Electric energy meter missort problem monitoring method, device and equipment and storage medium

Abstract

The invention discloses an electric energy meter missort problem monitoring method, device and equipment and a storage medium, and relates to the technical field of electric power metering, and the method comprises the steps: determining a to-be-analyzed target electric energy meter of a target user, and obtaining the historical electric energy meter operation data and predicted power utilization load characteristics of the target user; determining historical electrical load characteristics of the target user based on the historical electric energy meter operation data and the predicted electrical load characteristics, and determining a target to-be-analyzed transformer area based on the transformer area of the target electric energy meter and a preset range condition; determining a to-be-verified electric energy meter of a to-be-verified user in the target to-be-analyzed transformer area, and determining a target electrical load feature of the to-be-verified user based on the operation data of the to-be-verified electric energy meter; and performing similarity comparison on the historical electrical load characteristics and the target electrical load characteristics to obtain a similarity value, and monitoring whether the target electric energy meter and the to-be-verified electric energy meter have a missort problem or not based on the similarity value. According to the invention, efficient and accurate remote monitoring and positioning of the electric energy meter missort problem can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power metering, in particular to an electric energy meter household connection problem monitoring method, device, equipment and storage medium. BACKGROUND

[0002] As the core basis for electric energy transaction and electricity settlement between power supply enterprises and electricity customers, the metering accuracy of electric energy meters is directly related to the vital interests of both parties. Electric energy meter household connection is a common error in installation and replacement, which can be divided into two categories: file error and wiring error. Both will lead to inconsistent measurement results and actual electricity consumption, causing electricity overcharge, undercharge or overcharge problems, and even legal disputes and electricity safety hazards. It is a key problem that both power grid enterprises and customers are concerned about.

[0003] The current troubleshooting method for electric energy meter household connection has obvious limitations: the control load method requires on-site operation by staff, consumes manpower and resources, and affects normal electricity use of customers; the household connection troubleshooting instrument does not need to disconnect the load switch, but still relies on on-site operation, and is difficult to find the bus due to hidden line laying, and only compares electrical parameters at a certain time, so it cannot effectively identify if the household connection user has similar electricity consumption status; the method based on electric quantity comparison is only suitable for electric energy meter replacement scenarios, and only relies on single time period electric quantity data, so its accuracy is limited in the current diversified customer electricity load. The current electric energy meter household connection problem relies on passive disposal after being discovered by customers, and lacks effective active identification means.

[0004] In summary, how to realize efficient, accurate remote monitoring and precise positioning of electric energy meter household connection problems is a technical problem to be solved at present. SUMMARY

[0005] Therefore, the purpose of the present application is to provide an electric energy meter household connection problem monitoring method, device, equipment and storage medium, which can realize efficient, accurate remote monitoring and precise positioning of electric energy meter household connection problems. The specific scheme is as follows:

[0006] In a first aspect, the present application provides an electric energy meter household connection problem monitoring method, comprising:

[0007] determining a target electric energy meter to be analyzed for a target user, and obtaining historical electric energy meter operation data and predicted electricity load characteristics corresponding to the target user;

[0008] determining historical electricity load characteristics corresponding to the target user based on the historical electric energy meter operation data and the predicted electricity load characteristics, and determining a target analysis area based on a preset range condition and a distribution area corresponding to the target electric energy meter;

[0009] Identify the energy meters to be verified for the users to be verified in the target analysis area, and determine the target electricity load characteristics for the users to be verified based on the operating data of the energy meters to be verified.

[0010] The historical electricity load characteristics are compared with the target electricity load characteristics to obtain a target similarity value. Based on the target similarity value, it is used to monitor whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified.

[0011] Optionally, determining the historical electricity load characteristics corresponding to the target user based on the historical electricity meter operating data and the predicted electricity load characteristics includes:

[0012] Determine the commissioning time of the target electricity meter and determine whether the commissioning time is greater than a preset time threshold;

[0013] If the commissioning time is greater than the preset time threshold, the historical first electricity load characteristics of the target user are analyzed based on the preset data collection interval and the historical electricity meter operation data, and the historical second electricity load characteristics of the target user are analyzed based on the freeze interval corresponding to the frozen data of the target electricity meter and the historical electricity meter operation data.

[0014] Wherein, the preset data acquisition interval is much smaller than the freezing interval corresponding to the frozen data of the target electricity meter, and the time dimension of the historical first electricity load feature is smaller than the time dimension of the historical second electricity load feature.

[0015] Optionally, after determining whether the commissioning time is greater than a preset time threshold, the method further includes:

[0016] If the commissioning time is not greater than the preset time threshold, the historical first electricity load characteristics of the target user are analyzed based on the preset data collection interval and the historical electricity meter operation data.

[0017] Based on the predicted electricity load characteristics, target reference users are matched, and based on the electricity load characteristics corresponding to the target reference users, the historical second electricity load characteristics corresponding to the target users are determined.

