Digital low-voltage distribution network rural area user transaction detection method and system, and medium

Abstract

The invention discloses a digitized low-voltage distribution network rural transformer area user transaction detection method and system and a medium, and the method comprises the steps: collecting the digitized low-voltage distribution network rural transformer area user information, and forming a transformer area user data set; comparing the transformer area user data set with the transformer area user original data set; and finding out transaction users according to a comparison result, verifying user information, forming a user transaction detection result, and updating the transformer area user original data set. The method has the advantages of being simple in detection method, easy to program and implement, free of manual intervention, time-saving, labor-saving and high in accuracy.

Description

Technical Field

[0001] The present invention relates to the field of power grid digitization, and in particular to a method, system and medium for detecting abnormal user movements in rural areas of a digital low-voltage distribution network. Background Art

[0002] The low-voltage distribution network is a crucial link between the power system and users. Its operational effectiveness is crucial to the quality of power supply services. The construction of a digital low-voltage distribution network enables real-time mapping between the physical and digital grids, significantly improving the quality and efficiency of service for low-voltage distribution network users and enhancing the management of low-voltage distribution networks by power grid companies. To ensure the effectiveness of the digital low-voltage distribution network and the accurate mapping between the physical and digital grids, effective detection and management of user movements in rural areas of the low-voltage distribution network are necessary.

[0003] Currently, power grid companies primarily conduct regular on-site verification of user changes through manual methods, based on rural substations. Staff must manually compare new substation user data sets with the original data sets, verifying user location information and user names one by one. Due to the large number of substations and users in rural areas, the manpower and material resources required for these verifications are substantial, resulting in long inspection cycles, low efficiency, and the potential for errors. Summary of the Invention

[0004] In response to the above-mentioned problems in the prior art, the present invention provides a method, system and medium for detecting abnormal user changes in rural areas of a digital low-voltage distribution network, which are simple to implement, easy to program, require no manual intervention, save time and effort, and have high accuracy.

[0005] In a first aspect, a method for detecting abnormal user movement in a rural area of a digital low-voltage distribution network is provided, comprising the following steps:

[0006] S1: Collect user information in rural areas of the digital low-voltage distribution network to form a user data set in the area;

[0007] S2: Compare the user data set of the substation area with the original user data set of the substation area;

[0008] S3: Find the changed users and verify their information based on the comparison results, generate the user change detection results and update the original user data set of the substation area.

[0009] According to the first aspect, in some possible implementations, the user information of the rural substation of the digital low-voltage distribution network includes user latitude and longitude information and user name.

[0010] According to the first aspect, in some possible implementations, the area user data set is a three-dimensional matrix data set, and the three dimensions of the matrix are longitude data, dimension data, and user name.

[0011] According to the first aspect, in some possible implementations, the matrix dimension and size of the user data set in the substation area are the same as those of the original user data set in the substation area, but the data collection order may be different.

[0012] According to the first aspect, in some possible implementations, the step of comparing the user data set in the station area with the original user data set in the station area includes:

[0013] S21: Arrange the user data set and the original user data set of the substation area in ascending order of longitude. If the longitude data are the same, arrange the user data of the same longitude in ascending order of latitude.

[0014] S22: Calculate the absolute difference between the longitude data and latitude data of the user with the corresponding sequence number in the sorted backend area user data set and the sorted backend area user original data set;

[0015] S23: Calculating a similarity coefficient between the sorted background area user data set and the sorted background area user original data set based on the absolute difference between the user's longitude data and latitude data;

[0016] S24: Determine whether the similarity coefficient is greater than a similarity threshold. If so, determine that the user has changed.

[0017] S25: Determine whether the absolute difference between the longitude data or latitude data of each user exceeds a deviation threshold. If so, determine that the user's location has changed, and record the user's location change number.

