Transformer area data processing method and device, storage medium and electronic device

Abstract

The invention discloses a transformer area data processing method and device, a storage medium and an electronic device, and relates to the technical field of power systems. The method comprises the following steps: acquiring first operation data and second operation data of a power system within a preset time period; performing clustering processing on the first operation data to obtain third operation data, and performing clustering processing on the second operation data to obtain fourth operation data; determining a similar index based on the third operation data and the fourth operation data; and constructing a target topological connection diagram of the user ammeter and the preset transformer area based on the similar indexes. The technical problem that the connection relation between the user electric meter and the low-voltage transformer area cannot be accurately identified in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, storage medium and electronic device for processing substation data. Background Art

[0002] With the development of smart grids, the refined management and maintenance of power systems have become increasingly important. As an important part of the power system, the operating efficiency of low-voltage substations directly affects the end-users' electricity experience and service quality. However, traditional low-voltage substation management often relies on manual inspections and empirical judgments. This method is not only time-consuming and labor-intensive, but also prone to misjudgments and missed detections. Especially in large-scale urban power grids, this management model obviously can no longer meet the growing demand. Traditional data analysis methods are unable to cope with large-scale data sets, especially for the identification of the connection relationship between user meters and low-voltage substations. Traditional statistical methods are difficult to achieve ideal accuracy. In addition, due to the complex and changeable topological structure of low-voltage substations, traditional static analysis methods are difficult to adapt to the dynamically changing power grid environment.

[0003] In this context, how to use advanced information technology and big data analysis methods to improve the intelligence level of low-voltage substation management has become an important research direction. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, storage medium and electronic device for processing substation data, so as to at least solve the technical problem in the related art that the connection relationship between the user's electric meter and the low-voltage substation cannot be accurately identified.

[0005] According to one embodiment of the present invention, a method for processing substation data is provided, the method comprising: obtaining first operating data and second operating data of an electric power system within a preset time period, wherein the first operating data is historical operating data of a user's electric meter, and the second operating data is historical operating data of a preset substation; clustering the first operating data to obtain third operating data, and clustering the second operating data to obtain fourth operating data; determining similarity indicators based on the third operating data and the fourth operating data, wherein the similarity indicators are used to reflect the degree of match between the power consumption mode of the user's electric meter and the power supply mode of the preset substation; constructing a target topology connection diagram between the user's electric meter and the preset substation based on the similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user's electric meter and the preset substation in the electric power system.

[0006] Optionally, obtaining first operating data and second operating data of the power system within a preset time period includes: obtaining third operating data of the user's electricity meter and fourth operating data of a preset substation; performing data elimination processing on the third operating data to obtain fifth operating data, and performing data elimination processing on the fourth operating data to obtain sixth operating data; performing data interpolation processing on the fifth operating data to obtain first operating data, and performing data interpolation processing on the sixth operating data to obtain second operating data.

[0007] Optionally, determining the similarity index based on the third operating data and the fourth operating data includes: determining a first mean value and a first standard deviation of the user's electricity meter, and a second mean value and a second standard deviation of a preset substation based on the third operating data and the fourth operating data; determining a first feature vector of the user's electricity meter based on the first mean value and the first standard deviation, and determining a second feature vector of the preset substation based on the second mean value and the second standard deviation; determining the similarity index based on the first mean value, the first standard deviation, the first feature vector, the second mean value, the second standard deviation, and the second feature vector.

[0008] Optionally, constructing a target topological connection diagram between the user's electricity meter and the preset substation based on similarity indicators includes: determining an initial matching result between the user's electricity meter and the preset substation based on the similarity indicators, wherein the initial matching result is used to filter out a pairing list between the user's electricity meter and the preset substation; constructing an optimization function based on the similarity indicators, wherein the optimization function is used to improve the pairing accuracy of the initial matching result; updating the initial matching result based on the optimization function to obtain a target matching result; and constructing a target topological connection diagram between the user's electricity meter and the preset substation based on the target matching result.

[0009] Optionally, determining the initial matching result between the user meter and the preset substation based on the similarity index includes: determining the candidate matching result between the user meter and the preset substation based on the similarity index; screening the candidate matching results based on a first matching threshold and a second matching threshold to obtain an initial matching result, wherein the first matching threshold is greater than zero, the second matching threshold is less than zero, and the similarity index corresponding to the user meter and the preset substation in the initial matching result is greater than or equal to the first matching threshold or less than the second matching threshold.

[0010] Optionally, constructing an optimization function based on similarity indicators includes: constructing a diagonal matrix based on similarity indicators; constructing an operating data matrix of a user's electric meter based on the first operating data; constructing a parameter matrix of a preset substation based on the second operating data; constructing a topological structure matrix based on the initial matching results, the first operating data and the second operating data; and constructing an optimization function based on the diagonal matrix, the first operating data matrix, the parameter matrix and the topological structure matrix.

[0011] Optionally, constructing a target topology connection diagram between user meters and preset substations based on target matching results includes: creating an initial topology connection diagram based on user meters and preset substations; updating the initial topology connection diagram based on the target matching results and similarity indicators to obtain a target topology connection diagram.

[0012] According to one embodiment of the present invention, there is also provided a substation data processing device, the device comprising: an acquisition module, used to acquire first operating data and second operating data of the power system within a preset time period, wherein the first operating data is the historical operating data of the user's electricity meter, and the second operating data is the historical operating data of the preset substation; a clustering module, used to cluster the first operating data to obtain third operating data, and cluster the second operating data to obtain fourth operating data; a determination module, used to determine similarity indicators based on the third operating data and the fourth operating data, wherein the similarity indicators are used to reflect the matching degree between the power consumption mode of the user's electricity meter and the power supply mode of the preset substation; a construction module, used to construct a target topology connection diagram between the user's electricity meter and the preset substation based on the similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user's electricity meter and the preset substation in the power system.

[0013] Optionally, the acquisition module is also used to acquire the third operating data of the user's electricity meter and the fourth operating data of the preset substation; perform data elimination processing on the third operating data to obtain the fifth operating data, and perform data elimination processing on the fourth operating data to obtain the sixth operating data; perform data interpolation processing on the fifth operating data to obtain the first operating data, and perform data interpolation processing on the sixth operating data to obtain the second operating data.

[0014] Optionally, the determination module is also used to determine a first mean value and a first standard deviation of the user's electricity meter, and a second mean value and a second standard deviation of a preset substation based on the third operating data and the fourth operating data; determine a first feature vector of the user's electricity meter based on the first mean value and the first standard deviation, and determine a second feature vector of the preset substation based on the second mean value and the second standard deviation; determine a similarity index based on the first mean value, the first standard deviation, the first feature vector, the second mean value, the second standard deviation, and the second feature vector.