[0018] Optionally, determining the energy meter to be verified corresponding to the user to be verified in the target analysis area includes:

[0019] Determine the installation time corresponding to the target electricity meter, and determine the target time period based on the preset time interval condition and the installation time;

[0020] Based on the target time period, determine the energy meter to be verified corresponding to the user to be verified in the target analysis area;

[0021] A corresponding list of electricity meters to be verified is generated based on the electricity meter number corresponding to the electricity meter to be verified, so as to locate the electricity meter to be verified that has a cross-connection problem with the target electricity meter based on the electricity meter number in the list of electricity meters to be verified.

[0022] Optionally, determining the target electricity load characteristics corresponding to the user to be verified based on the operating data of the electricity meter to be verified includes:

[0023] Based on the operating data of the electricity meter to be verified, the target first electricity load characteristics and target second electricity load characteristics corresponding to the user to be verified are determined.

[0024] Accordingly, the step of comparing the historical electricity load characteristics with the target electricity load characteristics to obtain a target similarity value includes:

[0025] A similarity comparison is performed based on the historical first electricity load characteristics, the historical second electricity load characteristics, the target first electricity load characteristics, and the target second electricity load characteristics to obtain the target similarity value;

[0026] Wherein, the time dimension of the target first electricity load characteristic is equal to the time dimension of the historical first electricity load characteristic, and the time dimension of the target second electricity load characteristic is equal to the time dimension of the historical second electricity load characteristic.

[0027] Optionally, the step of performing a similarity comparison based on the historical first electricity load characteristics, the historical second electricity load characteristics, the target first electricity load characteristics, and the target second electricity load characteristics to obtain the target similarity value includes:

[0028] Determine the first time series data corresponding to the historical first electricity load characteristic, the second time series data corresponding to the historical second electricity load characteristic, the third time series data corresponding to the target first electricity load characteristic, and the fourth time series data corresponding to the target second electricity load characteristic;

[0029] Determine a first gap between the first time series data and the third time series data, and determine a second gap between the second time series data and the fourth time series data;

[0030] Determine the first difference time series data between the first time series data and the third time series data, and determine the second difference time series data between the second time series data and the fourth time series data;

[0031] Based on the first gap and the first difference time series data, a consistency evaluation is performed on the first time series data and the third time series data to obtain a first evaluation result; and based on the second gap and the second difference time series data, a consistency evaluation is performed on the second time series data and the fourth time series data to obtain a second evaluation result.

[0032] The target similarity value is determined based on the first evaluation result and the second evaluation result.

[0033] Optionally, the step of monitoring whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value includes:

[0034] If the target similarity value is greater than the preset similarity threshold, the meter number corresponding to the meter to be verified is determined, and corresponding cross-connection risk warning information is generated based on the meter number.

[0035] Based on a preset notification method, the cross-connection risk warning information is sent to the target management personnel so that the target electricity meter and the electricity meter to be verified can be investigated.

[0036] Secondly, this application provides a device for monitoring cross-connection issues in electricity meters, comprising:

[0037] The target electricity meter determination module is used to determine the target electricity meter of the target user to be analyzed, and to obtain the historical electricity meter operation data and predicted electricity load characteristics of the target user.

[0038] The module for determining the area to be analyzed is used to determine the historical electricity load characteristics corresponding to the target user based on the historical electricity meter operation data and the predicted electricity load characteristics, and to determine the target area to be analyzed based on the area corresponding to the target electricity meter and preset range conditions.

[0039] The module for determining the energy meter to be verified is used to determine the energy meter to be verified corresponding to the user in the target analysis area, and to determine the target electricity load characteristics corresponding to the user to be verified based on the operating data of the energy meter to be verified.

[0040] The cross-connection problem monitoring module is used to compare the historical electricity load characteristics with the target electricity load characteristics to obtain a target similarity value, and to monitor whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value.

[0041] Thirdly, this application provides an electronic device, comprising:

[0042] Memory, used to store computer programs;

[0043] A processor is used to execute the computer program to implement the aforementioned method for monitoring cross-connection issues in electricity meters.

[0044] Fourthly, this application provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for monitoring cross-connection issues in electricity meters.

