Location-Based IoT Data Acquisition Device Registration Location Verification Method and System

Abstract

This invention discloses a location-based IoT data acquisition device registration location verification method, including acquiring data information and registration information of the device to be registered; clustering the devices to be registered; the device to be registered initiating a registration request; verifying the registration information and registration location of the device to be registered; and completing the registration location verification of the IoT data acquisition device based on the verification result. This invention also discloses a system for implementing the location-based IoT data acquisition device registration location verification method. This invention establishes a link between device location and device authentication information. The device locates its current position using positioning technology, and verifies whether the device is installed in a predetermined location using real-time device location information. Therefore, this invention can safely and efficiently complete the connection between the device and the power IoT, effectively preventing the device from being incorrectly installed in an unprepared location, improving the efficiency of device deployment, and exhibiting high reliability, practicality, stability, and efficiency.

Description

Technical Field

[0001] This invention belongs to the field of power Internet of Things (IoT) technology, specifically relating to a location-based IoT data acquisition device registration location verification method and system. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] The Internet of Things (IoT) for the power system is an important component. With its rapid development, an increasing number of devices are being connected to the IoT, exhibiting a complex variety and continuous growth trend. Therefore, to address this, the power grid is establishing an IoT management platform to manage devices, collect and process data from these devices, and connect with upper-level applications.

[0004] Current power IoT devices require registration and installation near designated locations before connecting to the power grid. However, existing technologies cannot quickly verify the registration location of IoT devices, posing a significant risk to their safe operation. Summary of the Invention

[0005] One of the objectives of this invention is to provide a location verification method for IoT data acquisition devices that is highly reliable, practical, stable, and efficient.

[0006] The second objective of this invention is to provide a system for implementing the location verification method for IoT data acquisition devices based on positioning.

[0007] The location-based IoT data acquisition device registration location verification method provided by this invention includes the following steps:

[0008] S1. Obtain the data and registration information of the device to be registered;

[0009] S2. Based on the information obtained in step S1, cluster the devices to be registered;

[0010] S3. Once the device to be registered is installed, it initiates a registration request;

[0011] S4. Based on the registration request initiated in step S3, verify the registration information of the device to be registered, and verify the registration location based on the classification algorithm;

[0012] S5. Based on the verification results obtained in step S4, complete the registration location verification of the IoT acquisition device.

[0013] Step S1, which involves obtaining the data information and registration information of the device to be registered, specifically includes the following steps:

[0014] D = {D1,D2,...,D} N} represents N new devices that need to be connected to the power Internet of Things; C n For device D n Registration information required when connecting to the power Internet of Things; A n For device D n Authentication information; P n For device D n Location information of the scheduled installation location; C n A n and P n Each is unique and there is a one-to-one correspondence between them;

[0015] Collect and organize the registration, authentication, and location information of N new devices that need to be connected to the power Internet of Things, and establish a dictionary-based index table to store the data information.

[0016] The registration information includes username, password, and client ID; the authentication information includes device identifier, product manufacturer ID, device model, application ID, application key, and security certificate.

[0017] Step S2, which involves clustering the devices to be registered based on the information obtained in step S1, specifically includes the following steps:

[0018] Set P = {P1, P2, ..., P} N} represents the set of N predetermined installation locations for new equipment; each predetermined installation location consists of several location data points, thus constraining the installation area; P n,m P represents n The m-th location data point, and P n,m It is an I-dimensional vector; each new device that needs to be connected to the power Internet of Things has a permissible safe installation range, which is defined as... With r as the center, n A circle with radius , For P n The center of mass of P; n center of mass in Center of mass The i-th dimension of data, and For P n The data value of the i-th dimension of the m-th location data point, |P n |for Pn The number of location data points.