[0015] Optionally, the construction module is also used to determine the initial matching results of the user's meter and the preset substation based on similarity indicators, wherein the initial matching results are used to screen out a pairing list of the user's meter and the preset substation; construct an optimization function based on the similarity indicators, wherein the optimization function is used to improve the pairing accuracy of the initial matching results; update the initial matching results based on the optimization function to obtain the target matching results; and construct a target topological connection diagram of the user's meter and the preset substation based on the target matching results.

[0016] Optionally, the determination module is also used to determine candidate matching results between the user meter and the preset station area based on similarity indicators; the candidate matching results are screened based on a first matching threshold and a second matching threshold to obtain an initial matching result, wherein the first matching threshold is greater than zero, the second matching threshold is less than zero, and the similarity indicator corresponding to the user meter and the preset station area in the initial matching result is greater than or equal to the first matching threshold or less than the second matching threshold.

[0017] Optionally, the construction module is also used to construct a diagonal matrix based on similarity indicators; construct an operating data matrix of the user's electric meter based on the first operating data; construct a parameter matrix of the preset substation based on the second operating data; construct a topological structure matrix based on the initial matching results, the first operating data and the second operating data; and construct an optimization function based on the diagonal matrix, the first operating data matrix, the parameter matrix and the topological structure matrix.

[0018] Optionally, the construction module is further used to create an initial topology connection diagram based on user electricity meters and preset substations; update the initial topology connection diagram based on the target matching results and similarity indicators to obtain a target topology connection diagram.

[0019] According to one embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute any of the above-mentioned station area data processing methods when running on a computer or a processor.

[0020] According to one embodiment of the present invention, there is further provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned station area data processing methods.

[0021] According to one embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the computer program implements any of the above-mentioned station area data processing methods.

[0022] In an embodiment of the present invention, by obtaining first operating data and second operating data of the power system within a preset time period, wherein the first operating data is the historical operating data of the user's meter, and the second operating data is the historical operating data of the preset substation; clustering processing is performed on the first operating data to obtain third operating data, and clustering processing is performed on the second operating data to obtain fourth operating data; similarity indicators are determined based on the third operating data and the fourth operating data, wherein the similarity indicators are used to reflect the matching degree between the power consumption mode of the user's meter and the power supply mode of the preset substation; based on the similarity indicators, a target topology connection diagram of the user's meter and the preset substation is constructed, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user's meter and the preset substation in the power system, thereby achieving the purpose of effectively identifying the connection relationship between the user's meter and the low-voltage substation, thereby achieving the technical effect of efficiently processing large-scale data sets, accurately identifying the connection relationship between the user's meter and the low-voltage substation, and being able to continuously optimize the matching results as the power grid environment changes, thereby solving the technical problem in the related technology that the connection relationship between the user's meter and the low-voltage substation cannot be accurately identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0024] Figure 1 is a flow chart of a method for processing area data according to one embodiment of the present invention;

[0025] Figure 2 It is a block diagram of a low voltage area topology intelligent identification system based on data fusion according to one embodiment of the present invention;

[0026] Figure 3 It is a structural block diagram of a station area data processing device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0027] To facilitate understanding, some descriptions of concepts related to the embodiments of the present invention are exemplarily provided for reference.

[0028] As shown below:

[0029] Low voltage area: refers to the distribution area with lower voltage level in the power system, generally refers to the distribution area with voltage level below 1000V.

[0030] Topological structure: refers to the structure of the connection relationship between each node in the system, which describes the connection method and topological relationship between each element in the system.

[0031] Static analysis method: refers to a method of analyzing the parameters of each node and branch of the system without considering the dynamic process of the system, so as to obtain the steady-state working state and performance indicators of the system.

[0032] Data fusion: refers to integrating data from different data sources and obtaining more comprehensive and accurate information through data processing and analysis. Data fusion can help improve the reliability, accuracy and application value of data.

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In the description of this embodiment, unless otherwise specified, the meaning of "multiple" is two or more. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] According to one embodiment of the present invention, an embodiment of a station area data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] The method embodiment can be executed in an electronic device including a memory and a processor, a similar control device or a system. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the electronic device may also include a communication device and a display device for communication functions. It can be understood by a person of ordinary skill in the art that the above structural description is only illustrative and does not limit the structure of the above electronic device. For example, the electronic device may also include more or fewer components than the above structural description, or have a configuration different from the above structural description.

[0037] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a programmable logic device (field-programmable gate array, FPGA), a neural network processor (neural-network processing unit, NPU), a tensor processing unit (tensor processing unit, TPU), an artificial intelligence (artificial intelligent, AI) type processor and other processing devices. Among them, different processing units can be independent components or integrated in one or more processors. In some instances, the electronic device may also include one or more processors.

[0038] The memory can be used to store computer programs, such as storing a computer program corresponding to the area data processing method in the embodiment of the present invention, and the processor implements the above-mentioned area data processing method by running the computer program stored in the memory. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0039] The communication device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the communication device includes a network adapter (network interface controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the communication device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0040] The display device may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of a mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contact and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0041] In this embodiment, a method for processing station area data running on an electronic device is provided. Figure 1 is a flow chart of a method for processing area data according to one embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0042] Step S10, obtaining first operation data and second operation data of the power system within a preset time period, wherein the first operation data is the historical operation data of the user's electric meter, and the second operation data is the historical operation data of the preset substation;

[0043] In the embodiment of the present invention, the preset time period can be understood as a period of time in the past. For example, the preset time period can be a year in the past, which is not limited here.

[0044] The preset substation area can be understood as a specific substation area pre-selected in the power system as a research object. For example, the preset substation area can be a low-voltage substation area, which is not limited here.

[0045] Obtaining the first operating data and the second operating data of the power system within a preset time period can be understood as collecting the historical operating data of the user's electric meter in the power system and the historical operating data of the preset low-voltage area in the past period of time. Exemplarily, a power system of a city power grid is being studied, which covers multiple residential communities and commercial areas. The current goal is to collect the historical operating data of user meters and low-voltage areas in the past year, and use these data to optimize the connection relationship between user meters and low-voltage areas. Obtain multi-dimensional operating data of user meters and low-voltage areas in the past year from the database of the power company. These data may include, but are not limited to: user meter data may include daily or hourly power consumption, voltage, current, power factor, etc.; low-voltage area data may include daily or hourly total power supply, voltage, current, power factor, etc.