[0045] In this application, the target electricity meter of the target user to be analyzed is first identified, and the historical electricity meter operation data and predicted electricity load characteristics of the target user are obtained. Then, based on the historical electricity meter operation data and predicted electricity load characteristics, the historical electricity load characteristics of the target user are determined, and the target substation to be analyzed is determined based on the substation corresponding to the target electricity meter and preset range conditions. Subsequently, the electricity meter to be verified corresponding to the user to be verified in the target substation to be analyzed is determined, and the target electricity load characteristics of the user to be verified are determined based on the operation data of the electricity meter to be verified. Finally, the historical electricity load characteristics and the target electricity load characteristics are compared for similarity to obtain the target similarity value, and the target electricity meter and the electricity meter to be verified are monitored for cross-user problems based on the target similarity value. As can be seen from the above, this application first identifies the target electricity meters of the target users to be analyzed and collects the historical electricity meter operation data and predicted electricity load characteristics of the target users; then, combining the historical electricity meter operation data and predicted electricity load characteristics, the historical electricity load characteristics of the target users are determined, and the target analysis area is determined based on the transformer substation to which the target electricity meter belongs and the preset range; next, the electricity meters of the users to be verified within the target analysis area are identified, and the corresponding target electricity load characteristics are determined based on the operation data of the electricity meters to be verified; finally, by comparing the similarity between the historical electricity load characteristics of the target users and the target electricity load characteristics of the users to be verified, a target similarity value is obtained, thereby determining whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified. In this way, this application significantly narrows the scope of investigation by focusing on the target analysis area, avoiding the waste of resources in traditional full-area screening, and improving the efficiency of cross-connection problem monitoring. Meanwhile, this application uses an automated and systematic approach to assess the similarity between historical electricity load characteristics and real-time target electricity load characteristics. This allows for the simultaneous identification of cross-connection issues caused by both file errors and wiring errors, reducing the probability of false positives and improving the accuracy of cross-connection identification. Furthermore, this application eliminates the need for manual on-site checks during the analysis process, reducing the workload of on-site investigations and improving the efficiency of discovering and troubleshooting cross-connection issues. In this way, this application not only ensures the continuity of electricity supply for users but also achieves proactive monitoring and rapid response to cross-connection issues, thus comprehensively improving the level of intelligence in power grid operation and maintenance. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 A flowchart of a method for monitoring cross-connection issues in electricity meters provided in this application;

[0048] Figure 2 A specific workflow diagram of the association matching module provided in this application;

[0049] Figure 3 A flowchart of a specific method for monitoring cross-connection issues in electricity meters provided in this application;

[0050] Figure 4 This application provides a schematic diagram of the structure of a monitoring device for cross-connection issues in electricity meters;

[0051] Figure 5 This application provides a structural diagram of an electronic device. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] As the core basis for electricity transactions and bill settlement between power supply companies and electricity customers, the accuracy of electricity meters directly affects the vital interests of both parties. Instances of electricity meters being connected to other households are common errors during installation and replacement, falling into two categories: errors in documentation and errors in wiring. Both can lead to discrepancies between meter readings and actual electricity consumption, resulting in incorrect, under-, or overcharged electricity bills. In severe cases, this can even lead to legal disputes and electrical safety hazards, making it a critical issue of mutual concern for both power grid companies and customers. Current methods for detecting instances of electricity meter connection have significant limitations: the load control method requires on-site operation by staff, consuming manpower and resources and disrupting normal customer electricity use; while connection detection devices do not require disconnecting load switches, they still rely on on-site work and are difficult to locate due to concealed wiring, and they only compare electrical parameters at a specific moment, failing to effectively identify households with similar electricity usage; methods based on electricity consumption comparison are only suitable for electricity meter replacement scenarios and rely solely on electricity consumption data for a single time period, limiting their accuracy in the face of diverse customer electricity loads. Currently, issues related to cross-connection of electricity meters mostly rely on customers' reactive measures after discovery, lacking effective proactive identification methods. Therefore, this application provides a monitoring solution for cross-connection of electricity meters, enabling efficient, accurate remote monitoring and precise location of such issues.

[0054] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for monitoring cross-connection issues in electricity meters, which may include:

[0055] Step S11: Determine the target electricity meter of the target user to be analyzed, and obtain the historical electricity meter operation data and predicted electricity load characteristics corresponding to the target user.

[0056] In this embodiment, the first step is to identify the target electricity meter for the target user to be analyzed. The target electricity meter can be a newly installed meter or a replacement meter. Next, it is necessary to obtain the historical meter operation data and predicted electricity load characteristics for the target user. The predicted electricity load characteristics can be obtained based on the customer's business expansion electricity data. This data consists of the industry category, geographical location, and key data from the equipment list provided by the user during the business expansion application, primarily representing the expected electricity load characteristics of the newly installed customer after commissioning.

[0057] Step S12: Determine the historical electricity load characteristics corresponding to the target user based on the historical electricity meter operation data and the predicted electricity load characteristics, and determine the target distribution area to be analyzed based on the distribution area corresponding to the target electricity meter and the preset range conditions.

[0058] In this embodiment, the user's historical electricity load characteristics can be determined based on the historical electricity meter operation data monitored and recorded by the user's electricity meter and the predicted electricity load characteristics. It should be noted that this embodiment is based on the target electricity meter's commissioning time for analysis. First, it is necessary to determine the commissioning time of the target electricity meter and judge whether the commissioning time is greater than a preset time threshold.

