoT rating agency confirms the credit rating of the IoT device D through a network NW such as the Internet, and ranks the IoT device D according to the obtained degree of credit rating. Next, the result of the rating or the result and the basis thereof are provided to the data user side and the compensation is obtained. The data user analyzes and utilizes the data based on the credit rating of the IoT device D obtained from the IoT rating agency. In addition, a service is also conceivable in which a data user requests a rating agency for a target whose credit rating is to be confirmed, and the rating agency reports the result.
The consideration for these services may be money, set sales with data, etc., and these may be considered alone or in combination. In addition, the service itself may be free of charge as it is used for public acts and improvement of corporate value.
The target of rating may be the IoT device D itself, or the unit of a person or corporation such as the owner or manager of the IoT device D, or the unit of a place such as an installation place or an area. You may go to the target. By doing so, the data user can obtain a more accurate data analysis result by using the certainty of the data to be used as a premise of the data analysis.
　図１１は第１実施形態のＩｏＴ機器信用度算出装置１の第２適用例を示す図である。詳細には、図１１は第１実施形態のＩｏＴ機器信用度算出装置１が適用されたＩｏＴ機器信用度算出システムＳを示している。
　図１１に示す例では、ＩｏＴ機器信用度算出システムＳが、ＩｏＴ機器信用度算出装置１と、ＩｏＴ機器Ｄと、ネットワークＮＷとを備えている。ＩｏＴ機器信用度算出装置１は、ＩｏＴ機器Ｄの所有、管理および製造のいずれも行っていない者によって所有または管理される。ＩｏＴ機器Ｄは、ネットワークＮＷを介してＩｏＴ機器信用度算出装置１の第１取得部１１（図１参照）とＩｏＴ機器信用度算出装置１以外の箇所とにセンサＤ１（図１参照）の測定データを自動的に発信可能に構成されている。
　図１１に示す例では、ＩｏＴ機器信用度算出装置１の信用度算出部１７（図１参照）によって算出されたＩｏＴ機器ＤのセンサＤ１の測定データの信用度が、ＩｏＴ機器Ｄの認証（電子証明書の発行）に利用される。 FIG. 11 is a diagram showing a second application example of the IoT device credit rating calculation device 1 of the first embodiment. In detail, FIG. 11 shows the IoT device credit rating system S to which the IoT device credit rating calculation device 1 of the first embodiment is applied.
In the example shown in FIG. 11, the IoT device credit rating system S includes an IoT device credit rating calculation device 1, an IoT device D, and a network NW. The IoT device credit rating device 1 is owned or managed by a person who does not own, manage, or manufacture the IoT device D. The IoT device D transmits the measurement data of the sensor D1 (see FIG. 1) to a location other than the first acquisition unit 11 (see FIG. 1) of the IoT device credit rating device 1 and the IoT device credit rating calculation device 1 via the network NW. It is configured to be able to make calls automatically.
In the example shown in FIG. 11, the credit rating of the measurement data of the sensor D1 of the IoT device D calculated by the credit rating calculation unit 17 (see FIG. 1) of the IoT device credit rating calculation device 1 is the authentication of the IoT device D (electronic certificate). Used for issuance).
　現在、行われているＩｏＴ機器の認証では、ＩｏＴ機器の認証情報がＩｏＴ機器の製造段階または設置段階などにＩｏＴ機器に登録され、受け取ったデータが間違いなく対象とするＩｏＴ機器から発信されたデータかどうかが確認される。この認証は、通信経路などでのデータの改ざん、すり替えなどの対策には有効であるが、対象とするＩｏＴ機器の信用度、所有者または管理者の信用度までを確認することはできなかった。
　対象とするＩｏＴ機器の信用度、所有者または管理者の信用度までを確認するＩｏＴ機器の認証では、ＩｏＴ機器の所有者または管理者もしくは両方からの登録情報に基づいて、ＩｏＴ機器が登録どおりであるか、正常に管理されているか、管理者の資質や資格は適切か、センサの校正は適正になされているか、測定データは正しそうか、機器設定は適切か、設置方法や設置場所は適切かなどが確認され、問題なければ証明書が発行される。問題がある場合は指導が行われる。設置環境や管理体制など、デジタル的な情報のみでは判断しづらいものは、調査員が定期的または抜き打ちもしくは両方で行う。ＩｏＴ機器の設置場所がカメラ等で監視できるものであれば、遠隔で確認してもよい。これらの情報から、上述のような方法によって信用の度合いが算出される。 In the current IoT device certification, the IoT device authentication information is registered in the IoT device at the manufacturing stage or installation stage of the IoT device, and the received data is definitely the data transmitted from the target IoT device. It is confirmed whether or not. Although this authentication is effective for measures such as falsification and replacement of data in communication paths, it was not possible to confirm the creditworthiness of the target IoT device and the creditworthiness of the owner or administrator.
In the authentication of IoT device that confirms the creditworthiness of the target IoT device and the creditworthiness of the owner or manager, the IoT device is registered based on the registration information from the owner and / or manager of the IoT device. Is it managed normally, is the administrator's qualifications and qualifications appropriate, is the sensor calibrated properly, is the measurement data likely to be correct, is the equipment setting appropriate, is the installation method and installation location appropriate? If there is no problem, a certificate will be issued. Guidance will be provided if there is a problem. Investigators perform things that are difficult to judge from digital information alone, such as the installation environment and management system, on a regular basis, unannounced, or both. If the installation location of the IoT device can be monitored by a camera or the like, it may be confirmed remotely. From this information, the degree of credit is calculated by the method described above.
　図１２は第１実施形態のＩｏＴ機器信用度算出装置１の第２適用例のＩｏＴ機器信用度算出システムＳにおいて実行される処理の一例を示すフローチャートである。
　図１２に示す例では、ステップＳ３０１において、ＩｏＴ機器Ｄの信頼性の確認が行われ、信頼できるか否かが判定される。信頼できない場合には、ステップＳ３０２に進む。一方、信頼できる場合には、ステップＳ３０３に進む。
　ステップＳ３０２では、指導・是正が行われ、次いで、ステップＳ３０１に戻る。
　ステップＳ３０３では、証明書が発行される。
　次いで、ステップＳ３０４では、証明書の期限内であるか否かが判定される。証明書の期限内である場合には、ステップＳ３０５に進む。一方、証明書の期限内でない場合には、ステップＳ３０１に戻る。
　ステップＳ３０５では、ＩｏＴ機器Ｄが正常であるか否かが判定される。ＩｏＴ機器Ｄが正常である場合には、ステップＳ３０１に戻る。一方、ＩｏＴ機器Ｄが正常でない場合には、ステップＳ３０２に進む。 FIG. 12 is a flowchart showing an example of processing executed in the IoT device credit rating calculation system S of the second application example of the IoT device credit rating calculation device 1 of the first embodiment.