[0046] In an embodiment of the present invention, by obtaining the first operating data and the second operating data of the power system within a preset time period, wherein the first operating data is the historical operating data of the user's electricity meter, and the second operating data is the historical operating data of the preset substation, a solid data foundation is provided for realizing intelligent identification of low-voltage substation topology based on data fusion, which helps power companies to achieve refined management and preventive maintenance, and ultimately improve the operating efficiency of the power grid and the service experience of users.

[0047] Step S11, clustering the first operation data to obtain third operation data, and clustering the second operation data to obtain fourth operation data;

[0048] In the embodiment of the present invention, clustering processing can be understood as a data processing method. Exemplarily, the first operating data and the second operating data can be clustered using, but not limited to, the K-means clustering algorithm. The K-means clustering algorithm is an unsupervised learning algorithm that can divide data into K clusters. The data points within each cluster are similar to each other, while there are large differences between different clusters. This principle is used to screen out key features. Set the number of clusters K, and then use the K-means algorithm to cluster the historical operating data of user meters and low-voltage areas. The working principle of the K-means algorithm is: randomly select K center points as the initial centroid, assign each data point to the cluster where the nearest centroid is located, update the centroid of each cluster to the mean of its member data points, and repeat the above process until the centroid no longer changes or reaches a predetermined maximum number of iterations to obtain a clustering result.

[0049] Clustering the first operating data to obtain the third operating data, and clustering the second operating data to obtain the fourth operating data can be understood as clustering the historical operating data of the user's meter by adopting a clustering processing method to obtain the key data of the user's meter, and clustering the historical operating data of the preset substation by adopting a clustering processing method to obtain the key data of the low-voltage substation. Exemplarily, for user meters, we can focus on samples with typical behavior patterns, such as high-energy consumption user meters, low-energy consumption user meters, etc.; for low-voltage substations, we can focus on substations with higher or lower loads. Furthermore, the key data of high-energy consumption user meters and low-energy consumption user meters can be the average power consumption and peak power consumption at each time point; the key data of high-load and low-load substations can be the substation power consumption, which is not limited here.

[0050] In the embodiment of the present invention, by clustering the first operating data to obtain the third operating data, and clustering the second operating data to obtain the fourth operating data, not only the data set is simplified and the analyzability of the data is improved, but also a more accurate basis is provided for subsequent intelligent identification, thereby enhancing the performance and practicality of the entire smart grid topology identification system.

[0051] Step S12, determining a similarity index based on the third operation data and the fourth operation data, wherein the similarity index is used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset station area;

[0052] In an embodiment of the present invention, the power consumption pattern can be understood as the characteristic performance of the user's power consumption behavior in a specific time period, including the time distribution of power consumption, power consumption habits (such as the difference in power consumption during peak and off-peak periods), the type of power consumption equipment and the frequency of use, etc., which are not limited here.

[0053] The power supply mode can be understood as the power supply characteristics of a preset substation (or any power supply unit), including the temporal changes in power supply, power supply capacity, power supply quality (such as voltage stability), and power dispatching strategies to cope with load changes, etc., which are not limited here.

[0054] Determining the similarity index based on the third operating data and the fourth operating data can be understood as calculating the similarity index value between the user's meter and the low-voltage area based on the user's meter key data and the low-voltage area key data.

[0055] In the embodiment of the present invention, similarity indexes are determined based on the third operation data and the fourth operation data, wherein the similarity indexes are used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset area, which plays an important role in improving the operation efficiency, stability and service quality of the power system, and promoting the development of smart grid technology and energy conservation and emission reduction. This technical means provides power companies with more accurate data analysis tools, which helps them formulate more scientific and reasonable power management and operation strategies.

[0056] Step S13, constructing a target topology connection diagram between user meters and preset substations based on similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between user meters and preset substations in the power system.

[0057] In an embodiment of the present invention, the connection relationship can be understood as the physical or logical connection between the user's meter and the preset substation in the power system. For example, in the power system, the user's meter is usually connected to the nearest low-voltage substation to receive power supply. This connection is not just a simple line connection, but also involves the complex logic of power distribution, management and monitoring. Through the calculation and analysis of similar indicators, it is possible to identify which user meters are most likely to be connected to which preset substations, thereby inferring the actual connection relationship in the power system. The identification of this connection relationship is crucial to the operation, maintenance and fault diagnosis of the power grid because it reveals the actual path of the power flow, which is not limited here.

[0058] The network structure can be understood as a comprehensive layout of the connection relationship between all user meters and preset areas in the power system, which describes the overall architecture and organizational form of the power system. For example, in the context of smart grids, the network structure can be regarded as a complex graph model, in which user meters and preset areas are nodes in the graph, and the connection relationship between user meters and preset areas is represented by edges, which is not limited here.

[0059] Constructing a target topological connection diagram between user meters and preset substations based on similarity indicators can be understood as, by way of example, determining the optimal connection relationship between user meters and preset substations in the power system through a quantitative analysis method, and presenting this relationship in a graphical form, which is not limited here.

[0060] In an embodiment of the present invention, a target topology connection diagram between user meters and preset substations is constructed based on similar indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between user meters and preset substations in the power system, which not only provides a clear insight into the connection relationship and network structure of the power system, but also supports power companies to conduct more efficient and intelligent power grid management and operation, which is of great significance for improving the quality of power services, reducing costs, and promoting energy conservation and emission reduction.

[0061] By obtaining the first operation data and the second operation data of the power system within a preset time period, wherein the first operation data is the historical operation data of the user's meter, and the second operation data is the historical operation data of the preset area, a solid data foundation is provided for realizing the intelligent identification of the low-voltage area topology based on data fusion, which helps the power company to realize refined management and preventive maintenance, and ultimately improves the operation efficiency of the power grid and the service experience of users. By clustering the first operation data to obtain the third operation data, and clustering the second operation data to obtain the fourth operation data, not only the data set is simplified and the data analyzability is improved, but also a more accurate basis is provided for subsequent intelligent identification, thereby enhancing the performance and practicality of the entire smart grid topology identification system. Based on the third operation data and the fourth operation data, similarity indicators are determined, wherein the similarity indicators are used to reflect the matching degree between the power consumption mode of the user's meter and the power supply mode of the preset area, which plays an important role in improving the operation efficiency, stability and service quality of the power system, as well as promoting the development of smart grid technology and energy conservation and emission reduction. This technical means provides power companies with more accurate data analysis tools, which helps them to formulate more scientific and reasonable power management and operation strategies. Based on similar indicators, a target topology connection diagram between user meters and preset substations is constructed. The target topology connection diagram is used to graphically display the connection relationship and network structure between user meters and preset substations in the power system. It not only provides a clear insight into the connection relationship and network structure of the power system, but also supports power companies to conduct more efficient and intelligent grid management and operation, which is of great significance for improving the quality of power services, reducing costs, and promoting energy conservation and emission reduction.