[0059] Next, in one specific implementation, if the commissioning time of the target electricity meter is greater than a preset time threshold, the historical first electricity load characteristics corresponding to the target user are analyzed based on a preset data collection interval and historical electricity meter operation data. Furthermore, the historical second electricity load characteristics corresponding to the target user are analyzed based on the freezing interval corresponding to the frozen data of the target electricity meter and historical electricity meter operation data. Specifically, the preset data collection interval is much shorter than the freezing interval corresponding to the frozen data of the target electricity meter, and the time dimension of the historical first electricity load characteristics is shorter than the time dimension of the historical second electricity load characteristics. In particular, if the commissioning time of the target electricity meter is greater than the preset time threshold, the historical electricity load characteristics of the target user can be directly analyzed based on the historical electricity meter operation data. It should be noted that if the target electricity meter is a newly installed electricity meter, the historical electricity meter operation data only includes the historical operation data of the target electricity meter; if the target electricity meter is a replaced electricity meter, the historical electricity meter operation data includes the historical operation data of both the target electricity meter and the replaced historical electricity meter. Historical electricity meter operating data is analyzed based on preset data collection intervals to determine the historical primary electricity load characteristics of the target user, i.e., short-term electricity load characteristics. Further, the short-term electricity load characteristics consist of data measured and recorded by the user's electricity meter, including voltage, current, power factor, active power, reactive power, active energy consumption, and reactive energy consumption. All data are sorted in time series, specifically determined by the data collection interval, including frequencies such as every 1 minute, 5 minutes, 15 minutes, and 60 minutes. Next, historical electricity meter operating data is analyzed based on the freeze interval corresponding to the frozen data of the target electricity meter to determine the historical secondary electricity load characteristics of the target user, i.e., medium- and long-term electricity load characteristics. Further, the medium- and long-term electricity load characteristics consist of data measured and recorded by the user's electricity meter, including voltage, current, power factor, active power, reactive power, active energy consumption, and reactive energy consumption. All data are sorted in time series, specifically determined by the data freeze interval, including frequencies such as daily freeze, weekly freeze, and monthly freeze.

[0060] In another specific implementation, if the commissioning time of the target electricity meter is no greater than a preset time threshold, the historical first electricity load characteristics corresponding to the target user are analyzed based on the preset data collection interval and historical electricity meter operation data. Target reference users are matched based on the predicted electricity load characteristics, and the historical second electricity load characteristics corresponding to the target user are determined based on the electricity load characteristics corresponding to the target reference users. Specifically, if the commissioning time of the target electricity meter is no greater than the preset time threshold, it may be impossible to analyze the historical medium- and long-term electricity load characteristics of the target user. Therefore, for the historical first electricity load characteristics, i.e., short-term electricity load characteristics, they can be directly obtained by analyzing historical electricity meter operation data based on the preset data collection interval. For the historical second electricity load characteristics, i.e., medium- and long-term electricity load characteristics, it is necessary to first match target reference users based on information representing predicted electricity load characteristics such as industry classification, installed capacity, geographical location, and electrical equipment in the business expansion electricity data. Then, the electricity load characteristics corresponding to the target reference users are analyzed based on the operation data of the electricity meters already in operation, and this analysis is used as a reference to determine the medium- and long-term electricity load characteristics of the target users.

[0061] It should be noted that, in order to accurately determine the monitoring scope of cross-connection issues, this embodiment can determine the target analysis area based on the transformer area corresponding to the target electricity meter and the nearby transformer areas within a preset range, so as to further monitor whether there are electricity meters in the target analysis area that have cross-connection issues with the target electricity meter.

[0062] Step S13: Determine the energy meter to be verified corresponding to the user to be verified in the target analysis area, and determine the target power load characteristics corresponding to the user to be verified based on the operating data of the energy meter to be verified.

[0063] In this embodiment, a work order statistics module is also designed to determine the unverified electricity meters corresponding to the unverified users in the target analysis area. The specific workflow of the work order statistics module may include: first, determining the installation time corresponding to the target electricity meter, and determining a target time period based on a preset time interval condition and the installation time; then, determining the unverified electricity meters corresponding to the unverified users in the target analysis area based on the target time period; finally, generating a corresponding list of unverified electricity meters based on the electricity meter number corresponding to the unverified electricity meters, so as to locate the unverified electricity meters that have cross-user problems with the target electricity meter based on the electricity meter number in the list of unverified electricity meters.

[0064] Specifically, the work order statistics module can primarily screen new installation or replacement records of target users. It determines the target time period based on the installation time of the target electricity meter and preset time interval conditions, and retrieves the electricity meters to be verified for users within the target analysis area that have new installation or replacement records within the target time period. Finally, the work order statistics module can generate a corresponding list of electricity meters to be verified. Based on this list, it can query the operating data of the electricity meters to be verified and analyze the new electricity load characteristics corresponding to the users to be verified, i.e., the target electricity load characteristics, including: the first target electricity load characteristic and the second target electricity load characteristic. The first target electricity load characteristic represents the short-term electricity load characteristics of the users to be verified, while the second target electricity load characteristic represents the medium- and long-term electricity load characteristics. It should be noted that the new electricity load characteristics consist of data measured and recorded by the newly commissioned electricity meters of the user, including voltage, current, power factor, active power, reactive power, active energy consumption, and reactive energy consumption, all of which are sorted in time series. Based on the operating time of newly commissioned electricity meters, for meters with a short operating time, such as less than one month, only the short-term electricity load characteristics of the user to be verified are analyzed to obtain the target first electricity load characteristics. Then, based on the business expansion electricity data of the user to be verified, the corresponding reference user is determined. Based on the medium- and long-term electricity load characteristics of the reference user, the medium- and long-term electricity load characteristics of the user to be verified are analyzed to obtain the target second electricity load characteristics. For electricity meters with a long operating time, the short-term electricity load characteristics of the user to be verified are analyzed to obtain the target first electricity load characteristics, and the medium- and long-term electricity load characteristics of the user to be verified are analyzed to obtain the target second electricity load characteristics.