In the example shown in FIG. 12, in step S301, the reliability of the IoT device D is confirmed, and it is determined whether or not the IoT device D is reliable. If it is unreliable, the process proceeds to step S302. On the other hand, if it is reliable, the process proceeds to step S303.
In step S302, guidance / correction is performed, and then the process returns to step S301.
In step S303, a certificate is issued.
Next, in step S304, it is determined whether or not the certificate is within the expiration date. If the certificate is within the expiration date, the process proceeds to step S305. On the other hand, if the certificate is not within the expiration date, the process returns to step S301.
In step S305, it is determined whether or not the IoT device D is normal. If the IoT device D is normal, the process returns to step S301. On the other hand, if the IoT device D is not normal, the process proceeds to step S302.
　図１１および図１２に示す例では、証明書に期限が設定され、定期的に期限のチェックが行われ、証明書が必要に応じて再発行される。また、証明書が発行されたＩｏＴ機器Ｄに対しては、インターネットなどの通信回線を介して、モニタリングが行われる。
　測定データの信用度が保たれていない可能性が高いと判断された場合には、指導・是正が行われる。
　他の例では、強い公平性を求められた場合に、指導などコンサルティングの行為を行わないことが求められることもある。
　更に他の例では、測定データの信用度が保たれていない可能性が高いと判断された場合に、証明書の取り消しが実施されてもよい。 In the examples shown in FIGS. 11 and 12, the certificate has an expiration date, the expiration date is checked periodically, and the certificate is reissued as needed. In addition, the IoT device D for which the certificate has been issued is monitored via a communication line such as the Internet.
If it is determined that the reliability of the measurement data is not maintained, guidance and correction will be provided.
In other cases, when strong fairness is required, it may be required not to provide consulting services such as guidance.
In yet another example, the certificate may be revoked if it is determined that the credibility of the measured data is likely to be unreliable.
　以上のようなＩｏＴ機器のデータの信用の度合いが証明されたＩｏＴ機器もしくは、それを管理する個人またはグループもしくは両方は、信用できると証明されたデータを発信することが可能になり、データの価値を高めることが可能になる。
　これらのサービスの対価を求める場合は、お金、データの２次利用の権利、特定のセキュアチップの利用などが考えられ、これらを単独もしくは組み合わせて対価とする。 The IoT device whose degree of trust in the data of the IoT device as described above, or the individual or group who manages the IoT device, or both, can transmit the data proved to be reliable, and the value of the data. Can be increased.
When seeking compensation for these services, money, the right to secondary use of data, the use of a specific secure chip, etc. can be considered, and these are considered alone or in combination.
　図１３は第１実施形態のＩｏＴ機器信用度算出装置１の第３適用例を示す図である。詳細には、図１３は第１実施形態のＩｏＴ機器信用度算出装置１が適用されたＩｏＴ機器信用度算出システムＳを示している。
　図１２までに示して例では、データの利用者が、ＩｏＴ機器Ｄからデータを直接受け取る。一方、図１３に示す例では、データの利用者が、クラウドなどのデータベースに保存されたデータを受け取る。データベースＤＢに情報を集めると、ＩｏＴ機器Ｄへのデータアクセスの集中を防ぐことができる、クラウドのセキュリティーシステムを活用できるなどの利点がある。データベースＤＢ内での改ざんのリスクに対しては、データベースＤＢとしてブロックチェーンを用いれば登録したデータに改ざんがなされていないことが証明でき、登録する前の証明と組み合わせることで、より高いデータの信用度を得ることが可能になる。 FIG. 13 is a diagram showing a third application example of the IoT device credit rating calculation device 1 of the first embodiment. In detail, FIG. 13 shows the IoT device credit rating calculation system S to which the IoT device credit rating calculation device 1 of the first embodiment is applied.
In the example shown up to FIG. 12, the data user receives the data directly from the IoT device D. On the other hand, in the example shown in FIG. 13, a data user receives data stored in a database such as a cloud. Collecting information in the database DB has advantages such as preventing the concentration of data access to the IoT device D and utilizing the cloud security system. Regarding the risk of tampering in the database DB, if blockchain is used as the database DB, it can be proved that the registered data has not been tampered with, and by combining it with the proof before registration, the creditworthiness of the data is higher. Will be able to be obtained.
＜第２実施形態＞
　以下、添付図面を参照し、本発明のＩｏＴデータプラットフォーム、および、ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法の実施形態について説明する。 <Second Embodiment>
Hereinafter, embodiments of the IoT data platform of the present invention and the method for processing IoT data by the IoT data platform will be described with reference to the accompanying drawings.
　図１４は第２実施形態のＩｏＴデータプラットフォームＰの概略構成の一例などを示す図である。
　図１４に示す例では、ＩｏＴデータプラットフォームＰが、ＩｏＴデータ受信部Ｐ１と、第１対価算出部Ｐ２と、ＩｏＴデータ送信部Ｐ３と、第２対価算出部Ｐ４と、信用度情報受信部Ｐ５とを備えている。
　ＩｏＴデータ受信部Ｐ１は、ＩｏＴ機器Ｄ（図１参照）から発信されたＩｏＴデータ（例えばＩｏＴ機器ＤのセンサＤ１の測定データ）を受信する。第１対価算出部Ｐ２は、ＩｏＴデータ受信部Ｐ１によって受信されたＩｏＴデータに対する対価の根拠である第１対価を算出する。ＩｏＴデータ送信部Ｐ３は、ＩｏＴデータ受信部Ｐ１によって受信されたＩｏＴデータを送信する。第２対価算出部Ｐ４は、ＩｏＴデータ送信部Ｐ３によって送信されたＩｏＴデータに対する対価の根拠である第２対価を算出する。信用度情報受信部Ｐ５は、ＩｏＴデータ受信部Ｐ１によって受信されたＩｏＴデータの信用度に関する情報を受信する。
　詳細には、第１対価算出部Ｐ２は、信用度情報受信部Ｐ５によって受信されたＩｏＴデータの信用度に関する情報に基づいて第１対価を算出する。また、第２対価算出部Ｐ４は、信用度情報受信部Ｐ５によって受信されたＩｏＴデータの信用度に関する情報に基づいて第２対価を算出する。 FIG. 14 is a diagram showing an example of a schematic configuration of the IoT data platform P of the second embodiment.
In the example shown in FIG. 14, the IoT data platform P includes an IoT data receiving unit P1, a first consideration calculation unit P2, an IoT data transmission unit P3, a second consideration calculation unit P4, and a credit rating information receiving unit P5. I have.