[0019] Step S2 further includes the following steps:

[0020] To improve efficiency and computation speed, the devices are clustered: for device D n and equipment D m If the center of mass and If the distance between them is less than the set value, then device D is considered to be... n and equipment D m They belong to the same cluster;

[0021] The N new devices that need to be connected to the power Internet of Things are clustered into a total of K clusters, denoted as G = {G1, G2, ..., G...} K}

[0022] Step S3, which describes the process where the device to be registered initiates a registration request after installation, specifically involves the device sending its current location L and its authentication information to the system. The registration request is initiated by sending the message via the HTTPS protocol.

[0023] Step S4, which involves verifying the registration information of the device to be registered based on the registration request initiated in step S3 and verifying the registration location based on a classification algorithm, specifically includes the following steps:

[0024] A. Regarding the obtained authentication information Indexing:

[0025] If the indexing is successful, the corresponding registration information C1 is obtained, and subsequent steps are performed.

[0026] If the index fails, the verification will be terminated and an alarm will be triggered.

[0027] B. Based on careful information Index to the corresponding device D i Then, device D is matched from the clustering result set G. i Cluster G to which it belongs k ;gather For cluster G k The set of location data points of all devices in the system. For set The m-th element; d m To locate position L and position data points The Euclidean distance between them; Location data points The corresponding tags but For equipment A data location point in the predetermined installation location; the number of data location points in the predetermined installation location of each device is not less than K;

[0028] C. For sets Each data point in Calculate the distance value d m Then the distance value d m The following formula is used for mapping, thereby transforming the distance value d m Mapped to similarity s m :

[0029]

[0030] In the formula, λ1 and λ2 are set constants; θ is the value of the inflection point; if the distance value d m The value range of is [0, +∞). We need to map [0, θ) to [1, η) and [θ, +∞) to [η, 0], where η is a set value and η∈(0, 1].

[0031] D. Select the K location data points with the highest similarity to the location L; using the KNN algorithm, if the label of the location data points that are more than a certain proportion among the selected K location data points is c, then the classification result of the current data point is c;

[0032] If K = 1, then the calculated value of c is:

[0033] E. Based on the classification result c obtained in step D, obtain the registration information C2, and judge it according to the following rules:

[0034] If the scheduled installation location P of device c c center of mass The distance from the positioning position L is greater than the radius value r c If C1≠C2, it is determined that the installation location of the equipment is incorrect or the authentication information is incorrect, and an alarm will be triggered;

[0035] If the scheduled installation location P of device c c center of mass The distance from the positioning position L is not greater than the radius value r. c If C1 = C2, then the installation location of the equipment is considered correct, the location verification is correct, and the equipment registration is successful.

[0036] This invention also discloses a system for implementing the location-based IoT data acquisition device registration location verification method, specifically including a verification data acquisition module, a registration device clustering module, a registration request module, a registration location verification module, and an output module; the verification data acquisition module, registration device clustering module, registration request module, registration location verification module, and output module are connected in series; the verification data acquisition module is used to acquire the data information and registration information of the device to be registered, and upload the data to the registration device clustering module; the registration device clustering module is used to cluster the devices to be registered according to the received data, and upload the data to the registration request module; the registration request module is used to initiate a registration request after the device to be registered is installed, and upload the data to the registration location verification module; the registration location verification module is used to verify the registration information of the device to be registered according to the received data, and verify the registration location based on a classification algorithm, and upload the data to the output module; the output module is used to complete the registration location verification of the IoT data acquisition device according to the received data.

[0037] The location-based IoT data acquisition device registration location verification method and system provided by this invention establishes a link between device location and device authentication information. The device locates its current position using positioning technology, and verifies whether the device is installed in the predetermined location using real-time device location information. Therefore, this invention can more safely and efficiently complete the connection between the device and the power IoT, effectively avoid the device being installed in the wrong location, further improve the efficiency of device deployment, and is highly reliable, practical, stable and efficient. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0039] Figure 2 This is a schematic diagram of the system functional modules of the present invention. Detailed Implementation

[0040] like Figure 1 The diagram shown is a schematic of the method flow of the present invention: The location-based IoT data acquisition device registration location verification method provided by the present invention includes the following steps:

[0041] S1. Obtain the data and registration information of the device to be registered; specifically including the following steps:

[0042] D = {D1,D2,...,D} N} represents N new devices that need to be connected to the power Internet of Things; C n For device D n Registration information required when connecting to the power Internet of Things; A n For device D n Authentication information; Pn For device D n Location information of the scheduled installation location; C n A n and P n Each is unique and there is a one-to-one correspondence between them;

[0043] Collect and organize the registration information, authentication information and location information of N new devices that need to be connected to the power Internet of Things, and establish a dictionary-based index table to store the data information; through the index table, the corresponding registration information can be indexed in O(1) time complexity based on the authentication information or the location information of the pre-installed location; at the same time, each device that needs to be connected to the power Internet of Things can connect to the registration server based on the HTTPS protocol, and each device is equipped with a positioning chip (preferably a Beidou positioning chip).

[0044] In practice, the registration information includes username, password, and client ID; the authentication information includes device identifier, product manufacturer ID, device model, application ID, application key, and security certificate.

[0045] S2. Based on the information obtained in step S1, cluster the devices to be registered; specifically, this includes the following steps:

[0046] Set P = {P1, P2, ..., P} N} represents the set of N predetermined installation locations for new equipment; each predetermined installation location consists of several location data points, thus constraining the installation area; P n,m P represents n The m-th location data point, and P n,m It is an I-dimensional vector; each new device that needs to be connected to the power Internet of Things has a permissible safe installation range, which is defined as... With r as the center, n A circle with radius , For P n The center of mass of P; n center of mass in Center of mass The i-th dimension of data, and For P n The data value of the i-th dimension of the m-th location data point, |P n |for P n The number of location data points;

[0047] At the same time, in order to improve efficiency and computing speed, the devices are clustered: for device D n and equipment D mIf the center of mass and If the distance between them is less than the set value, then device D is considered to be... n and equipment D m They belong to the same cluster; the specific algorithm process is as follows:

[0048] a. Let n = 1, H = {2, 3, ..., N}; proceed to step b;

[0049] b. Create a new cluster, with device D as the cluster head. n For any m∈H, satisfying and If the Euclidean distance is less than the set value, then D will be... m Join D n Then remove m from H; proceed to step c;

[0050] c. If H is an empty set, then the clustering ends; otherwise, let n be the first element in set H and go to step b to repeat.

[0051] The N new devices that need to be connected to the power Internet of Things are clustered into a total of K clusters, denoted as G = {G1, G2, ..., G...} K After clustering, only the distance to nodes within the cluster needs to be calculated, instead of the distance to all nodes, which reduces the complexity of the algorithm.

[0052] S3. After the device to be registered is installed, it initiates a registration request; specifically, after installation, the device to be registered will send its current location L and its own authentication information. The registration request is initiated by sending the request via the HTTPS protocol.

[0053] S4. Based on the registration request initiated in step S3, verify the registration information of the device to be registered, and verify the registration location based on a classification algorithm; specifically including the following steps:

[0054] A. Regarding the obtained authentication information Indexing:

[0055] If the indexing is successful, the corresponding registration information C1 is obtained, and subsequent steps are performed.

[0056] If the index fails, the verification will be terminated and an alarm will be triggered.

[0057] The method of this invention improves the measurement of distance by mapping the distance to similarity through a piecewise function. Then, based on the idea of ​​the K-nearest neighbor algorithm, it classifies the class to which the location L belongs, which is used to determine whether the device is installed in the predetermined location.

[0058] B. Based on careful information Index to the corresponding device D i Then, device D is matched from the clustering result set G. i Cluster G to which it belongs k ;gather For cluster G k The set of location data points of all devices in the system. For set The m-th element; d m To locate position L and position data points The Euclidean distance between them; Location data points The corresponding tags but For equipment A data location point in the predetermined installation location; the number of data location points in the predetermined installation location of each device is not less than K;

[0059] C. For sets Each data point in Calculate the distance value d m Then the distance value d m The following formula is used for mapping, thereby transforming the distance value d m Mapped to similarity s m :

[0060]

[0061] In the formula, λ1 and λ2 are set constants; θ is the value of the inflection point; if the distance value d m The value range of is [0, +∞). We need to map [0, θ) to [1, η) and [θ, +∞) to [η, 0], where η is a set value and η∈(0, 1].