[0065] Step S14: Compare the historical electricity load characteristics with the target electricity load characteristics to obtain a target similarity value, and monitor whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value.

[0066] In this embodiment, an association matching module is also designed to compare the similarity between historical electricity load characteristics and target electricity load characteristics. That is, the association matching module can perform similarity comparison based on historical first electricity load characteristics, historical second electricity load characteristics, target first electricity load characteristics, and target second electricity load characteristics to obtain a target similarity value; wherein, the time dimension of the target first electricity load characteristics is equal to the time dimension of the historical first electricity load characteristics, and the time dimension of the target second electricity load characteristics is equal to the time dimension of the historical second electricity load characteristics.

[0067] It should be noted that the association matching module performs similarity comparisons based on historical first electricity load characteristics, historical second electricity load characteristics, target first electricity load characteristics, and target second electricity load characteristics to obtain a target similarity value. The specific process may include: firstly, determining the first time series data corresponding to the historical first electricity load characteristics, the second time series data corresponding to the historical second electricity load characteristics, the third time series data corresponding to the target first electricity load characteristics, and the fourth time series data corresponding to the target second electricity load characteristics; then, determining the first difference between the first time series data and the third time series data, and determining the difference between the second time series data and the fourth time series data. The second gap between the data is determined; then, the first difference time series data between the first time series data and the third time series data is determined, and the second difference time series data between the second time series data and the fourth time series data is determined; then, based on the first gap and the first difference time series data, a consistency evaluation is performed on the first time series data and the third time series data to obtain a first evaluation result, and based on the second gap and the second difference time series data, a consistency evaluation is performed on the second time series data and the fourth time series data to obtain a second evaluation result; finally, the target similarity value is determined based on the first evaluation result and the second evaluation result.

[0068] For details, see Figure 2 As shown, in this embodiment, similarity comparisons are performed according to short-term and medium-to-long-term electricity load characteristics. The short-term electricity load characteristics include historical first electricity load characteristics and target first electricity load characteristics; the medium-to-long-term electricity load characteristics include historical second electricity load characteristics and target second electricity load characteristics. First, the historical first electricity load characteristics, historical second electricity load characteristics, target first electricity load characteristics, and target second electricity load characteristics are each broken down into several time-series data, such as voltage time-series data and current time-series data, to obtain the first time-series data corresponding to the historical first electricity load characteristics, the second time-series data corresponding to the historical second electricity load characteristics, the third time-series data corresponding to the target first electricity load characteristics, and the fourth time-series data corresponding to the target second electricity load characteristics. Next, each first time-series data is compared with each third time-series data; each second time-series data is compared with each fourth time-series data, for example, comparing the voltage time-series data of the historical first electricity load characteristics with the voltage time-series data of the target first electricity load characteristics.

[0069] For more details, see Figure 2As shown, the comparison process first calculates the maximum value, maximum value index, minimum value, minimum value index, and average value for each group of time series data to be compared, and then evaluates the differences between the groups of time series data based on these characteristics. This yields the first difference between the first and third time series data, and the second difference between the second and fourth time series data. Next, for each group of time series data to be compared, the difference time series data for each group is calculated by subtracting corresponding values. This yields the first difference time series data between the first and third time series data, and the second difference time series data between the second and fourth time series data. Simultaneously, the mean, variance, and standard deviation of the difference time series data are calculated. Finally, a consistency evaluation is performed on each group of time series data based on the differences and characteristics, and the consistency evaluation results for each group of time series data are obtained. Subsequently, the time series similarity index corresponding to each group of time series data can be determined based on the first preset weight and the consistency evaluation results corresponding to each group of time series data. Finally, the target similarity value between the historical electricity load characteristics and the target electricity load characteristics can be determined based on the second preset weight and the time series similarity index corresponding to each group of time series data.

[0070] In this embodiment, an anomaly warning module is also designed to monitor whether there is a cross-connection problem between the target energy meter and the energy meter to be verified based on the target similarity value. The specific process may include: if the target similarity value is greater than a preset similarity threshold, determining the energy meter number corresponding to the energy meter to be verified, and generating corresponding cross-connection risk warning information based on the energy meter number; and sending the cross-connection risk warning information to the target management personnel based on a preset notification method to facilitate investigation of the target energy meter and the energy meter to be verified. Specifically, the association matching module can send the target similarity value and the energy meter number of the energy meter to be verified to the anomaly warning module. The anomaly warning module first determines whether the target similarity value is greater than the preset similarity threshold. If so, it indicates that there may be a cross-connection problem between the target energy meter and the energy meter to be verified. The anomaly warning module can generate cross-connection risk warning information based on the energy meter number corresponding to the energy meter to be verified, and send the cross-connection risk warning information to the target management personnel through a preset notification method to guide the target management personnel to conduct on-site investigation of the target energy meter and the energy meter to be verified. The preset notification methods include, but are not limited to, generating work orders, system pop-ups, system prompts, voice calls, and smart text messages.