The IoT data receiving unit P1 receives IoT data (for example, measurement data of the sensor D1 of the IoT device D) transmitted from the IoT device D (see FIG. 1). The first consideration calculation unit P2 calculates the first consideration which is the basis of the consideration for the IoT data received by the IoT data receiving unit P1. The IoT data transmission unit P3 transmits the IoT data received by the IoT data reception unit P1. The second consideration calculation unit P4 calculates the second consideration which is the basis of the consideration for the IoT data transmitted by the IoT data transmission unit P3. The credit rating information receiving unit P5 receives information regarding the credit rating of the IoT data received by the IoT data receiving unit P1.
Specifically, the first consideration calculation unit P2 calculates the first consideration based on the information regarding the credit rating of the IoT data received by the credit rating information receiving unit P5. Further, the second consideration calculation unit P4 calculates the second consideration based on the information regarding the credit rating of the IoT data received by the credit rating information receiving unit P5.
　図１５は第２実施形態のＩｏＴデータプラットフォームＰにおいて実行される処理の一例を説明するためのフローチャートである。
　図１５に示す例では、ステップＳ１において、ＩｏＴデータ受信部Ｐ１が、ＩｏＴ機器Ｄ（図１参照）から発信されたＩｏＴデータを受信する。
　次いで、ステップＳ２では、信用度情報受信部Ｐ５が、ＩｏＴデータ受信部Ｐ１によって受信されたＩｏＴデータの信用度に関する情報を受信する。
　次いで、ステップＳ３では、第１対価算出部Ｐ２が、ステップＳ２において受信されたＩｏＴデータの信用度に関する情報に基づいて、ステップＳ１において受信されたＩｏＴデータに対する対価の根拠である第１対価を算出する。
　また、ステップＳ４では、ＩｏＴデータ送信部Ｐ３が、ステップＳ１において受信されたＩｏＴデータを送信する。
　次いで、ステップＳ５では、第２対価算出部Ｐ４が、ステップＳ２において受信されたＩｏＴデータの信用度に関する情報に基づいて、ステップＳ４において送信されたＩｏＴデータに対する対価の根拠である第２対価を算出する。 FIG. 15 is a flowchart for explaining an example of processing executed in the IoT data platform P of the second embodiment.
In the example shown in FIG. 15, in step S1, the IoT data receiving unit P1 receives the IoT data transmitted from the IoT device D (see FIG. 1).
Next, in step S2, the credit rating information receiving unit P5 receives information regarding the credit rating of the IoT data received by the IoT data receiving unit P1.
Next, in step S3, the first consideration calculation unit P2 calculates the first consideration, which is the basis of the consideration for the IoT data received in step S1, based on the information regarding the creditworthiness of the IoT data received in step S2. ..
Further, in step S4, the IoT data transmission unit P3 transmits the IoT data received in step S1.
Next, in step S5, the second consideration calculation unit P4 calculates the second consideration, which is the basis of the consideration for the IoT data transmitted in step S4, based on the information regarding the creditworthiness of the IoT data received in step S2. ..
　第２実施形態のＩｏＴデータプラットフォームＰでは、送受信されるＩｏＴデータの信用度に関する情報に基づいて、送受信されるＩｏＴデータに対する対価の根拠が算出されるため、ＩｏＴデータを利用する利便性を向上させることができる。 In the IoT data platform P of the second embodiment, the basis of consideration for the transmitted / received IoT data is calculated based on the information on the creditworthiness of the transmitted / received IoT data, so that the convenience of using the IoT data is improved. Can be done.
　図１６は第２実施形態のＩｏＴデータプラットフォームＰが適用されたＩｏＴデータ流通システムＤＳの一例を示す図である。
　図１６に示す例では、ＩｏＴデータプラットフォームＰのＩｏＴデータ受信部Ｐ１（図１４参照）が、ＩｏＴデータ送信部Ｐ３（図１４参照）によって送信されたＩｏＴデータを利用するＩｏＴデータ利用部Ｕの情報を秘匿化した状態で、ＩｏＴ機器Ｄ（図１参照）から発信されたＩｏＴデータを所有していたＩｏＴデータ所有部ＷからＩｏＴデータを受信する。また、ＩｏＴデータ送信部Ｐ３は、ＩｏＴデータ所有部Ｗの情報を秘匿化した状態で、ＩｏＴデータ利用部ＵにＩｏＴデータを送信する。
　また、信用度情報受信部Ｐ５は、ＩｏＴデータの信用度に関する情報を格付け部Ｒから受信する。 FIG. 16 is a diagram showing an example of an IoT data distribution system DS to which the IoT data platform P of the second embodiment is applied.
In the example shown in FIG. 16, the information of the IoT data utilization unit U in which the IoT data reception unit P1 (see FIG. 14) of the IoT data platform P uses the IoT data transmitted by the IoT data transmission unit P3 (see FIG. 14). The IoT data is received from the IoT data possessing unit W that possesses the IoT data transmitted from the IoT device D (see FIG. 1) in a concealed state. Further, the IoT data transmission unit P3 transmits IoT data to the IoT data utilization unit U in a state where the information of the IoT data possession unit W is concealed.
Further, the credit rating information receiving unit P5 receives information on the credit rating of the IoT data from the rating unit R.
　ＩｏＴデータプラットフォームＰはデータプライシング機能を有する。つまり、ＩｏＴデータプラットフォームＰでは、データ利用者（ＩｏＴデータ利用部Ｕ）からの要求（需要）に対し、データ発信者（ＩｏＴデータ所有部Ｗ）に応じて、利用価格が決定される。双方の合意に達したとき、情報の交換がなされる。
　このとき、ＩｏＴデータの信頼性について格付け会社（格付け部Ｒ）の情報を参照、加味し、ＩｏＴデータの信頼性も価格に反映することが可能となる。信頼性も価格設定の根拠として表示することができる。
　また、ＩｏＴデータプラットフォームＰでは、情報が取り引きされるときのプレミアムや割引が意図的に加えられるような処理が実行されてもよい。 The IoT data platform P has a data pricing function. That is, in the IoT data platform P, the usage price is determined according to the data sender (IoT data possession unit W) in response to the request (demand) from the data user (IoT data utilization unit U). Information will be exchanged when mutual agreement is reached.
At this time, the reliability of the IoT data can be reflected in the price by referring to and adding the information of the rating agency (Rating Department R). Reliability can also be displayed as the basis for pricing.
Further, in the IoT data platform P, a process may be executed in which a premium or a discount is intentionally added when information is traded.
　ＩｏＴデータプラットフォームＰでは、データ所有者（ＩｏＴデータ所有部Ｗ）および利用者（ＩｏＴデータ利用部Ｕ）等の情報を匿名化し、個人情報を介さずにＩｏＴデータの流通が管理、運営されることが望ましい。
　格付け会社（格付け部Ｒ）は、匿名性の保証に関しても評価、格付けを行う情報の一つとして利用できる。 In the IoT data platform P, the information of the data owner (IoT data owner W) and the user (IoT data user U) is anonymized, and the distribution of IoT data is managed and operated without personal information. Is desirable.