[0062] Through the above steps, by obtaining the first operating data and the second operating data of the power system within a preset time period, wherein the first operating data is the historical operating data of the user's meter, and the second operating data is the historical operating data of the preset substation; clustering the first operating data to obtain the third operating data, and clustering the second operating data to obtain the fourth operating data; determining similarity indicators based on the third operating data and the fourth operating data, wherein the similarity indicators are used to reflect the matching degree between the power consumption mode of the user's meter and the power supply mode of the preset substation; constructing a target topology connection diagram of the user's meter and the preset substation based on the similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user's meter and the preset substation in the power system, the purpose of effectively identifying the connection relationship between the user's meter and the low-voltage substation is achieved, thereby achieving the technical effect of efficiently processing large-scale data sets, accurately identifying the connection relationship between the user's meter and the low-voltage substation, and being able to continuously optimize the matching results as the power grid environment changes, thereby solving the technical problem that the connection relationship between the user's meter and the low-voltage substation cannot be accurately identified in the related technology.

[0063] Optionally, in step S10, acquiring first operation data and second operation data of the power system within a preset time period may include the following execution steps:

[0064] Step S101, obtaining third operation data of the user's electric meter and fourth operation data of the preset area;

[0065] Step S102, performing data elimination processing on the third operation data to obtain fifth operation data, and performing data elimination processing on the fourth operation data to obtain sixth operation data;

[0066] Step S103, performing data interpolation processing on the fifth operation data to obtain first operation data, and performing data interpolation processing on the sixth operation data to obtain second operation data.

[0067] In the embodiment of the present invention, the data elimination process can be exemplarily understood as deleting duplicate values ​​and abnormal values ​​in the operating data, which is not limited here.

[0068] The data interpolation process can be understood as interpolating missing values, which is not limited here.

[0069] Acquiring the third operating data of the user's electric meter and the fourth operating data of the preset area can be understood as respectively acquiring the key data of the user's electric meter and the key data of the preset area, which is not limited here.

[0070] Performing data elimination processing on the third operating data to obtain the fifth operating data, and performing data elimination processing on the fourth operating data to obtain the sixth operating data can be understood as deleting duplicate values ​​and abnormal values ​​in the key data of the user's electricity meter to obtain the fifth operating data, and deleting duplicate values ​​and abnormal values ​​in the key data of the preset substation to obtain the sixth operating data, which are not limited here.

[0071] Performing data interpolation processing on the fifth operating data to obtain the first operating data, and performing data interpolation processing on the sixth operating data to obtain the second operating data can be understood as performing interpolation processing operations on the missing values ​​in the fifth operating data to obtain the first operating data, and performing interpolation processing operations on the missing values ​​in the sixth operating data to obtain the second operating data, which is not limited here.

[0072] In an embodiment of the present invention, by acquiring the third operating data of the user's electricity meter and the fourth operating data of the preset substation, and then performing data elimination processing on the third operating data to obtain the fifth operating data, and performing data elimination processing on the fourth operating data to obtain the sixth operating data, then performing data interpolation processing on the fifth operating data to obtain the first operating data, and performing data interpolation processing on the sixth operating data to obtain the second operating data, by acquiring, eliminating and interpolating the operating data of the user's electricity meter and the preset substation, the quality and integrity of the data can be significantly improved, providing a solid data foundation for the intelligent management and optimization of the power system, thereby improving the efficiency of power services and user experience, and promoting the innovation and development of smart grid technology.

[0073] Optionally, in step S12, determining the similarity index based on the third operating data and the fourth operating data may include the following execution steps:

[0074] Step S121, determining a first average value and a first standard deviation of the user's electric meter, and a second average value and a second standard deviation of the preset area based on the third operation data and the fourth operation data;

[0075] Step S122, determining a first feature vector of the user's electric meter based on the first average value and the first standard deviation, and determining a second feature vector of the preset station area based on the second average value and the second standard deviation;

[0076] Step S123, determining a similarity index based on the first mean value, the first standard deviation, the first eigenvector, the second mean value, the second standard deviation, and the second eigenvector.

[0077] In the embodiment of the present invention, the first average value can be understood as the average power consumption of the user's electricity meter, which is obtained by calculating the average value of all power consumption data points of the user's electricity meter in a specific time period (such as one day, one week or longer). The average value of all power consumption data points in the specific time period reflects the average power consumption of the user's electricity meter, which can help understand the user's typical power consumption behavior. Exemplarily, if the power consumption data of the user's electricity meter is a daily power consumption record, then the first average value is the arithmetic mean of the daily power consumption record, which is not limited here.

[0078] The first standard deviation can be understood as the statistical standard deviation of the user's electricity consumption data, which measures the degree of fluctuation of the electricity consumption value relative to the first average value. For example, the smaller the standard deviation, the more stable the user's electricity consumption and the smaller the fluctuation range; conversely, the larger the standard deviation, the more unstable the user's electricity consumption and the larger the fluctuation range, which is not limited here.

[0079] The second average value can be understood as the average power supply value of the preset area, that is, the average value of all power supply data points of the preset area within the same time range. For example, the second average value reflects the average power supply capacity of the area, which is helpful for evaluating the load situation and power supply stability of the area, and is not limited here.

[0080] The second standard deviation can be understood as the statistical standard deviation of the power supply data of the preset area, which is used to measure the degree of fluctuation of the power supply value relative to the second average value. For example, the smaller the second standard deviation, the more stable the power supply of the preset area; the larger the standard deviation, the more unstable the power supply. The stability and volatility of the power supply of the preset area are crucial to the overall operating efficiency and stability of the power system, and are not limited here.

[0081] The first feature vector can be understood as a vector representation of the user's electric meter feature. Generally, each dimension (or element) of the vector corresponds to a statistical feature or other key attribute related to the user's electric meter.

[0082] The second feature vector can be understood as a vector representation of the preset station area feature.

[0083] Determining the first mean value and the first standard deviation of the user's electricity meter based on the third operating data and the fourth operating data, and the second mean value and the second standard deviation of the preset substation can be understood as determining the average power consumption value of the user's electricity meter and the statistical standard deviation of the user's electricity consumption data based on the third operating data and the fourth operating data, as well as the average power supply value of the preset substation and the statistical standard deviation of the power supply data of the preset substation.