[0018] According to the first aspect, in some possible implementations, a difference-based exponential decay similarity scoring method is used to calculate the similarity coefficient between the sorting background area user dataset and the sorting background area user original dataset, and the calculation formula is as follows:

[0019]

[0020] Where A is the user dataset in the sorting backend area, B is the original user dataset in the sorting backend area, max(AB) is the maximum value of the elements in the longitude and latitude difference array between the user dataset in the sorting backend area and the original user dataset in the sorting backend area, and mean(B) is the average value of the elements in the longitude and latitude array of the original user dataset in the sorting backend area.

[0021] According to the first aspect, in some possible implementations, the similarity threshold is 0.99, and the deviation threshold is 0.0002°.

[0022] According to the first aspect, in some possible implementations, the step of finding the changed user based on the comparison result and verifying the user information includes:

[0023] S31: Find the name of the user whose location has changed according to the location change user serial number in the comparison result;

[0024] S32: Based on the names of the changed users in the sorted backstage area user data set, search in the sorted backstage area user original data set to verify whether the names of the users with changed locations are consistent. If they are consistent, it is determined that the electricity usage location of the same user has changed. Otherwise, search in sequence according to the sorting and determine whether the names of other users in the substation are consistent. If the names of the changed users are inconsistent with the names of other users, it is determined that the location of the new user has changed while the number of users remains unchanged, and a user change detection result is generated.

[0025] According to the first aspect, in some possible implementations, the user change detection results are a user change table and a user change graph. The user change table includes the user name, change direction, and change distance before and after the change. The user change graph is a graph of the changes in the user's geographic spatial location distribution before and after the change.

[0026] In the second aspect, a system for detecting abnormal user changes in rural areas of a digital low-voltage distribution network is provided, comprising a memory and a processor connected to each other, wherein a computer program or instruction is stored on the memory, and when the processor executes the computer program or instruction, the steps of the method for detecting abnormal user changes in rural areas of a digital low-voltage distribution network are implemented as described above.

[0027] In a third aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a computer device, the steps of the method for detecting abnormal changes in users in rural areas of a digital low-voltage distribution network are implemented as described above.

[0028] Compared with the existing technology, the present invention has the following beneficial effects: the present invention includes collecting user information of rural substations in a digital low-voltage distribution network to form a substation user data set; comparing the substation user data set with the substation user original data set; finding out abnormal users and verifying user information, forming user abnormality detection results and updating the original data set of users in rural substations in the digital low-voltage distribution network. The entire abnormality comparison and detection process does not require manual intervention and can be achieved by relying on digital and intelligent technology. It has the advantages of simple detection method, easy programming implementation, time and labor saving, and high detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 This is a flow chart of a method for detecting abnormal user movement in a rural area of a digital low-voltage distribution network provided by an embodiment of the present invention;

[0031] Figure 2 This is a flow chart of comparing a user data set in a substation area with an original user data set in the substation area provided by an embodiment of the present invention;

[0032] Figure 3 This is a rural area user anomaly diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, an embodiment of the present invention provides a method for detecting abnormal user movement in a rural area of a digital low-voltage distribution network, comprising the following steps:

[0035] S1: Collect user information of rural substations in the digital low-voltage distribution network to form a substation user data set.

[0036] In this embodiment, the rural substation user information of the digital low-voltage distribution network includes user latitude and longitude information and user name. The staff can collect user information through mobile terminals (such as smart phones) to form a substation user data set.

[0037] In this embodiment, the user data set of the station area is a three-dimensional matrix data set, and the three dimensions of the matrix are longitude data, dimension data, and user name. Table 1 shows an example of a user data set of the station area.

[0038]

[0039] S2: Compare the user dataset of the substation area with the original user dataset of the substation area.

[0040] In this embodiment, the matrix dimensions and sizes of the substation user data set and the substation user original data set are the same (in this embodiment, only the case where there are no new users or new users are added when the total number of users remains unchanged) are considered, but the data collection order can be different. Table 2 shows an example of a substation user original data set.

[0041]

[0042] The step of comparing the user data set of the substation area with the original user data set of the substation area includes:

[0043] S21: Arrange the user data set and the original user data set of the substation area in ascending order of longitude. If the longitude data are the same, arrange the user data of the same longitude in ascending order of latitude.