The rating agency (Rating Department R) can also use it as one of the information for evaluating and rating the guarantee of anonymity.
　ＩｏＴデータプラットフォームＰでは、ブロックチェーンなどの技術だけでなく既存のクラウドセキュリティシステムを使うことで、ＩｏＴデータの所有者（ＩｏＴデータ所有部Ｗ）、流路、移動などの履歴を管理することができ、データ改ざんに対して強い手段として利用できる。
　データ利用者（ＩｏＴデータ利用部Ｕ）は、ＩｏＴデータプラットフォームＰを利用することで、使いたいＩｏＴデータを簡単に検索でき、必要条件に応じたデータを入手しやすくなる。また、利用したいＩｏＴデータを入手するときにＩｏＴデータプラットフォームＰにリクエストをかけ、信用度の高い情報を簡単に利用できるサービスを受けられる。
　データ所有者（ＩｏＴデータ所有部Ｗ）は、ＩｏＴデータプラットフォームＰを活用することで、データ利用者（ＩｏＴデータ利用部Ｕ）を簡単に見つけることが可能となり、データの対価を得やすくなる。また、データ利用者（ＩｏＴデータ利用部Ｕ）が欲している情報（需要の大きなデータ）も得ることが可能となる。流通させるデータの利用範囲、期間等も提供側（ＩｏＴデータ所有部Ｗ）が自由に設定可能であり、提供価値も担保される。
　格付け会社（格付け部Ｒ）は、ＩｏＴデータプラットフォームＰのセキュリティ、管理状況、利用状態を監査し、格付け、認証、是正を行う。リアルタイム性、検索性などユーザの利便性も評価情報として利用できる。また、ＩｏＴデータプラットフォームＰのデータ管理がブロックチェーンを用いて管理されている場合、データ改ざん等が起こりにくいと判断でき、スコア格付け用の情報の一つとして利用できる。公平性が担保される必要性がある場合には、指導などのコンサルティングの行為は行われないことが求められる場合もある。
　このＩｏＴデータプラットフォームＰはＩｏＴデバイス（ＩｏＴ機器Ｄ）からの情報だけでなく、広くデータを取扱い、流通させるプラットフォームとして利用することが可能である。 In IoT data platform P, it is possible to manage the history of IoT data owner (IoT data owner W), flow path, movement, etc. by using not only technology such as blockchain but also existing cloud security system. , Can be used as a strong means against data tampering.
By using the IoT data platform P, the data user (IoT data utilization unit U) can easily search for the IoT data that he / she wants to use, and can easily obtain the data according to the necessary conditions. In addition, when obtaining the IoT data to be used, a request is made to the IoT data platform P to receive a service that makes it easy to use highly reliable information.
By utilizing the IoT data platform P, the data owner (IoT data owner unit W) can easily find the data user (IoT data user unit U), and it becomes easier to obtain the compensation for the data. In addition, it is possible to obtain the information (data with large demand) that the data user (IoT data utilization unit U) wants. The range of use and period of data to be distributed can be freely set by the provider (IoT data ownership unit W), and the value provided is also guaranteed.
The rating agency (Rating Department R) audits the security, management status, and usage status of the IoT data platform P, and performs rating, authentication, and correction. User convenience such as real-time and searchability can also be used as evaluation information. Further, when the data management of the IoT data platform P is managed by using the blockchain, it can be determined that data tampering is unlikely to occur, and it can be used as one of the information for score rating. If there is a need to ensure fairness, it may be required that no consulting activities such as guidance be provided.
This IoT data platform P can be used as a platform for widely handling and distributing data as well as information from an IoT device (IoT device D).
　以上、本発明を実施するための形態について実施形態を用いて説明したが、本発明はこうした実施形態に何等限定されるものではなく、本発明の要旨を逸脱しない範囲内において種々の変形および置換を加えることができる。上述した各実施形態および各例に記載の構成を適宜組み合わせてもよい。 Although the embodiments for carrying out the present invention have been described above using the embodiments, the present invention is not limited to these embodiments, and various modifications and substitutions are made without departing from the gist of the present invention. Can be added. The configurations described in each of the above-described embodiments and examples may be appropriately combined.
　なお、上述した実施形態におけるＩｏＴ機器信用度算出装置１およびＩｏＴデータプラットフォームＰが備える各部の機能全体あるいはその一部は、これらの機能を実現するためのプログラムをコンピュータ読み取り可能な記録媒体に記録して、この記録媒体に記録されたプログラムをコンピュータシステムに読み込ませ、実行することによって実現しても良い。なお、ここでいう「コンピュータシステム」とは、ＯＳや周辺機器等のハードウェアを含むものとする。
　また、「コンピュータ読み取り可能な記録媒体」とは、フレキシブルディスク、光磁気ディスク、ＲＯＭ、ＣＤ－ＲＯＭ等の可搬媒体、コンピュータシステムに内蔵されるハードディスク等の記憶部のことをいう。さらに「コンピュータ読み取り可能な記録媒体」とは、インターネット等のネットワークや電話回線等の通信回線を介してプログラムを送信する場合の通信線のように、短時間の間、動的にプログラムを保持するもの、その場合のサーバやクライアントとなるコンピュータシステム内部の揮発性メモリのように、一定時間プログラムを保持しているものも含んでも良い。また上記プログラムは、前述した機能の一部を実現するためのものであっても良く、さらに前述した機能をコンピュータシステムにすでに記録されているプログラムとの組み合わせで実現できるものであっても良い。 In addition, all or a part of the functions of each part included in the IoT device credit calculation device 1 and the IoT data platform P in the above-described embodiment are recorded on a computer-readable recording medium with a program for realizing these functions. , The program recorded on this recording medium may be read by a computer system and executed. The term "computer system" as used herein includes hardware such as an OS and peripheral devices.
Further, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, or a CD-ROM, or a storage unit such as a hard disk built in a computer system. Further, a "computer-readable recording medium" is a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, and dynamically holds the program for a short period of time. In this case, a volatile memory inside the computer system that serves as a server or a client in that case may hold a program for a certain period of time. Further, the above-mentioned program may be a program for realizing a part of the above-mentioned functions, and may be a program for realizing the above-mentioned functions in combination with a program already recorded in the computer system.