[0084] Determining the first feature vector of the user's electric meter based on the first average value and the first standard deviation, and determining the second feature vector of the preset area based on the second average value and the second standard deviation can be understood as obtaining the user's electric meter feature vector according to the average value and the standard deviation of the user's electric meter. For example, the user's electric meter feature vector f i It can be calculated using formula (1), which is not limited here:

[0085] f i =[μ i σ i ] Formula (1)

[0086] Among them, f i Represents the characteristic vector of the ith user's electricity meter, μ i represents the average value of the i-th user's electricity meter, σ i Represents the standard deviation of the i-th user's electricity meter.

[0087] According to the average value and standard deviation of the low-pressure area, the low-pressure area characteristic vector is obtained. For example, the low-pressure area characteristic vector g jIt can be calculated using formula (2), which is not limited here:

[0088] g j =[μ j σ j ] Formula (2)

[0089] Among them, g j Represents the characteristic vector of the jth low-pressure area, μ j represents the average value of the jth low-pressure area, σ j Represents the standard deviation of the j-th low-pressure area.

[0090] Determining the similarity index based on the first average value, the first standard deviation, the first eigenvector, the second average value, the second standard deviation, and the second eigenvector can be understood as calculating the similarity index value between the user meter and the low-voltage area based on the average value and standard deviation of the low-voltage area, the average value and standard deviation of the user meter, the user meter eigenvector, and the low-voltage area eigenvector. Exemplarily, the calculation formula of the similarity index value can be as shown in formula (3), which is not limited here:

[0091]

[0092] Among them, r ij represents the similarity index value between the ith user meter and the jth low-voltage area, f it Represents the user meter feature vector of the i-th user meter at time point t, represents the average value of the ith user's electricity meter, g jt represents the low-pressure area feature vector of the j-th low-pressure area at time point t, represents the average value of the jth low-pressure area, and T represents the maximum time point of historical operation data collection.

[0093] In an embodiment of the present invention, a first mean value and a first standard deviation of a user's electric meter, as well as a second mean value and a second standard deviation of a preset substation are determined based on the third operating data and the fourth operating data; a first eigenvector of the user's electric meter is determined based on the first mean value and the first standard deviation, and a second eigenvector of the preset substation is determined based on the second mean value and the second standard deviation; a similarity index is determined based on the first mean value, the first standard deviation, the first eigenvector, the second mean value, the second standard deviation, and the second eigenvector, which not only helps to accurately identify the best matching relationship between the user's electric meter and the preset substation, but also provides strong data support for the intelligent management and optimization of the power system, and promotes the development of the power industry towards a more efficient, intelligent and user-friendly direction.

[0094] Optionally, in step S13, constructing a target topological connection diagram of user electric meters and preset substations based on similarity indicators may include the following execution steps:

[0095] Step S131, determining an initial matching result between the user's electric meter and the preset station area based on the similarity index, wherein the initial matching result is used to screen out a pairing list between the user's electric meter and the preset station area;

[0096] Step S132, constructing an optimization function based on the similarity index, wherein the optimization function is used to improve the pairing accuracy of the initial matching result;

[0097] Step S133, updating the initial matching result based on the optimization function to obtain a target matching result;

[0098] Step S134: construct a target topology connection diagram between the user's electric meter and the preset substation based on the target matching result.

[0099] In the embodiment of the present invention, determining the initial matching result between the user's electric meter and the preset substation based on the similarity index can be understood as determining the primary matching result between the user's electric meter and the low-voltage substation based on the similarity index value. For example, the value range of the similarity index value is between -1 and 1. The closer the similarity index value is to 1 and -1, the higher the similarity, and the closer the similarity index value is to 0, the lower the similarity. Based on this logic, the primary matching result is obtained by setting the matching threshold, which is not limited here.

[0100] Building an optimization function based on similarity indicators, updating the initial matching results based on the optimization function, and obtaining the target matching results can be understood as building an optimization function based on similarity indicator values, optimizing the primary matching results with the optimization function, and obtaining optimized matching results.

[0101] Constructing a target topological connection diagram between user meters and preset substations based on target matching results can be understood as constructing a topological connection diagram between user meters and low-voltage substations based on optimized matching results.

[0102] In an embodiment of the present invention, an initial matching result between a user's electric meter and a preset substation is determined based on a similarity index, wherein the initial matching result is used to screen out a pairing list of the user's electric meter and the preset substation, an optimization function is constructed based on the similarity index, wherein the optimization function is used to improve the pairing accuracy of the initial matching result, the initial matching result is updated based on the optimization function to obtain a target matching result, and a target topological connection diagram between the user's electric meter and the preset substation is constructed based on the target matching result. The power system can achieve more intelligent, efficient and user-friendly management. At the same time, this process also provides a practical basis for technological innovation in the power industry and promotes the modernization of power services.

[0103] Optionally, in step S131, determining the initial matching result between the user's electric meter and the preset station area based on the similarity index may include the following execution steps:

[0104] Step S1311, determining candidate matching results between the user's electric meter and the preset area based on similarity indicators;

[0105] Step S1312, screening the candidate matching results based on the first matching threshold and the second matching threshold to obtain an initial matching result, wherein the first matching threshold is greater than zero, the second matching threshold is less than zero, and the similarity index corresponding to the user's meter and the preset station area in the initial matching result is greater than or equal to the first matching threshold or less than the second matching threshold.

[0106] In the embodiment of the present invention, the candidate matching results can be understood as the user electric meters and low-voltage areas corresponding to the similar index values ​​that have not been screened.

[0107] The first matching threshold may be understood as a threshold for screening candidate matching results. Exemplarily, the first matching threshold may be represented by θ1, which is not limited herein.

[0108] The second matching threshold may be understood as a threshold for screening candidate matching results. Exemplarily, the first matching threshold may be represented by θ2, which is not limited herein.

[0109] Determining the candidate matching results between the user's electric meter and the preset substation based on similarity indicators can be exemplarily understood as screening out the most likely pairing relationship from a large data set through a data-driven approach using machine learning and statistical analysis techniques, which is not limited here.

[0110] The candidate matching results are screened based on the first matching threshold and the second matching threshold to obtain the initial matching result, which can be understood as constructing a first matching threshold θ1 and a second matching threshold θ2, wherein the first matching threshold θ1 is greater than 0 and the second matching threshold θ2 is less than 0. When the similarity index value is greater than or equal to the first matching threshold or less than the second matching threshold, the user meter and low-voltage substation corresponding to the similarity index value are used as the primary matching result. The user meter and low-voltage substation corresponding to the similarity index value greater than the second matching threshold and less than the first matching threshold are deleted. Preferably, the first matching threshold θ1=0.6 and the second matching threshold θ2=-0.6, which are not limited here.