[0062] The purpose of step C is to amplify the differences in short distances and weaken the differences in long distances. For example, the distances of point A from point B and point C are 0.1 and 0.101 respectively, but the distances of point A from point D and point E are 100 and 1000 respectively. Obviously, from a distance perspective, there is no difference between distances of 100 and 1000 for point A. The key is to distinguish short distances and avoid misclassification. Therefore, the piecewise function in step C is used for mapping. The piecewise function satisfies the property that the closer the distance, the larger the function output value, thus preserving the characteristic that the closer the distance, the greater the similarity. At the same time, it amplifies the differences in short distances and weakens the differences in long distances.

[0063] D. Select the K location data points with the highest similarity to the location L; using the KNN algorithm, if the label of the location data points that are more than a certain proportion among the selected K location data points is c, then the classification result of the current data point is c;

[0064] If K = 1, then the calculated value of c is:

[0065] E. Based on the classification result c obtained in step D, obtain the registration information C2, and judge it according to the following rules:

[0066] If the scheduled installation location P of device c c center of mass The distance from the positioning position L is greater than the radius value r c If C1≠C2, it is determined that the installation location of the equipment is incorrect or the authentication information is incorrect, and an alarm will be triggered;

[0067] If the scheduled installation location P of device c c center of mass The distance from the positioning position L is not greater than the radius value r. c If C1 = C2, then the installation location of the equipment is considered correct, the location verification is correct, and the equipment registration is successful.

[0068] S5. Based on the verification results obtained in step S4, complete the registration location verification of the IoT acquisition device.

[0069] like Figure 2 The diagram shows the system functional modules of the present invention: The system for implementing the location-based IoT data acquisition device registration location verification method disclosed in this invention specifically includes a verification data acquisition module, a registration device clustering module, a registration request module, a registration location verification module, and an output module; the verification data acquisition module, registration device clustering module, registration request module, registration location verification module, and output module are connected in series; the verification data acquisition module is used to acquire the data information and registration information of the device to be registered, and upload the data to the registration device clustering module; the registration device clustering module is used to cluster the devices to be registered according to the received data, and upload the data to the registration request module; the registration request module is used to initiate a registration request after the device to be registered is installed, and upload the data to the registration location verification module; the registration location verification module is used to verify the registration information of the device to be registered according to the received data, and verify the registration location based on a classification algorithm, and upload the data to the output module; the output module is used to complete the registration location verification of the IoT data acquisition device according to the received data.

Claims

1. A location-based IoT data acquisition device registration location verification method, comprising the following steps: S1. Obtain the data and registration information of the device to be registered; S2. Based on the information obtained in step S1, cluster the devices to be registered; S3. Once the device to be registered is installed, it initiates a registration request; S4. Based on the registration request initiated in step S3, verify the registration information of the device to be registered, and verify the registration location based on the classification algorithm; specifically including the following steps: A. Regarding the obtained authentication information Indexing: If the indexing is successful, retrieve the corresponding registration information. And proceed with the next steps; If the index fails, the verification will be terminated and an alarm will be triggered. B. Based on authentication information Index to the corresponding device Then from the clustering result set Matched devices To which the cluster belongs ;gather For clusters The set of location data points of all devices in the system. , For set The m-th element; To locate the position and location data points The Euclidean distance between them; For location data points The corresponding tags ,but For equipment A data location point in the predetermined installation location; the number of data location points in the predetermined installation location of each device is not less than KK; C. For sets Each data point in Distance values ​​are calculated for all cases. Then the distance value The following formula is used for mapping, thereby transforming the distance value Mapping to similarity : In the formula and A constant that is set; The value at the turning point; if the distance value The range of values ​​is It is necessary to Mapped to And will Mapped to , For set value and ,but , ; D. Select and locate the position. The K most similar location data points; using the KNN algorithm, if the label of the location data points that are more than a certain proportion among the selected K location data points is c, then the classification result of the current data point is c; like Then the calculated value of c is ; E. Based on the classification result c obtained in step D, obtain the registration information C2, and judge it according to the following rules: If the scheduled installation location of device c center of mass with location The distance is greater than the radius value ,or If the error occurs, it will be determined that the equipment is installed in the wrong location or the authentication information is incorrect, and an alarm will be triggered. If the scheduled installation location of device c center of mass with location The distance is not greater than the radius value ,and If the installation location is correct, the location verification is correct, and the equipment registration is successful; S5. Based on the verification results obtained in step S4, complete the registration location verification of the IoT acquisition device.