[0071] In one specific implementation, see Figure 3As shown, the specific process of the method for monitoring cross-connection issues of electricity meters can be as follows: First, acquire the target user's business expansion electricity consumption data and historical electricity meter operation data, and generate short-term and medium-to-long-term electricity load characteristics of the target user based on the business expansion electricity consumption data and historical electricity meter operation data; simultaneously, acquire relevant information of the user to be verified through the work order statistics module, and generate the user's electricity load characteristics; next, input the target user's short-term and medium-to-long-term electricity load characteristics and the user's electricity load characteristics into the association matching module for analysis; finally, the association matching module transmits the analysis results to the anomaly warning module to realize the monitoring and warning of cross-connection issues of electricity meters. In this way, this embodiment analyzes the user's electricity load characteristics through multi-dimensional electricity consumption data, according to short-term and medium-to-long-term scales, and quantifies the risk of cross-connection of newly installed or replaced electricity meters at similar times and locations through the similarity value of electricity load characteristics, thereby guiding on-site investigation and analysis, thus greatly reducing the workload of on-site investigation and improving the detection and investigation efficiency of cross-connection issues. Furthermore, this embodiment can adapt to changes in load characteristics caused by variations in user electricity load and differences in electricity usage habits.

[0072] As can be seen from the above, this embodiment first identifies the target electricity meter of the target user to be analyzed, and obtains the historical electricity meter operation data and predicted electricity load characteristics of the target user; then, based on the historical electricity meter operation data and predicted electricity load characteristics, it determines the historical electricity load characteristics of the target user, and determines the target substation to be analyzed based on the substation corresponding to the target electricity meter and preset range conditions; subsequently, it identifies the electricity meter to be verified corresponding to the user to be verified in the target substation to be analyzed, and determines the target electricity load characteristics of the user to be verified based on the operation data of the electricity meter to be verified; finally, it compares the historical electricity load characteristics with the target electricity load characteristics to obtain the target similarity value, and monitors whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value. As can be seen from the above, this embodiment first identifies the target electricity meter of the target user to be analyzed and collects the historical electricity meter operation data and predicted electricity load characteristics of the target user; then, it combines the historical electricity meter operation data and predicted electricity load characteristics to determine the historical electricity load characteristics of the target user, and simultaneously determines the target analysis area based on the transformer substation to which the target electricity meter belongs and a preset range; next, it identifies the electricity meter to be verified of the user to be verified within the target analysis area, and determines the corresponding target electricity load characteristics based on the operation data of the electricity meter to be verified; finally, it compares the similarity between the historical electricity load characteristics of the target user and the target electricity load characteristics of the user to be verified to obtain a target similarity value, thereby determining whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified. In this way, this embodiment significantly narrows the scope of investigation by focusing on the target analysis area, avoids the waste of resources in traditional full-area screening, and improves the efficiency of cross-connection problem monitoring. Meanwhile, this embodiment uses an automated and systematic approach to determine the similarity between historical electricity load characteristics and real-time target electricity load characteristics. This allows for the simultaneous identification of cross-connection issues caused by file errors and wiring errors, reducing the probability of false positives and improving the accuracy of cross-connection identification. Furthermore, this embodiment eliminates the need for manual on-site checks during the analysis process, reducing the workload of on-site investigations and improving the efficiency of discovering and troubleshooting cross-connection issues. In this way, this embodiment not only ensures the continuity of power supply for users but also achieves proactive monitoring and rapid response to cross-connection issues, thus comprehensively improving the intelligence level of power grid operation and maintenance.

[0073] Accordingly, see Figure 4 As shown in the figure, this application embodiment also provides a device for monitoring electricity meter cross-connection problems, which may include:

[0074] The target electricity meter determination module 11 is used to determine the target electricity meter to be analyzed for the target user, and to obtain the historical electricity meter operation data and predicted electricity load characteristics corresponding to the target user.

[0075] The module 12 for determining the area to be analyzed is used to determine the historical electricity load characteristics corresponding to the target user based on the historical electricity meter operation data and the predicted electricity load characteristics, and to determine the target area to be analyzed based on the area corresponding to the target electricity meter and the preset range conditions.

[0076] The energy meter to be verified determination module 13 is used to determine the energy meter to be verified corresponding to the user to be verified in the target analysis area, and to determine the target power load characteristics corresponding to the user to be verified based on the operating data of the energy meter to be verified.

[0077] The cross-connection problem monitoring module 14 is used to compare the historical electricity load characteristics with the target electricity load characteristics to obtain a target similarity value, and monitor whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value.

[0078] In some specific embodiments, the module 12 for determining the area to be analyzed may include:

[0079] The condition judgment unit is used to determine the commissioning time of the target electricity meter and to determine whether the commissioning time is greater than a preset time threshold.

[0080] The historical electricity load characteristic determination unit is used to analyze the historical first electricity load characteristics corresponding to the target user based on a preset data acquisition interval and the historical electricity meter operation data if the commissioning time is greater than the preset time threshold, and to analyze the historical second electricity load characteristics corresponding to the target user based on the freeze interval corresponding to the frozen data of the target electricity meter and the historical electricity meter operation data; wherein, the preset data acquisition interval is much smaller than the freeze interval corresponding to the frozen data of the target electricity meter, and the time dimension of the historical first electricity load characteristics is smaller than the time dimension of the historical second electricity load characteristics.