[0111] In an embodiment of the present invention, candidate matching results between a user's electric meter and a preset substation are determined based on similarity indicators, and the candidate matching results are screened based on a first matching threshold and a second matching threshold to obtain an initial matching result, wherein the first matching threshold is greater than zero, and the second matching threshold is less than zero, and the similarity indicator corresponding to the user's electric meter and the preset substation in the initial matching result is greater than or equal to the first matching threshold or less than the second matching threshold, thereby enabling the user's electric meter to be quickly and accurately identified as a preliminary correct match with the preset substation based on data science methods, providing a solid foundation for subsequent optimization analysis, fault detection, resource allocation, and personalized services.

[0112] Optionally, in step S132, constructing an optimization function based on similarity indicators may include the following execution steps:

[0113] Step S1321, constructing a diagonal matrix based on similarity indicators;

[0114] Step S1322, constructing an operation data matrix of the user's electric meter based on the first operation data;

[0115] Step S1323, constructing a parameter matrix of a preset station area based on the second operation data;

[0116] Step S1324, constructing a topological structure matrix based on the initial matching result, the first operation data and the second operation data;

[0117] Step S1325, constructing an optimization function based on the diagonal matrix, the first operation data matrix, the parameter matrix and the topological structure matrix.

[0118] In the embodiment of the present invention, constructing a diagonal matrix based on similarity index can be understood as constructing a diagonal matrix according to the similarity index values ​​corresponding to the user meter and the low-voltage area in the primary matching results. The diagonal elements in the diagonal matrix are the squares of the similarity index values ​​corresponding to the user meter and the low-voltage area in the primary matching results, so that the user meter and the low-voltage area with greater similarity can be highlighted, so that these pairs are given more attention during optimization, which is not limited here.

[0119] Constructing the operation data matrix of the user meter based on the first operation data can be understood as constructing the user meter operation data matrix according to the historical operation data of the user meter. The user meter operation data construction matrix is ​​constructed according to the historical operation data of the user meter in the primary matching result. The rows of the matrix represent different user meters, the columns represent different time points, and the element values ​​are the power consumption at the corresponding time, so as to facilitate subsequent calculations and operations, which are not limited here.

[0120] The parameter matrix of the preset area constructed based on the second operation data can be understood as, for example, constructing the parameter matrix of the low-voltage area according to the historical operation data of the low-voltage area. The operation data construction matrix of the low-voltage area is constructed according to the historical operation data of the low-voltage area in the primary matching result. The rows of the matrix represent different low-voltage areas, the columns represent different time points, and the element values ​​are the power supply at the corresponding moment. The operation data construction matrix of the low-voltage area is used to describe the operation of the low-voltage area. The construction of the user meter operation data matrix and the parameter matrix of the low-voltage area does not take into account all the historical operation data, but only considers the historical operation data corresponding to the user meter and the low-voltage area in the primary matching results. The primary matching results usually contain the most likely correct pairing of the user meter and the low-voltage area. Therefore, considering only this part of the data helps to focus on the most important information and avoid the influence of irrelevant noise, which means that it can be optimized on a smaller data set to improve the accuracy of the final matching result, and it is easier to find the optimal solution in a limited data set, which is not limited here.

[0121] The construction of the topological structure matrix based on the initial matching results, the first operating data, and the second operating data can be understood as, for example, constructing the topological structure matrix based on the primary matching results, the historical operating data of the user's meter, and the historical operating data of the low-voltage area. The topological structure matrix is ​​constructed by combining the primary matching results, the historical operating data of the user's meter, and the historical operating data of the low-voltage area. The topological structure matrix is ​​an N×M matrix, and each element X in the matrix ij Indicates user meter X i Is it connected to the low voltage area X j . Normally, if the user's meter X i Connect to low voltage area X j , then X ij =1, otherwise X ij =0, no restriction here.

[0122] The optimization function constructed based on the diagonal matrix, the first operation data matrix, the parameter matrix and the topological structure matrix can be understood as constructing the optimization function based on the diagonal matrix, the user meter operation data matrix, the parameter matrix of the low voltage area and the topological structure matrix. The optimization function can be expressed by formula (4):

[0123]

[0124] in, The goal of the optimization function is to obtain the minimized topological matrix, Y i represents the operating data matrix of the ith user's electricity meter, X i represents the topological structure matrix of the ith user power and low-voltage area, A represents the parameter matrix of the low-voltage area, T represents the transposition operation, represents a diagonal matrix.

[0125] The goal of the optimization function is to minimize the optimization function, obtain the minimized topological structure matrix, and obtain the optimized matching result based on the minimized topological structure matrix.

[0126] The optimization function optimizes the primary matching results through the gradient descent algorithm. The gradient descent algorithm is a commonly used optimization method for finding the local minimum of the optimization function. In a given optimization problem, we hope to find an optimal topological structure matrix X. i , so that the loss function is minimized. To achieve this, it is necessary to continuously iterate and update the value of the topological matrix X until it converges to a local minimum point. When the optimization function converges to the local minimum point, the topological matrix X obtained is the minimized topological structure matrix. The optimized matching result is obtained according to the minimized topological structure matrix, which is not restricted here.

[0127] The process of optimizing the optimization function through the gradient descent algorithm is a technical means, and the specific optimization process will not be described in detail in this embodiment.

[0128] In the embodiment of the present invention, a diagonal matrix is ​​constructed based on similarity indicators, an operating data matrix of the user's electric meter is constructed based on the first operating data, a parameter matrix of the preset substation is constructed based on the second operating data, a topological structure matrix is ​​constructed based on the initial matching result, the first operating data and the second operating data, and an optimization function is constructed based on the diagonal matrix, the first operating data matrix, the parameter matrix and the topological structure matrix. The power company can build an intelligent power grid management and optimization system based on data science and machine learning methods, which can not only improve the operating efficiency of the power grid, but also provide users with higher quality power services. At the same time, this intelligent operation and maintenance mode also reduces the operation and maintenance burden of the power company and improves the intelligence level and resilience of the entire power system.

[0129] Optionally, in step S134, constructing a target topological connection diagram between the user's electric meter and the preset substation based on the target matching result may include the following execution steps:

[0130] Step S1341, creating an initial topology connection diagram based on the user's electric meter and the preset station area;

[0131] Step S1342, updating the initial topology connection graph based on the target matching result and the similarity index to obtain the target topology connection graph.

[0132] In the embodiment of the present invention, the creation of the initial topological connection diagram based on the user meter and the preset area can be understood as, according to the best correspondence between the user meter and the low-voltage area, creating an empty graphic structure for representing the topological connection diagram between the user meter and the low-voltage area, which is not limited here.