（付記）
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記ＩｏＴ機器は、前記ＩｏＴ機器がソフト的および／またはハード的に改造されたか否か、前記ＩｏＴ機器の履歴および／または前記ＩｏＴ機器のログデータをチェックするチェック部を更に備え、前記第３取得部は、前記チェック部によってチェックされた前記ＩｏＴ機器がソフト的および／またはハード的に改造されたか否か、前記ＩｏＴ機器の履歴および／または前記ＩｏＴ機器のログデータを取得し、前記ＩｏＴ機器属性分析部は、前記第３取得部によって取得された前記ＩｏＴ機器がソフト的および／またはハード的に改造されたか否か、前記ＩｏＴ機器の履歴および／または前記ＩｏＴ機器のログデータに基づいて、前記ＩｏＴ機器の属性の分析を実行してもよい。 (Additional note)
In the IoT device credit rating device of one aspect of the present invention, the IoT device is the history of the IoT device and / or the log data of the IoT device, whether or not the IoT device has been modified in terms of software and / or hardware. The third acquisition unit further includes a check unit for checking whether or not the IoT device checked by the check unit has been modified in a soft and / or hard manner, a history of the IoT device and / or the above. The log data of the IoT device is acquired, and the IoT device attribute analysis unit determines whether or not the IoT device acquired by the third acquisition unit has been modified in terms of software and / or hardware, the history of the IoT device, and the history of the IoT device. / Or, the attribute analysis of the IoT device may be performed based on the log data of the IoT device.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記信用度算出部は、多変量解析または状態ポイント計算を実行することによって、前記ＩｏＴ機器から発信された前記測定データの信用度としての信用スコアを算出してもよい。 In the IoT device credit rating device of one aspect of the present invention, the credit rating calculation unit calculates the credit score as the credit rating of the measurement data transmitted from the IoT device by executing multivariate analysis or state point calculation. You may.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記信用度算出部は、前記ＩｏＴ機器から発信された前記測定データの信用度としての数値またはランクを算出してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the credit rating calculation unit may calculate a numerical value or rank as the credit rating of the measurement data transmitted from the IoT device.
　本発明の一態様のＩｏＴ機器信用度算出装置では、前記ＩｏＴ機器は、前記所有者情報と前記属性情報とを記憶する記憶部を更に備え、前記第２取得部は、前記記憶部に記憶されている前記所有者情報を取得し、前記第３取得部は、前記記憶部に記憶されている前記属性情報を取得してもよい。 In the IoT device credit rating calculation device of one aspect of the present invention, the IoT device further includes a storage unit for storing the owner information and the attribute information, and the second acquisition unit is stored in the storage unit. The owner information is acquired, and the third acquisition unit may acquire the attribute information stored in the storage unit.
　本発明の一態様のＩｏＴ機器信用度算出装置の利用方法では、前記証明書が電子証明書であってもよい。 In the method of using the IoT device credit rating device according to one aspect of the present invention, the certificate may be an electronic certificate.
　本発明の一態様は、ＩｏＴ機器信用度算出装置の利用方法であって、前記信用度算出部によって算出された前記測定データの信用度が、前記ＩｏＴ機器の格付けに利用される、ＩｏＴ機器信用度算出装置の利用方法である。 One aspect of the present invention is a method of using the IoT device credit rating device, wherein the credit rating of the measurement data calculated by the credit rating calculation unit is used for rating the IoT device. How to use.
　本発明の一態様のＩｏＴデータプラットフォームでは、前記信用度情報受信部は、前記ＩｏＴデータの信用度に関する情報を格付け部から受信してもよい。 In the IoT data platform of one aspect of the present invention, the credit rating information receiving unit may receive information on the credit rating of the IoT data from the rating unit.
１…ＩｏＴ機器信用度算出装置、１１…第１取得部、１２…第２取得部、１３…第３取得部、１４…測定データ分析部、１５…所有者分析部、１６…ＩｏＴ機器属性分析部、１７…信用度算出部、１８…記憶部、Ｓ…ＩｏＴ機器信用度算出システム、Ｄ…ＩｏＴ機器、Ｄ１…センサ、Ｄ２…モニタリング部、Ｄ３…チェック部、Ｄ４…記憶部、ＮＷ…ネットワーク、ＤＢ…データベース、Ｐ…ＩｏＴデータプラットフォーム、Ｐ１…ＩｏＴデータ受信部、Ｐ２…第１対価算出部、Ｐ３…ＩｏＴデータ送信部、Ｐ４…第２対価算出部、Ｐ５…信用度情報受信部、ＤＳ…ＩｏＴデータ流通システム、Ｒ…格付け部、Ｗ…ＩｏＴデータ所有部、Ｕ…ＩｏＴデータ利用部 1 ... IoT device credit calculation device, 11 ... 1st acquisition unit, 12 ... 2nd acquisition unit, 13 ... 3rd acquisition unit, 14 ... measurement data analysis unit, 15 ... owner analysis unit, 16 ... IoT equipment attribute analysis unit , 17 ... Credit calculation unit, 18 ... Storage unit, S ... IoT device Credit calculation system, D ... IoT device, D1 ... Sensor, D2 ... Monitoring unit, D3 ... Check unit, D4 ... Storage unit, NW ... Network, DB ... Database, P ... IoT data platform, P1 ... IoT data receiving unit, P2 ... 1st consideration calculation unit, P3 ... IoT data transmission unit, P4 ... 2nd consideration calculation unit, P5 ... Credit information receiving unit, DS ... IoT data distribution System, R ... Rating Department, W ... IoT Data Ownering Department, U ... IoT Data Usage Department

Claims (20)

  1. 　センサを有するＩｏＴ機器から経時的に発信された測定データを取得する第１取得部と、
    　前記ＩｏＴ機器から発信された前記測定データ以外の情報である非測定情報を取得する非測定情報取得部と、
    　前記第１取得部によって取得された前記測定データと、前記非測定情報取得部によって取得された前記非測定情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する信用度算出部とを備えるＩｏＴ機器信用度算出装置。     The first acquisition unit that acquires measurement data transmitted over time from an IoT device that has a sensor,
    A non-measurement information acquisition unit that acquires non-measurement information that is information other than the measurement data transmitted from the IoT device, and a non-measurement information acquisition unit.
    Credit rating calculation to calculate the credit rating of the measurement data transmitted from the IoT device based on the measurement data acquired by the first acquisition unit and the non-measurement information acquired by the non-measurement information acquisition section. An IoT device credit calculation device including a unit.
  2. 　前記非測定情報には、前記ＩｏＴ機器の所有者および／または管理者の情報である所有者情報と、前記ＩｏＴ機器の属性に関する情報である属性情報とが含まれ、
    　前記非測定情報取得部は、
    　前記所有者情報を取得する第２取得部と、
    　前記属性情報を取得する第３取得部とを備え、
    　前記信用度算出部は、前記第１取得部によって取得された前記測定データと、前記第２取得部によって取得された前記所有者情報と、前記第３取得部によって取得された前記属性情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する、
    　請求項１に記載のＩｏＴ機器信用度算出装置。     The non-measurement information includes owner information which is information on the owner and / or administrator of the IoT device, and attribute information which is information on attributes of the IoT device.