2. The location verification method for IoT data acquisition devices based on positioning according to claim 1, characterized in that... Step S1, which involves obtaining the data information and registration information of the device to be registered, specifically includes the following steps: For those currently needing to connect to the power Internet of Things A new piece of equipment; For equipment Registration information required when connecting to the power Internet of Things; For equipment Authentication information; For equipment Location information of the scheduled installation location; , and Each is unique and there is a one-to-one correspondence between them; Collect and organize information that currently needs to be connected to the power Internet of Things. The system collects registration, authentication, and location information for each new device and establishes a dictionary-based index table to store the data.

3. The location verification method for IoT data acquisition devices based on positioning according to claim 2, characterized in that... The registration information includes username, password, and client ID; the authentication information includes device identifier, product manufacturer ID, device model, application ID, application key, and security certificate.

4. The location verification method for IoT data acquisition devices based on positioning according to claim 2, characterized in that... Step S2, which involves clustering the devices to be registered based on the information obtained in step S1, specifically includes the following steps: gather for A set of pre-booked installation locations for new equipment; each pre-booked installation location consists of several location data points, thereby constraining the installation area; express The m-th location data point, and It is an I-dimensional vector; each new device that needs to be connected to the power Internet of Things has a permissible safe installation range, which is defined as... With the center, and A circle with radius , for The center of mass; for center of mass , ,in Center of mass The i-th dimension of data, and , for The data value of the i-th dimension of the m-th location data point. for The number of location data points.

5. The location verification method for IoT data acquisition devices based on positioning according to claim 4, characterized in that... Step S2 further includes the following steps: To improve efficiency and computing speed, devices are clustered: For devices and equipment If the center of mass and If the distance between them is less than the set value, then the device is considered to be faulty. and equipment They belong to the same cluster; For those currently needing to connect to the power Internet of Things The new devices are clustered, resulting in a total of K clusters, denoted as... .

6. The location verification method for IoT data acquisition devices based on positioning according to claim 5, characterized in that... Step S3, which describes the process where the device to be registered initiates a registration request after installation, specifically involves the device to be registered setting its current location. and its own authentication information The registration request is initiated by sending the request via the HTTPS protocol.

7. A system for implementing the location-based IoT data acquisition device registration and location verification method according to any one of claims 1 to 6, characterized in that... Specifically, it includes a verification data acquisition module, a registration device clustering module, a registration request module, a registration location verification module, and an output module; the verification data acquisition module, the registration device clustering module, the registration request module, the registration location verification module, and the output module are connected in series; the verification data acquisition module is used to acquire the data information and registration information of the device to be registered, and upload the data to the registration device clustering module; The device clustering module is used to cluster the devices to be registered based on the received data and upload the data to the registration request module; The registration request module is used to initiate a registration request after the device to be registered is installed, and to upload the data to the registration location verification module. The registration location verification module is used to verify the registration information of the device to be registered based on the received data, verify the registration location based on the classification algorithm, and upload the data to the output module. The output module is used to verify the registration location of the IoT data acquisition device based on the received data.

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