[0081] In some specific embodiments, the condition judgment unit may be followed by:

[0082] The historical first electricity load characteristic determination unit is used to analyze the historical first electricity load characteristics of the target user based on the preset data acquisition interval and the historical electricity meter operation data if the commissioning time is not greater than the preset time threshold.

[0083] The reference user matching unit is used to match target reference users based on the predicted electricity load characteristics, and to determine the historical second electricity load characteristics corresponding to the target user based on the electricity load characteristics corresponding to the target reference user.

[0084] In some specific embodiments, the energy meter determination module 13 may include:

[0085] The target time period determination unit is used to determine the installation time corresponding to the target energy meter, and to determine the target time period based on the preset time interval condition and the installation time;

[0086] The unit for determining the energy meter to be verified is used to determine the energy meter to be verified corresponding to the user to be verified in the target analysis area based on the target time period.

[0087] The electricity meter list generation unit is used to generate a corresponding electricity meter list to be verified based on the electricity meter number corresponding to the electricity meter to be verified, so as to locate the electricity meter to be verified that has a cross-connection problem with the target electricity meter based on the electricity meter number in the electricity meter list to be verified.

[0088] In some specific embodiments, the energy meter determination module 13 may include:

[0089] The target electricity load characteristic determination unit is used to determine the target first electricity load characteristic and the target second electricity load characteristic corresponding to the user to be verified based on the operating data of the electricity meter to be verified.

[0090] Accordingly, the cross-user problem monitoring module 14 may include:

[0091] The similarity comparison submodule is used to perform similarity comparison based on the historical first electricity load feature, the historical second electricity load feature, the target first electricity load feature, and the target second electricity load feature to obtain the target similarity value; wherein, the time dimension of the target first electricity load feature is equal to the time dimension of the historical first electricity load feature, and the time dimension of the target second electricity load feature is equal to the time dimension of the historical second electricity load feature.

[0092] In some specific implementations, the similarity comparison submodule may include:

[0093] A time series data determination unit is used to determine the first time series data corresponding to the historical first electricity load characteristic, the second time series data corresponding to the historical second electricity load characteristic, the third time series data corresponding to the target first electricity load characteristic, and the fourth time series data corresponding to the target second electricity load characteristic;

[0094] The gap determination unit is used to determine a first gap between the first time series data and the third time series data, and to determine a second gap between the second time series data and the fourth time series data;

[0095] The difference time series data determination unit is used to determine the first difference time series data between the first time series data and the third time series data, and to determine the second difference time series data between the second time series data and the fourth time series data;

[0096] A consistency evaluation unit is used to evaluate the consistency between the first time series data and the third time series data based on the first gap and the first difference time series data to obtain a first evaluation result, and to evaluate the consistency between the second time series data and the fourth time series data based on the second gap and the second difference time series data to obtain a second evaluation result.

[0097] A similarity value determination unit is used to determine the target similarity value based on the first evaluation result and the second evaluation result.

[0098] In some specific embodiments, the cross-user problem monitoring module 14 may include:

[0099] The early warning information generation unit is used to determine the electricity meter number corresponding to the electricity meter to be verified if the target similarity value is greater than a preset similarity threshold, and generate corresponding cross-household risk early warning information based on the electricity meter number.

[0100] The early warning information sending unit is used to send the cross-connection risk early warning information to the target management personnel based on a preset notification method, so as to investigate the target electricity meter and the electricity meter to be verified.

[0101] Furthermore, embodiments of this application also disclose an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the electricity meter cross-connection problem monitoring method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be a computer.

[0102] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0103] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0104] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the electricity meter cross-connection problem monitoring method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0105] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for monitoring cross-connection issues in electricity meters. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0106] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0107] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0109] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0110] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for monitoring cross-connection issues in electricity meters, characterized in that, include: Identify the target electricity meters for the target users to be analyzed, and obtain the historical electricity meter operation data and predicted electricity load characteristics corresponding to the target users; Based on the historical electricity meter operation data and the predicted electricity load characteristics, the historical electricity load characteristics corresponding to the target user are determined, and the target distribution area to be analyzed is determined based on the distribution area corresponding to the target electricity meter and the preset range conditions. Identify the energy meters to be verified for the users to be verified in the target analysis area, and determine the target electricity load characteristics for the users to be verified based on the operating data of the energy meters to be verified. The historical electricity load characteristics are compared with the target electricity load characteristics to obtain a target similarity value. Based on the target similarity value, it is used to monitor whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified.