    The non-measurement information acquisition unit
    The second acquisition unit that acquires the owner information and
    It is provided with a third acquisition unit that acquires the attribute information.
    The credit rating calculation unit is based on the measurement data acquired by the first acquisition unit, the owner information acquired by the second acquisition unit, and the attribute information acquired by the third acquisition unit. To calculate the creditworthiness of the measurement data transmitted from the IoT device.
    The IoT device credit rating calculation device according to claim 1.
  3. 　前記第１取得部によって取得された前記測定データの分析を実行する測定データ分析部と、
    　前記第２取得部によって取得された前記所有者情報に基づいて、前記ＩｏＴ機器の所有者および／または管理者の分析を実行する所有者分析部と、
    　前記第３取得部によって取得された前記属性情報に基づいて、前記ＩｏＴ機器の属性の分析を実行するＩｏＴ機器属性分析部とを更に備え、
    　前記信用度算出部は、前記測定データ分析部の分析結果と前記所有者分析部の分析結果と前記ＩｏＴ機器属性分析部の分析結果とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する、
    　請求項２に記載のＩｏＴ機器信用度算出装置。     A measurement data analysis unit that executes analysis of the measurement data acquired by the first acquisition unit,
    An owner analysis unit that executes analysis of the owner and / or manager of the IoT device based on the owner information acquired by the second acquisition unit.
    An IoT device attribute analysis unit that executes an analysis of the attributes of the IoT device based on the attribute information acquired by the third acquisition unit is further provided.
    The credit rating calculation unit has the credit rating of the measurement data transmitted from the IoT device based on the analysis result of the measurement data analysis section, the analysis result of the owner analysis section, and the analysis result of the IoT device attribute analysis section. To calculate,
    The IoT device credit rating calculation device according to claim 2.
  4. 　前記測定データ分析部は、
    　前記第１取得部によって取得された前記測定データと、前記第１取得部によって過去に取得された過去分測定データとの比較を実行すると共に、
    　前記第１取得部によって取得された前記測定データと、前記ＩｏＴ機器以外のＩｏＴ機器から発信されたデータとの比較を実行する、
    　請求項３に記載のＩｏＴ機器信用度算出装置。     The measurement data analysis unit
    The measurement data acquired by the first acquisition unit is compared with the past measurement data acquired in the past by the first acquisition unit, and the comparison is performed.
    A comparison is performed between the measurement data acquired by the first acquisition unit and the data transmitted from the IoT device other than the IoT device.
    The IoT device credit rating calculation device according to claim 3.
  5. 　前記測定データ分析部は、
    　前記第１取得部によって取得された前記測定データに対する人為的な操作の痕跡の有無の分析を実行する、
    　請求項４に記載のＩｏＴ機器信用度算出装置。     The measurement data analysis unit
    An analysis of the presence or absence of traces of artificial manipulation on the measurement data acquired by the first acquisition unit is performed.
    The IoT device credit rating calculation device according to claim 4.
  6. 　前記測定データ分析部は、
    　前記測定データの変化の特徴を図形化した後にデータマイニング処理を実行することによって、前記測定データの分析を実行する、
    　請求項３に記載のＩｏＴ機器信用度算出装置。     The measurement data analysis unit
    An analysis of the measurement data is performed by performing a data mining process after graphicizing the characteristics of changes in the measurement data.
    The IoT device credit rating calculation device according to claim 3.
  7. 　前記所有者分析部は、
    　前記ＩｏＴ機器の所有者および／または管理者の申告内容、過去の履歴、他のデータベースから得られた情報、および、調査結果から得られた情報の少なくともいずれかに基づいて、前記ＩｏＴ機器の所有者および／または管理者が、前記ＩｏＴ機器の正規の所有者および／または管理者であるか否かを分析する、
    　請求項３に記載のＩｏＴ機器信用度算出装置。     The owner analysis department
    Ownership of the IoT device based on at least one of the declarations made by the owner and / or administrator of the IoT device, past history, information obtained from other databases, and information obtained from the survey results. Analyze whether the person and / or administrator is the authorized owner and / or administrator of the IoT device.
    The IoT device credit rating calculation device according to claim 3.
  8. 　前記所有者分析部は、
    　前記ＩｏＴ機器の所有者および／または管理者が前記ＩｏＴ機器に関する情報を発信しているか否か、前記ＩｏＴ機器の所有者および／または管理者が過去に発信した前記ＩｏＴ機器に関する情報の内容、前記ＩｏＴ機器の所有者および／または管理者が所有または管理している他のＩｏＴ機器の管理状況、前記ＩｏＴ機器の所有者および／または管理者が前記他のＩｏＴ機器の廃棄処理を適切に実行しているか否か、前記ＩｏＴ機器の所有者および／または管理者の与信に関する情報、および、前記ＩｏＴ機器の所有者および／または管理者の管理体制に関する情報の少なくともいずれかに基づいて、前記ＩｏＴ機器の所有者および／または管理者の分析を実行する、
    　請求項３に記載のＩｏＴ機器信用度算出装置。     The owner analysis department
    Whether or not the owner and / or administrator of the IoT device has transmitted information about the IoT device, the content of the information about the IoT device that the owner and / or administrator of the IoT device has transmitted in the past, the above. The management status of other IoT devices owned or managed by the owner and / or administrator of the IoT device, the owner and / or administrator of the IoT device appropriately executes the disposal process of the other IoT device. Based on at least one of the information about the credit of the owner and / or the manager of the IoT device and the information about the management system of the owner and / or the manager of the IoT device. Perform an analysis of the owner and / or administrator of
    The IoT device credit rating calculation device according to claim 3.
  9. 　前記ＩｏＴ機器属性分析部は、
    　前記ＩｏＴ機器の製造元、前記ＩｏＴ機器の製造時期、前記ＩｏＴ機器の校正および／またはメンテナンスが実行されたか否か、前記ＩｏＴ機器の校正および／またはメンテナンスが実行された時期、前記ＩｏＴ機器の校正および／またはメンテナンスの内容、前記ＩｏＴ機器の応答、前記ＩｏＴ機器にセキュアチップが備えられているか否か、前記ＩｏＴ機器の動作状況、前記ＩｏＴ機器に関する認証情報、前記ＩｏＴ機器の設置場所、前記ＩｏＴ機器の設置環境および／または測定環境、前記ＩｏＴ機器の前記センサによる前記測定データの測定の難易度、前記ＩｏＴ機器によって発信された前記測定データの通信経路、および、前記ＩｏＴ機器における暗号化のレベルの少なくともいずれかに基づいて、前記ＩｏＴ機器の属性の分析を実行する、
    　請求項３に記載のＩｏＴ機器信用度算出装置。     The IoT device attribute analysis unit
    Manufacturer of the IoT device, when the IoT device was manufactured, whether or not the calibration and / or maintenance of the IoT device was performed, when the calibration and / or maintenance of the IoT device was performed, and when the IoT device was calibrated and / or maintained. / Or the content of maintenance, the response of the IoT device, whether or not the IoT device is equipped with a secure chip, the operating status of the IoT device, the authentication information about the IoT device, the installation location of the IoT device, the IoT device. Installation environment and / or measurement environment, the difficulty of measuring the measurement data by the sensor of the IoT device, the communication path of the measurement data transmitted by the IoT device, and the level of encryption in the IoT device. Perform an analysis of the attributes of the IoT device based on at least one of the above.