[0133] Based on the target matching results and similarity indicators, the initial topology connection graph is updated to obtain the target topology connection graph. It can be understood as using an adjacency matrix or an adjacency list to represent the target topology connection graph. All user meters and low-voltage areas are added to the graph as nodes of the graph. Each user meter is a node, and each low-voltage area is also a node. According to the optimization matching results, edges are added to the graph. If a user meter is best matched with a low-voltage area, an edge from the user meter to the low-voltage area is added to the graph. The weight of the edge can be set to the similarity index value, indicating the degree of similarity between the user meter and the low-voltage area. Use a visualization tool to draw the graph to show the connection relationship between the user meter and the low-voltage area. Nodes of different colors or shapes can be used to represent user meters and low-voltage areas, and edges of different thicknesses or colors can be used to represent the degree of similarity, which is not limited here.

[0134] In the embodiment of the present invention, an initial topology connection diagram is created based on the user's electric meter and the preset substation area, and the initial topology connection diagram is updated based on the target matching result and the similarity index to obtain the target topology connection diagram.

[0135] Figure 2 FIG. 1 is a block diagram of a low voltage area topology intelligent identification system based on data fusion according to one embodiment of the present invention. Figure 2 As shown, the system includes: a collection module, a preprocessing module, a key data acquisition module, a similarity index value calculation module, a primary matching result acquisition module, an optimization module and a connection module.

[0136] Among them, the collection module is used to collect the historical operation data of user meters and low-voltage substations in the power system over the past period of time; the preprocessing module is connected to the collection module and is used to preprocess the historical operation data; the key data acquisition module is connected to the preprocessing module and is used to extract the key data of user meters and the key data of low-voltage substations from the preprocessed historical operation data using a clustering algorithm; the similarity index value calculation module is connected to the key data acquisition module and is used to calculate the similarity index value between the user meter and the low-voltage substation based on the key data of the user meter and the key data of the low-voltage substation; the primary matching result acquisition module is connected to the similarity index value calculation module and is used to determine the primary matching result between the user meter and the low-voltage substation based on the similarity index value; the optimization module is connected to the primary matching result acquisition module and is used to construct an optimization function based on the similarity index value, and the optimization function optimizes the primary matching result to obtain an optimized matching result; the connection module is connected to the optimization module and is used to construct a topological connection diagram between the user meter and the low-voltage substation based on the optimized matching result.

[0137] This application can efficiently process large-scale data sets, accurately identify the connection relationship between user meters and low-voltage areas, and continuously optimize the matching results as the power grid environment changes. The primary matching results are determined by calculating similarity, and the primary results are further optimized using optimization functions, making the final topology map more accurate and reliable.

[0138] This application can help identify the most likely candidates by using key data to calculate similarity and perform preliminary matching. By screening out these candidates, you can focus on a more detailed analysis of them in the next optimization process, thereby improving the accuracy of the final matching results. Significantly reduce the amount of data for subsequent processing, thereby speeding up processing and improving overall work efficiency. This application can also use the similarity index value as a parameter of the optimization function to more finely adjust the matching relationship between the user's meter and the low-voltage area. By optimizing the algorithm, the accuracy of the matching results can be further improved to ensure that the final matching results are more in line with the actual situation. Based on the primary matching results, the optimization function can be used to further eliminate those matches with lower similarity and retain the most likely correct matching results. This helps to improve the overall performance of the system and reduce the negative impact of incorrect matching.

[0139] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0140] In this embodiment, a station area data processing device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0141] Figure 3 is a structural block diagram of a station area data processing device according to one embodiment of the present invention, such as Figure 3As shown, taking a substation data processing device 300 as an example, the device includes: an acquisition module 301, used to acquire first operating data and second operating data of the power system within a preset time period, wherein the first operating data is the historical operating data of the user's electric meter, and the second operating data is the historical operating data of the preset substation; a clustering module 302, used to cluster the first operating data to obtain third operating data, and cluster the second operating data to obtain fourth operating data; a determination module 303, used to determine similarity indicators based on the third operating data and the fourth operating data, wherein the similarity indicators are used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset substation; a construction module 304, used to construct a target topology connection diagram between the user's electric meter and the preset substation based on the similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user's electric meter and the preset substation in the power system.

[0142] Optionally, the acquisition module 301 is also used to acquire the third operating data of the user's electric meter and the fourth operating data of the preset substation; perform data elimination processing on the third operating data to obtain the fifth operating data, and perform data elimination processing on the fourth operating data to obtain the sixth operating data; perform data interpolation processing on the fifth operating data to obtain the first operating data, and perform data interpolation processing on the sixth operating data to obtain the second operating data.

[0143] Optionally, the determination module 301 is also used to determine a first mean value and a first standard deviation of the user's electricity meter, and a second mean value and a second standard deviation of a preset substation based on the third operating data and the fourth operating data; determine a first feature vector of the user's electricity meter based on the first mean value and the first standard deviation, and determine a second feature vector of the preset substation based on the second mean value and the second standard deviation; determine a similarity index based on the first mean value, the first standard deviation, the first feature vector, the second mean value, the second standard deviation, and the second feature vector.

[0144] Optionally, the construction module 304 is also used to determine the initial matching results between the user meter and the preset substation based on similarity indicators, wherein the initial matching results are used to screen out a pairing list between the user meter and the preset substation; construct an optimization function based on the similarity indicators, wherein the optimization function is used to improve the pairing accuracy of the initial matching results; update the initial matching results based on the optimization function to obtain a target matching result; and construct a target topology connection diagram between the user meter and the preset substation based on the target matching result.

[0145] Optionally, the determination module 301 is also used to determine candidate matching results between the user meter and the preset substation based on similarity indicators; the candidate matching results are screened based on a first matching threshold and a second matching threshold to obtain an initial matching result, wherein the first matching threshold is greater than zero, the second matching threshold is less than zero, and the similarity indicator corresponding to the user meter and the preset substation in the initial matching result is greater than or equal to the first matching threshold or less than the second matching threshold.

[0146] Optionally, the construction module 304 is also used to construct a diagonal matrix based on similarity indicators; construct an operating data matrix of the user's electric meter based on the first operating data; construct a parameter matrix of the preset substation based on the second operating data; construct a topological structure matrix based on the initial matching results, the first operating data and the second operating data; and construct an optimization function based on the diagonal matrix, the first operating data matrix, the parameter matrix and the topological structure matrix.

[0147] Optionally, the construction module 304 is further configured to create an initial topology connection diagram based on the user's electric meter and the preset substation area; update the initial topology connection diagram based on the target matching result and the similarity index to obtain a target topology connection diagram.

[0148] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0149] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any of the above method embodiments when running on a computer or a processor.