2. The method for monitoring cross-connection issues in electricity meters according to claim 1, characterized in that, The step of determining the historical electricity load characteristics corresponding to the target user based on the historical electricity meter operating data and the predicted electricity load characteristics includes: Determine the commissioning time of the target electricity meter and determine whether the commissioning time is greater than a preset time threshold; If the commissioning time is greater than the preset time threshold, the historical first electricity load characteristics of the target user are analyzed based on the preset data collection interval and the historical electricity meter operation data, and the historical second electricity load characteristics of the target user are analyzed based on the freeze interval corresponding to the frozen data of the target electricity meter and the historical electricity meter operation data. Wherein, the preset data acquisition interval is much smaller than the freezing interval corresponding to the frozen data of the target electricity meter, and the time dimension of the historical first electricity load feature is smaller than the time dimension of the historical second electricity load feature.

3. The method for monitoring cross-connection issues in electricity meters according to claim 2, characterized in that, After determining whether the commissioning time is greater than a preset time threshold, the method further includes: If the commissioning time is not greater than the preset time threshold, the historical first electricity load characteristics of the target user are analyzed based on the preset data collection interval and the historical electricity meter operation data. Based on the predicted electricity load characteristics, target reference users are matched, and based on the electricity load characteristics corresponding to the target reference users, the historical second electricity load characteristics corresponding to the target users are determined.

4. The method for monitoring cross-connection issues in electricity meters according to claim 1, characterized in that, The step of determining the energy meter to be verified corresponding to the user to be verified in the target analysis area includes: Determine the installation time corresponding to the target electricity meter, and determine the target time period based on the preset time interval condition and the installation time; Based on the target time period, determine the energy meter to be verified corresponding to the user to be verified in the target analysis area; A corresponding list of electricity meters to be verified is generated based on the electricity meter number corresponding to the electricity meter to be verified, so as to locate the electricity meter to be verified that has a cross-connection problem with the target electricity meter based on the electricity meter number in the list of electricity meters to be verified.

5. The method for monitoring cross-connection issues in electricity meters according to claim 3, characterized in that, The process of determining the target electricity load characteristics corresponding to the user to be verified based on the operating data of the electricity meter to be verified includes: Based on the operating data of the electricity meter to be verified, the target first electricity load characteristics and target second electricity load characteristics corresponding to the user to be verified are determined. Accordingly, the step of comparing the historical electricity load characteristics with the target electricity load characteristics to obtain a target similarity value includes: A similarity comparison is performed based on the historical first electricity load characteristics, the historical second electricity load characteristics, the target first electricity load characteristics, and the target second electricity load characteristics to obtain the target similarity value; Wherein, the time dimension of the target first electricity load characteristic is equal to the time dimension of the historical first electricity load characteristic, and the time dimension of the target second electricity load characteristic is equal to the time dimension of the historical second electricity load characteristic.

6. The method for monitoring cross-connection issues in electricity meters according to claim 5, characterized in that, The step of performing a similarity comparison based on the historical first electricity load characteristics, the historical second electricity load characteristics, the target first electricity load characteristics, and the target second electricity load characteristics to obtain the target similarity value includes: Determine the first time series data corresponding to the historical first electricity load characteristic, the second time series data corresponding to the historical second electricity load characteristic, the third time series data corresponding to the target first electricity load characteristic, and the fourth time series data corresponding to the target second electricity load characteristic; Determine a first gap between the first time series data and the third time series data, and determine a second gap between the second time series data and the fourth time series data; Determine the first difference time series data between the first time series data and the third time series data, and determine the second difference time series data between the second time series data and the fourth time series data; Based on the first gap and the first difference time series data, a consistency evaluation is performed on the first time series data and the third time series data to obtain a first evaluation result; and based on the second gap and the second difference time series data, a consistency evaluation is performed on the second time series data and the fourth time series data to obtain a second evaluation result. The target similarity value is determined based on the first evaluation result and the second evaluation result.

7. The method for monitoring cross-connection issues in electricity meters according to any one of claims 1 to 6, characterized in that, The step of monitoring whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value includes: If the target similarity value is greater than the preset similarity threshold, the meter number corresponding to the meter to be verified is determined, and corresponding cross-connection risk warning information is generated based on the meter number. Based on a preset notification method, the cross-connection risk warning information is sent to the target management personnel so that the target electricity meter and the electricity meter to be verified can be investigated.

8. A device for monitoring cross-connection issues in electricity meters, characterized in that, include: The target electricity meter determination module is used to determine the target electricity meter of the target user to be analyzed, and to obtain the historical electricity meter operation data and predicted electricity load characteristics of the target user. The module for determining the area to be analyzed is used to determine the historical electricity load characteristics corresponding to the target user based on the historical electricity meter operation data and the predicted electricity load characteristics, and to determine the target area to be analyzed based on the area corresponding to the target electricity meter and preset range conditions. The module for determining the energy meter to be verified is used to determine the energy meter to be verified corresponding to the user in the target analysis area, and to determine the target electricity load characteristics corresponding to the user to be verified based on the operating data of the energy meter to be verified. The cross-connection problem monitoring module is used to compare the historical electricity load characteristics with the target electricity load characteristics to obtain a target similarity value, and to monitor whether there is a cross-connection problem between the target electricity meter and the electricity meter to be verified based on the target similarity value.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, which is loaded and executed by the processor to implement the method for monitoring cross-connection problems of electricity meters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the method for monitoring cross-connection problems of electricity meters as described in any one of claims 1 to 7.

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