    The IoT device credit rating calculation device according to claim 3.
  10. 　前記ＩｏＴ機器は、前記ＩｏＴ機器の消費電力、前記ＩｏＴ機器の演算部の温度および／または前記ＩｏＴ機器のケースの開閉状態をモニタリングするモニタリング部を更に備え、
    　前記第３取得部は、前記モニタリング部によってモニタリングされた前記ＩｏＴ機器の消費電力、前記ＩｏＴ機器の演算部の温度および／または前記ＩｏＴ機器のケースの開閉状態の情報を取得し、
    　前記ＩｏＴ機器属性分析部は、
    　前記第３取得部によって取得された前記ＩｏＴ機器の消費電力、前記ＩｏＴ機器の演算部の温度および／または前記ＩｏＴ機器のケースの開閉状態の情報に基づいて、前記ＩｏＴ機器の属性の分析を実行する、
    　請求項９に記載のＩｏＴ機器信用度算出装置。     The IoT device further includes a monitoring unit that monitors the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and / or the open / closed state of the case of the IoT device.
    The third acquisition unit acquires information on the power consumption of the IoT device, the temperature of the calculation unit of the IoT device, and / or the open / closed state of the case of the IoT device, which is monitored by the monitoring unit.
    The IoT device attribute analysis unit
    The attribute analysis of the IoT device is executed based on the power consumption of the IoT device acquired by the third acquisition unit, the temperature of the calculation unit of the IoT device, and / or the open / closed state information of the case of the IoT device. To do,
    The IoT device credit rating calculation device according to claim 9.
  11. 　前記信用度算出部が、前記ＩｏＴ機器から発信された前記測定データの信用度を算出した後に、
    　前記信用度算出部は、
    　前記測定データ分析部によって継続的に実行された分析の結果と、前記所有者分析部によって継続的に実行された分析の結果と、前記ＩｏＴ機器属性分析部によって継続的に実行された分析の結果とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を継続的に算出する、
    　請求項３に記載のＩｏＴ機器信用度算出装置。     After the credit rating calculation unit calculates the credit rating of the measurement data transmitted from the IoT device,
    The credit rating calculation unit
    The result of the analysis continuously performed by the measurement data analysis unit, the result of the analysis continuously performed by the owner analysis unit, and the result of the analysis continuously performed by the IoT device attribute analysis unit. Based on the above, the credit rating of the measurement data transmitted from the IoT device is continuously calculated.
    The IoT device credit rating calculation device according to claim 3.
  12. 　前記信用度算出部は、人工知能を利用することによって、前記ＩｏＴ機器から発信された前記測定データの信用度としての信用スコアを算出する、
    　請求項１に記載のＩｏＴ機器信用度算出装置。     The credit rating calculation unit calculates a credit score as the credit rating of the measurement data transmitted from the IoT device by using artificial intelligence.
    The IoT device credit rating calculation device according to claim 1.
  13. 　請求項１に記載のＩｏＴ機器信用度算出装置と、
    　前記ＩｏＴ機器と、
    　ネットワークとを備えるＩｏＴ機器信用度算出システムであって、
    　前記ＩｏＴ機器信用度算出装置は、前記ＩｏＴ機器の所有、管理および製造のいずれも行っていない者によって所有または管理され、
    　前記ＩｏＴ機器は、前記ネットワークを介して前記ＩｏＴ機器信用度算出装置の前記第１取得部と前記ＩｏＴ機器信用度算出装置以外の箇所とに前記測定データを自動的に発信可能に構成されている、
    　ＩｏＴ機器信用度算出システム。     The IoT device credit rating device according to claim 1 and
    With the IoT device
    It is an IoT device credit calculation system equipped with a network.
    The IoT device credit calculation device is owned or controlled by a person who does not own, control or manufacture the IoT device.
    The IoT device is configured to be able to automatically transmit the measurement data to a location other than the first acquisition unit of the IoT device credit calculation device and the IoT device credit calculation device via the network.
    IoT device credit calculation system.
  14. 　請求項１に記載のＩｏＴ機器信用度算出装置の利用方法であって、
    　前記信用度算出部によって算出された前記測定データの信用度が、前記ＩｏＴ機器の証明書の発行に利用される、
    　ＩｏＴ機器信用度算出装置の利用方法。     The method of using the IoT device credit rating calculation device according to claim 1.
    The credit rating of the measurement data calculated by the credit rating calculation unit is used for issuing a certificate of the IoT device.
    How to use the IoT device credit calculation device.
  15. 　請求項１に記載のＩｏＴ機器信用度算出装置の利用方法であって、
    　前記信用度算出部によって算出された前記測定データの信用度が、前記ＩｏＴ機器の所有者および／または管理者の格付け、および／または、前記ＩｏＴ機器の設置エリアの格付けに利用される、
    　ＩｏＴ機器信用度算出装置の利用方法。     The method of using the IoT device credit rating calculation device according to claim 1.
    The credit rating of the measurement data calculated by the credit rating calculation unit is used for rating the owner and / or manager of the IoT device and / or rating the installation area of the IoT device.
    How to use the IoT device credit calculation device.
  16. 　センサを有するＩｏＴ機器から経時的に発信された測定データを取得する第１取得ステップと、
    　前記ＩｏＴ機器から発信された前記測定データ以外の情報である非測定情報を取得する非測定情報取得ステップと、
    　前記第１取得ステップにおいて取得された前記測定データと、前記非測定情報取得ステップにおいて取得された前記非測定情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する信用度算出ステップとを備えるＩｏＴ機器信用度算出方法。     The first acquisition step of acquiring the measurement data transmitted over time from the IoT device having the sensor,
    A non-measurement information acquisition step for acquiring non-measurement information which is information other than the measurement data transmitted from the IoT device, and
    Credit rating calculation to calculate the credit rating of the measurement data transmitted from the IoT device based on the measurement data acquired in the first acquisition step and the non-measurement information acquired in the non-measurement information acquisition step. An IoT device credit rating calculation method including steps.