[0044] S22: Calculate the absolute difference between the longitude and latitude data of the user with the corresponding sequence number in the sorted background area user data set and the sorted background area user original data set; Table 3 shows an example of the absolute difference between the longitude and latitude data of the user;

[0045]

[0046] S23: Calculating a similarity coefficient between the sorted background area user data set and the sorted background area user original data set based on the absolute difference between the user's longitude data and latitude data;

[0047] In this embodiment, the similarity coefficient between the sorting background area user data set and the sorting background area user original data set is calculated using a difference-based exponential decay similarity scoring method. The calculation formula is as follows:

[0048]

[0049] Where A is the user dataset of the sorting backend area, B is the original user dataset of the sorting backend area, max(AB) is the maximum value of the elements in the longitude and latitude difference array AB between the user dataset of the sorting backend area and the original user dataset of the sorting backend area, and mean(B) is the average value of the elements in the longitude and latitude array of the original user dataset of the sorting backend area (the combined average value of longitude and latitude).

[0050] S24: Determine whether the similarity coefficient is greater than a similarity threshold. If so, determine that the user has changed.

[0051] In this embodiment, max(AB) is 0.03000, mean(B) is 78.15174, and the calculated similarity coefficient is 0.9974. In this embodiment, the similarity threshold is 0.99. Therefore, the calculated similarity coefficient exceeds the similarity threshold, and it is determined that the user has changed.

[0052] S25: Determine whether the absolute difference between the longitude data or latitude data of each user exceeds a deviation threshold. If so, determine that the user's location has changed, and record the user's serial number with the changed location.

[0053] Table 3 shows that users 10 and 4 have moved. The longitude difference for user 3 is 0.03000° and the latitude difference is 0.20000°, while the longitude difference for user 4 is 0.10000° and the latitude difference is 0.10000°. In this embodiment, the deviation threshold is 0.0002°, corresponding to a distance of approximately 20 meters. Therefore, the change is determined to be a user location change, and the user numbers for the change are recorded as 3 and 4.

[0054] S3: Find the changed users and verify their information based on the comparison results, generate the user change detection results and update the original user data set of the substation area.

[0055] The steps of finding the changed user and verifying the user information according to the comparison results include:

[0056] S31: Find the name of the user whose location has changed according to the location change user serial number in the comparison result;

[0057] S32: Based on the names of the changed users in the sorted backstage area user data set, search in the sorted backstage area user original data set to verify whether the names of the users with changed locations are consistent. If they are consistent, it is determined that the electricity usage location of the same user has changed. Otherwise, search in sequence according to the sorting and determine whether the names of other users in the substation are consistent. If the names of the changed users are inconsistent with the names of other users, it is determined that the location of the new user has changed while the number of users remains unchanged, and a user change detection result is generated.

[0058] In this embodiment, the changed user numbers are 3 and 4, corresponding to users 10 and 4. According to the comparison results of Table 1 and Table 2, the user names of users 10 and 4 are the same, so it is determined that users 10 and 4 have changed their electricity usage locations.

[0059] In order to intuitively present the changes, in this embodiment, the user change detection results are a user change table and a user change graph. The user change table contains data such as the user name, change direction, and change distance before and after the change. The user change graph is a graph of the changes in the user's geographic spatial location distribution before and after the change.

[0060] In this embodiment, the user change table is shown in Table 4. The change direction is 0° with due north as the starting point, and the angle increase direction is clockwise. Figure 3 As shown in the figure, "X" represents the user's position in the original user data set, and "o" represents the user's position in the user data set after the change, which can intuitively show the user's position before and after the change and the direction of the change.

[0061]

[0062] To sum up, the above embodiment provides a method for detecting abnormal changes in users in rural areas of a digital low-voltage distribution network, including collecting user information in rural areas of a digital low-voltage distribution network to form a user data set of the area; comparing the user data set of the area with the original data set of the area users; finding abnormal users and verifying user information to form user abnormality detection results and updating the original data set of users in rural areas of the digital low-voltage distribution network; the entire abnormality comparison and detection process does not require manual intervention and can be achieved by relying on digital and intelligent technology, and has the advantages of simple detection method, easy programming implementation, time-saving and labor-saving, and high detection accuracy.

