JP2020149218A - Iot apparatus credibility calculation device, iot apparatus credibility calculation system, method for using iot apparatus credibility calculation device, iot apparatus credibility calculation method, program, iot data platform, and iot data processing method by iot data platform - Google Patents

Abstract

To provide an IoT apparatus credibility calculation device, an IoT apparatus credibility calculation system, a method for using the IoT apparatus credibility calculation device, and an IoT apparatus credibility calculation method and program capable of calculating the credibility of measurement data transmitted from an IoT apparatus with high accuracy.SOLUTION: An IoT apparatus credibility calculation device 1 includes: a first acquisition part 11 for acquiring measurement data transmitted with time from an IoT apparatus D having a sensor; a second acquisition part 12 for acquiring non-measurement information being information other than the measurement data transmitted from the IoT apparatus D; and a credibility calculation part 17 for calculating the credibility of the measurement data transmitted from the IoT apparatus D on the basis of measurement data acquired by a third acquisition part 13 and the first acquisition part 11, and non-measurement information acquired by the second acquisition part 12 and the third acquisition part 13.SELECTED DRAWING: Figure 1

Description

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 program, an IoT data platform, and a method of processing IoT data by the IoT data platform.

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, citing guidance as a threat to the main IoT devices, and explaining 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. ..
Meanwhile, Professor Tsutomu Matsumoto of Yokohama National University and others say that the relatively closed-form IoT architecture will change to an open architecture around 2030 (https://www.nedo.go. jp / content / 108067110.pdf).

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.

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.

Further, conventionally, management devices and environmental sensing systems that ensure the reliability of environmental information detected by sensor devices have been 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.

JP-A-2018-160834

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. , IoT device credit calculation method and program.
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. It is an IoT device credit rating calculation device including a credit rating calculation unit for calculating the credit rating of data.

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.

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.

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.

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.

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.

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 authorized owner and / or administrator of the IoT device. May be good.

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.

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.

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.

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.

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.

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 /. Alternatively, 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. This is an IoT device credit rating calculation method including a credit rating calculation step for calculating the credit rating of data.

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. This is a program for executing the credit rating calculation step for calculating the credit rating of the measured data.

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.

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.

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.

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 programs 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.

It is a figure which shows an example of the schematic structure of the IoT apparatus credit degree calculation apparatus 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. 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. 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. 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. 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. It is a figure which shows the 1st application example of the IoT device credit degree calculation apparatus of 1st Embodiment. It is a figure which shows the 2nd application example of the IoT device credit degree calculation apparatus of 1st Embodiment. 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. It is a figure which shows the 3rd application example of the IoT device credit degree calculation apparatus of 1st Embodiment. It is a figure which shows an example of the schematic structure of the IoT data platform of the 2nd Embodiment. It is a flowchart for demonstrating an example of the process executed in the IoT data platform of 2nd Embodiment. 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. It is a figure which shows an example of the present problem.

<First Embodiment>
Hereinafter, with reference to the attached drawings, the IoT device credit rating calculation device, the IoT device credit rating calculation system, the usage method of the IoT device credit rating device, the IoT device credit rating calculation method, and the embodiment of the program will be described.

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 the 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.

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.

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. At that time, the contents of calibration and / or maintenance of IoT device D, response of IoT device, whether or not IoT device D is equipped with a secure chip (not shown), operating status of IoT device, certification of IoT device D 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).

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 non-measurement information acquisition sections, 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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. In the device credit rating calculation device 1, the use of the data can be appropriately determined according to the credit rating of the measurement data of the sensor D1 calculated by the credit rating calculation unit 17.

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.

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.

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, regarding (1) above, it is conceivable that the degree of credibility 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.

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 judgment" are executed by the credit calculation unit 17 (see FIG. 1).

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 by 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. 1) has the contents of the declaration of the owner and / or the administrator 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 of IoT device D is properly executed. Perform an analysis of the owner and / or administrator of IoT device D.

The information of the IoT device D includes the manufacturer of the IoT device D, the manufacturing time, the presence / absence and timing of the calibration of the IoT device D, the response of the IoT device D, the presence / absence of the secure chip, the operating status, various authentication information, the installation location, and the installation. There are environment, difficulty level of data measured by sensor D1 of IoT device D, communication path, level of encryption, etc. These information are passed through the model of 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.

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.

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. In addition, 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.
Further, 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.

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 key points for these judgments are the presence or absence of traces of artificial processing and 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.

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 letting 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. 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.

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.

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.

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.

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.

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's 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.

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, it is possible to obtain the credit score of the data 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.

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.

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 ( The management system score) and the state of the installation environment of the 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.

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.

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 (memory 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.

In the example shown in FIG. 9, for 14 IoT devices (indicated by reference numerals 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.

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 toxic substances 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.

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.

<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.

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.

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).

In the current IoT device authentication, 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 regular, unannounced, or both for things that are difficult to judge from digital information alone, such as the installation environment and management system. 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.
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.

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.

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.

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.

<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.

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 on 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.

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. ..

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.

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.

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.

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.

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 to be used, 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).

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.

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. It may also include a program that holds a program for a certain period of time, such as a volatile memory inside a computer system that serves as a server or a client in that case. 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.

(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.

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.

In the IoT device credit rating 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.

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.

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.

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.

In the IoT data platform of one aspect of the present invention, the credit rating information receiving unit may receive information regarding the credit rating of the IoT data from the rating unit.

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)

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. 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. 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.
The IoT device attribute analysis unit that executes the 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. 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. 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. 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 any one of claims 3 to 5. 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 any one of claims 3 to 6. 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 any one of claims 3 to 7. The IoT device attribute analysis unit
Manufacturer of the IoT device, when the IoT device was manufactured, whether or not the IoT device was calibrated and / or maintained, when the IoT device was calibrated and / or maintained, 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, difficulty of measuring the measurement data by the sensor of the IoT device, communication path of the measurement data transmitted by the IoT device, and encryption level 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 any one of claims 3 to 8. 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
Analysis of the attributes 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. 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 any one of claims 3 to 10. 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 any one of claims 1 to 11. The IoT device credit rating calculation device according to any one of claims 1 to 12.
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. The method of using the IoT device credit rating calculation device according to any one of claims 1 to 13.
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. The method of using the IoT device credit rating calculation device according to any one of claims 1 to 13.
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. 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. IoT device credit rating calculation method including steps. 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 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. A program to execute steps and. 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 creditworthiness of the IoT data received by the credit rating information receiving unit.
IoT data platform. 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. 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.