[0150] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0151] Step S10, obtaining first operation data and second operation data of the power system within a preset time period, wherein the first operation data is the historical operation data of the user's electric meter, and the second operation data is the historical operation data of the preset substation;

[0152] Step S11, clustering the first operation data to obtain third operation data, and clustering the second operation data to obtain fourth operation data;

[0153] Step S12, determining a similarity index based on the third operation data and the fourth operation data, wherein the similarity index is used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset station area;

[0154] Step S13, constructing a target topology connection diagram between user meters and preset substations based on similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between user meters and preset substations in the power system.

[0155] Optionally, in this embodiment, the above-mentioned computer-readable storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0156] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0157] Optionally, in this embodiment, the processor in the electronic device may be configured to run a computer program to perform the following steps:

[0158] Step S10, obtaining first operation data and second operation data of the power system within a preset time period, wherein the first operation data is the historical operation data of the user's electric meter, and the second operation data is the historical operation data of the preset substation;

[0159] Step S11, clustering the first operation data to obtain third operation data, and clustering the second operation data to obtain fourth operation data;

[0160] Step S12, determining a similarity index based on the third operation data and the fourth operation data, wherein the similarity index is used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset station area;

[0161] Step S13, constructing a target topology connection diagram between user meters and preset substations based on similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between user meters and preset substations in the power system.

[0162] An embodiment of the present invention further provides a computer program product, including a computer program, which implements the steps in any of the above method embodiments when executed by a processor.

[0163] Optionally, in this embodiment, the computer program in the above computer program product may be configured to perform the following steps when executed by a processor:

[0164] Step S10, obtaining first operation data and second operation data of the power system within a preset time period, wherein the first operation data is the historical operation data of the user's electric meter, and the second operation data is the historical operation data of the preset substation;

[0165] Step S11, clustering the first operation data to obtain third operation data, and clustering the second operation data to obtain fourth operation data;

[0166] Step S12, determining a similarity index based on the third operation data and the fourth operation data, wherein the similarity index is used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset station area;

[0167] Step S13, constructing a target topology connection diagram between user meters and preset substations based on similarity indicators, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between user meters and preset substations in the power system.

[0168] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0169] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0170] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0172] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0173] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0174] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk, etc., various media that can store program codes.

[0175] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing area data, characterized in that: The method comprises: Acquire first operation data and second operation data of the power system within a preset time period, wherein the first operation data is historical operation data of the user's electric meter, and the second operation data is historical operation data of a preset substation; performing clustering processing on the first operation data to obtain third operation data, and performing clustering processing on the second operation data to obtain fourth operation data; Determine a similarity index based on the third operation data and the fourth operation data, wherein the similarity index is used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset station area; A target topology connection diagram between the user's electric meter and the preset substation is constructed based on the similarity index, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user's electric meter and the preset substation in the power system.

2. The method according to claim 1, characterized in that The obtaining of first operating data and second operating data of the power system within a preset time period includes: Acquire the third operation data of the user's electric meter and the fourth operation data of the preset area; performing data elimination processing on the third operation data to obtain fifth operation data, and performing data elimination processing on the fourth operation data to obtain sixth operation data; The fifth operation data is subjected to data interpolation processing to obtain the first operation data, and the sixth operation data is subjected to data interpolation processing to obtain the second operation data.

3. The method according to claim 1, characterized in that Determining the similarity index based on the third operation data and the fourth operation data includes: Determine a first average value and a first standard deviation of the user's electric meter, and a second average value and a second standard deviation of the preset station area based on the third operation data and the fourth operation data; Determine a first feature vector of the user's electric meter based on the first average value and the first standard deviation, and determine a second feature vector of the preset station area based on the second average value and the second standard deviation; The similarity index is determined based on the first mean, the first standard deviation, the first eigenvector, the second mean, the second standard deviation, and the second eigenvector.

4. The method according to any one of claims 1 to 3, characterized in that The constructing of the target topological connection diagram between the user's electric meter and the preset area based on the similarity index comprises: Determine an initial matching result between the user's electric meter and the preset station area based on the similarity index, wherein the initial matching result is used to filter out a pairing list between the user's electric meter and the preset station area; constructing an optimization function based on the similarity index, wherein the optimization function is used to improve the pairing accuracy of the initial matching result; The initial matching result is updated based on the optimization function to obtain a target matching result; A target topological connection diagram between the user's electric meter and the preset substation is constructed based on the target matching result.

5. The method according to claim 4, characterized in that The determining of the initial matching result between the user's electric meter and the preset station area based on the similarity index comprises: Determine a candidate matching result between the user's electric meter and the preset station area based on the similarity index; The candidate matching results are screened based on a first matching threshold and a second matching threshold to obtain the initial matching result, wherein the first matching threshold is greater than zero, the second matching threshold is less than zero, and the similarity index corresponding to the user meter and the preset station area in the initial matching result is greater than or equal to the first matching threshold or less than the second matching threshold.

6. The method according to claim 4, characterized in that The constructing of the optimization function based on the similarity index comprises: Constructing a diagonal matrix based on the similarity index; Constructing an operation data matrix of the user's electric meter based on the first operation data; constructing a parameter matrix of the preset station area based on the second operation data; constructing a topological structure matrix based on the initial matching result, the first operation data and the second operation data; The optimization function is constructed based on the diagonal matrix, the first operation data matrix, the parameter matrix and the topology structure matrix.

7. The method according to claim 4, characterized in that The constructing of a target topological connection diagram between the user's electric meter and the preset area based on the target matching result comprises: Creating an initial topology connection diagram based on the user's electric meter and the preset station area; The initial topological connection graph is updated based on the target matching result and the similarity index to obtain the target topological connection graph.

8. A station area data processing device, characterized in that: The device comprises: An acquisition module, used to acquire first operation data and second operation data of the power system within a preset time period, wherein the first operation data is the historical operation data of the user's electric meter, and the second operation data is the historical operation data of the preset substation; a clustering module, configured to perform clustering processing on the first operation data to obtain third operation data, and to perform clustering processing on the second operation data to obtain fourth operation data; A determination module, configured to determine a similarity index based on the third operation data and the fourth operation data, wherein the similarity index is used to reflect the matching degree between the power consumption mode of the user's electric meter and the power supply mode of the preset station area; A construction module is used to construct a target topology connection diagram between the user meter and the preset substation based on the similarity index, wherein the target topology connection diagram is used to graphically display the connection relationship and network structure between the user meter and the preset substation in the power system.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the station area data processing method described in any one of claims 1 to 7 when running on a computer or a processor.

10. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the station area data processing method described in any one of claims 1 to 7.

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