[0063] In addition, an embodiment of the present invention also provides a digital low-voltage distribution network rural area user change detection system, including an interconnected memory and a processor, the memory storing a computer program or instructions, and the processor implementing the steps of a digital low-voltage distribution network rural area user change detection method as described above when executing the computer program or instructions.

[0064] In addition, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program or instruction is stored. When the computer program or instruction is executed by a computer device, the steps of the method for detecting abnormal changes in users in rural areas of a digital low-voltage distribution network are implemented as described above.

[0065] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0066] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0070] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network, characterized in that: The steps include: S1: Collect user information in rural areas of the digital low-voltage distribution network to form a user data set in the area; S2: Compare the user data set of the substation area with the original user data set of the substation area; S3: Find the changed users and verify their information based on the comparison results, generate the user change detection results and update the original user data set of the substation area.

2. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 1 is characterized in that: The rural area user information of the digital low-voltage distribution network includes user latitude and longitude information and user name.

3. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 2, characterized in that: The area user data set is a three-dimensional matrix data set, and the three dimensions of the matrix are longitude data, dimension data, and user name.

4. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 2, characterized in that: The step of comparing the user data set of the substation area with the original user data set of the substation area includes: S21: Arrange the user data set and the original user data set of the substation area in ascending order of longitude. If the longitude data are the same, arrange the user data of the same longitude in ascending order of latitude. S22: Calculate the absolute difference between the longitude data and latitude data of the user with the corresponding sequence number in the sorted backend area user data set and the sorted backend area user original data set; S23: Calculating a similarity coefficient between the sorted background area user data set and the sorted background area user original data set based on the absolute difference between the user's longitude data and latitude data; S24: Determine whether the similarity coefficient is greater than a similarity threshold. If so, determine that the user has changed. S25: Determine whether the absolute difference between the longitude data or latitude data of each user exceeds a deviation threshold. If so, determine that the user's location has changed, and record the user's location change number.

5. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 4 is characterized in that: The similarity coefficient between the user dataset in the sorting background area and the original user dataset in the sorting background area is calculated using the difference-based exponential decay similarity scoring method. The calculation formula is as follows: ; Where A is the user dataset in the sorting backend area, B is the original user dataset in the sorting backend area, max(AB) is the maximum value of the elements in the longitude and latitude difference array between the user dataset in the sorting backend area and the original user dataset in the sorting backend area, and mean(B) is the average value of the elements in the longitude and latitude array of the original user dataset in the sorting backend area.

6. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 4, characterized in that: The similarity threshold is 0.99, and the deviation threshold is 0.0002°.

7. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 1, characterized in that: The steps of finding the changed user and verifying the user information according to the comparison result include: S31: Find the name of the user whose location has changed according to the location change user serial number in the comparison result; S32: Based on the names of the changed users in the sorted backstage area user data set, search in the sorted backstage area user original data set to verify whether the names of the users with changed locations are consistent. If they are consistent, it is determined that the electricity usage location of the same user has changed. Otherwise, search in sequence according to the sorting and determine whether the names of other users in the substation are consistent. If the names of the changed users are inconsistent with the names of other users, it is determined that the location of the new user has changed while the number of users remains unchanged, and a user change detection result is generated.

8. The method for detecting abnormal user movement in rural areas of a digital low-voltage distribution network according to claim 1, characterized in that: The user change detection results are a user change table and a user change graph. The user change table includes the user name, change direction, and change distance before and after the change. The user change graph is a graph showing the changes in the user's geographic spatial location distribution before and after the change.

9. A digital low-voltage distribution network rural area user abnormality detection system, comprising a memory and a processor connected to each other, wherein the memory stores a computer program or instruction, characterized in that: When the processor executes the computer program or instruction, the processor implements the steps of a method for detecting abnormal user changes in a rural area of a digital low-voltage distribution network as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a computer device, the steps of a method for detecting abnormal user changes in a rural area of a digital low-voltage distribution network as described in any one of claims 1 to 8 are implemented.

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