  17. 　コンピュータに、
    　センサを有するＩｏＴ機器から経時的に発信された測定データを取得する第１取得ステップと、
    　前記ＩｏＴ機器から発信された前記測定データ以外の情報である非測定情報を取得する非測定情報取得ステップと、
    　前記第１取得ステップにおいて取得された前記測定データと、前記非測定情報取得ステップにおいて取得された前記非測定情報とに基づいて、前記ＩｏＴ機器から発信された前記測定データの信用度を算出する信用度算出ステップと
    　を実行させるためのプログラムが記録され、前記コンピュータによって読み取り可能な記憶媒体。     On the computer
    The first acquisition step of acquiring the measurement data transmitted over time from the IoT device having the sensor,
    A non-measurement information acquisition step for acquiring non-measurement information which is information other than the measurement data transmitted from the IoT device, and
    Credit rating calculation for calculating the credit rating of the measurement data transmitted from the IoT device based on the measurement data acquired in the first acquisition step and the non-measurement information acquired in the non-measurement information acquisition step. A storage medium on which a program for performing steps and executions is recorded and readable by the computer.
  18. 　ＩｏＴ機器から発信されたＩｏＴデータを受信するＩｏＴデータ受信部と、
    　前記ＩｏＴデータ受信部によって受信された前記ＩｏＴデータに対する対価の根拠である第１対価を算出する第１対価算出部と、
    　前記ＩｏＴデータ受信部によって受信された前記ＩｏＴデータを送信するＩｏＴデータ送信部と、
    　前記ＩｏＴデータ送信部によって送信された前記ＩｏＴデータに対する対価の根拠である第２対価を算出する第２対価算出部と、
    　前記ＩｏＴデータ受信部によって受信された前記ＩｏＴデータの信用度に関する情報を受信する信用度情報受信部とを備え、
    　前記第１対価算出部は、前記信用度情報受信部によって受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第１対価を算出し、
    　前記第２対価算出部は、前記信用度情報受信部によって受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第２対価を算出する、
    　ＩｏＴデータプラットフォーム。     An IoT data receiver that receives IoT data transmitted from IoT devices,
    The first consideration calculation unit for calculating the first consideration, which is the basis of the consideration for the IoT data received by the IoT data receiving unit,
    An IoT data transmission unit that transmits the IoT data received by the IoT data reception unit, and an IoT data transmission unit.
    A second consideration calculation unit that calculates the second consideration that is the basis of the consideration for the IoT data transmitted by the IoT data transmission unit, and
    It includes a credit rating information receiving unit that receives information on the credit rating of the IoT data received by the IoT data receiving unit.
    The first consideration calculation unit calculates the first consideration based on the information regarding the creditworthiness of the IoT data received by the creditworthiness information receiving unit.
    The second consideration calculation unit calculates the second consideration based on the information regarding the credit rating of the IoT data received by the credit rating information receiving unit.
    IoT data platform.
  19. 　前記ＩｏＴデータ受信部は、前記ＩｏＴデータ送信部によって送信された前記ＩｏＴデータを利用するＩｏＴデータ利用部の情報を秘匿化した状態で、前記ＩｏＴ機器から発信された前記ＩｏＴデータを所有していたＩｏＴデータ所有部から前記ＩｏＴデータを受信し、
    　前記ＩｏＴデータ送信部は、前記ＩｏＴデータ所有部の情報を秘匿化した状態で、前記ＩｏＴデータ利用部に前記ＩｏＴデータを送信する、
    　請求項１８に記載のＩｏＴデータプラットフォーム。     The IoT data receiving unit possesses the IoT data transmitted from the IoT device in a state in which the information of the IoT data using unit that uses the IoT data transmitted by the IoT data transmitting unit is concealed. Receive the IoT data from the IoT data possession unit and
    The IoT data transmission unit transmits the IoT data to the IoT data utilization unit in a state where the information of the IoT data possession unit is concealed.
    The IoT data platform according to claim 18.
  20. 　ＩｏＴデータプラットフォームが、
    　ＩｏＴ機器から発信されたＩｏＴデータを受信するＩｏＴデータ受信ステップと、
    　前記ＩｏＴデータ受信ステップにおいて受信された前記ＩｏＴデータに対する対価の根拠である第１対価を算出する第１対価算出ステップと、
    　前記ＩｏＴデータ受信ステップにおいて受信された前記ＩｏＴデータを送信するＩｏＴデータ送信ステップと、
    　前記ＩｏＴデータ送信ステップにおいて送信された前記ＩｏＴデータに対する対価の根拠である第２対価を算出する第２対価算出ステップと、
    　前記ＩｏＴデータ受信ステップにおいて受信された前記ＩｏＴデータの信用度に関する情報を受信する信用度情報受信ステップとを備え、
    　前記第１対価算出ステップでは、前記信用度情報受信ステップにおいて受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第１対価を算出し、
    　前記第２対価算出ステップでは、前記信用度情報受信ステップにおいて受信された前記ＩｏＴデータの信用度に関する情報に基づいて前記第２対価を算出する、
    　ＩｏＴデータプラットフォームによるＩｏＴデータの処理方法。     IoT data platform
    An IoT data receiving step for receiving IoT data transmitted from an IoT device, and
    The first consideration calculation step for calculating the first consideration which is the basis of the consideration for the IoT data received in the IoT data receiving step, and
    An IoT data transmission step for transmitting the IoT data received in the IoT data reception step, and an IoT data transmission step.
    The second consideration calculation step for calculating the second consideration which is the basis of the consideration for the IoT data transmitted in the IoT data transmission step, and
    It includes a credit rating information receiving step for receiving information regarding the credit rating of the IoT data received in the IoT data receiving step.
    In the first consideration calculation step, the first consideration is calculated based on the information regarding the credit rating of the IoT data received in the credit rating information receiving step.
    In the second consideration calculation step, the second consideration is calculated based on the information regarding the credit rating of the IoT data received in the credit rating information receiving step.
    How to process IoT data with the IoT data platform.
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WO2013024673A1 (en) * 2011-08-12 2013-02-21 オムロン株式会社 Information management device, network system, information management program, and information management method
JP2017219930A (en) * 2016-06-03 2017-12-14 日本電信電話株式会社 IoT device sharing device, IoT device sharing method and program
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* Cited by examiner, † Cited by third party
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WO2013024673A1 (en) * 2011-08-12 2013-02-21 オムロン株式会社 Information management device, network system, information management program, and information management method
JP2017219930A (en) * 2016-06-03 2017-12-14 日本電信電話株式会社 IoT device sharing device, IoT device sharing method and program
KR20190134930A (en) * 2018-05-17 2019-12-05 경북대학교 산학협력단 METHOD AND DEVICE OF SEARCHING AROUND DEVICES BASED Internet of Things

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