Abnormality detection method, device and equipment in transformer area user transformer cutover scene and storage medium

Abstract

The invention discloses an anomaly detection method and device in a transformer area user transformer cutover scene, equipment and a storage medium. An anomaly detection method in a transformer area user transformer cutover scene comprises the steps of obtaining transformer area data before and after transformer area user transformer cutover, the transformer area data before transformer area user transformer cutover comprises transformer data and user data before transformer area user transformer cutover, and the transformer area data before transformer area user transformer cutover comprises transformer data before transformer area user transformer cutover and user data before transformer area user transformer cutover; the transformer area data after transformer area user transformer cutover comprises transformer data and user data after transformer area user transformer cutover; based on the transformer area data before and after transformer area user transformer cutover, first user transformer matching data before transformer area user transformer cutover and second user transformer matching data after transformer area user transformer cutover are determined respectively; and determining an anomaly detection result in the transformer area user-transformer cutover scene based on the first user-transformer matching data and the second user-transformer matching data. The problem that trouble is caused by incorrect user transformer data due to load cutover and the like to automatic meter reading of the electricity meters in the transformer area and correct statistics of the line loss of the transformer area is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an abnormality detection method, device, equipment and storage medium in a transformer cutover scenario. Background Art

[0002] With the construction and application of the electricity information collection system, the management of line losses in the substation area is gradually required to be refined. The accuracy of the household-to-transformer relationship is not only the basis for the electricity information collection system to realize remote meter reading, but also the guarantee for the statistics and accurate analysis of the substation line losses. Failure to grasp the correct household-to-transformer relationship not only affects the success rate of meter reading of the electricity information collection system, but also affects the development of substation line loss analysis. Most of the existing related technologies rely on manual judgment of household-to-transformer relationship division based on the direction of low-voltage lines, which is highly subjective, or judgment by shutting down transformers. This method is huge and time-consuming and labor-intensive. Due to load cutover and other reasons, the number of incorrect household-to-transformer relationships fluctuates, which causes trouble for the automatic meter reading of substation meters and the correct statistics of substation line losses. Summary of the invention

[0003] The present invention provides a method, device, equipment and storage medium for detecting anomalies in a scenario of household transformer cutover in a transformer area, so as to solve the problem of abnormal household transformer data in a scenario of household transformer cutover in a transformer area.

[0004] According to one aspect of the present invention, a method for detecting anomalies in a cutover scenario of a transformer substation is provided, the method comprising:

[0005] Obtaining the data of the substation area before and after the substation area household transformer cutover, wherein the substation area data before the substation area household transformer cutover includes the transformer data and user data before the substation area household transformer cutover, and the substation area data after the substation area household transformer cutover includes the transformer data and user data after the substation area household transformer cutover;

[0006] Determine first household transformer matching data before the household transformer cutover in the area and second household transformer matching data after the household transformer cutover in the area based on the area data before and after the household transformer cutover in the area;

[0007] An abnormality detection result in a household transformer cutover scenario in a substation is determined based on the first household transformer matching data and the second household transformer matching data.

[0008] According to another aspect of the present invention, a method and device for detecting anomalies in a subscriber-to-substation cutover scenario is provided, the device comprising:

[0009] The substation data acquisition module is used to acquire the substation data before and after the substation household transformer cutover, wherein the substation data before the substation household transformer cutover includes the transformer data and user data before the substation household transformer cutover, and the substation data after the substation household transformer cutover includes the transformer data and user data after the substation household transformer cutover;

[0010] A household transformer matching data acquisition module, used to determine first household transformer matching data before the household transformer cutover in the station area and second household transformer matching data after the household transformer cutover in the station area based on the station area data before and after the household transformer cutover in the station area;

[0011] The anomaly detection module is used to determine the anomaly detection result in the substation area substation cutover scenario based on the first substation matching data and the second substation matching data.

[0012] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the anomaly detection method in the substation user transformer cutover scenario of any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the anomaly detection method in a station area user transformer cutover scenario of any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention obtains the substation data before and after the substation household transformer cutover, wherein the substation data includes transformer data and user data, determines the first household transformer matching data before the substation household transformer cutover based on the substation data before the substation household transformer cutover, and determines the second household transformer matching data before the substation household transformer cutover based on the substation data after the substation household transformer cutover, determines the abnormal detection result in the substation household transformer cutover scenario according to the first household transformer matching data and the second household transformer matching data, solves the problem that incorrect household transformer data in the substation household transformer cutover scenario causes trouble for the automatic meter reading of the substation electric meter and the correct statistics of the substation line loss, performs abnormal detection on the household transformer data in the substation household transformer cutover scenario, is conducive to locating the abnormal user list of non-substation users, and makes adjustments after verification, saving manpower and material resources.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of an abnormality detection method in a transformer cutover scenario provided by the first embodiment of the present invention;

[0021] Figure 2 It is a structural schematic diagram of an abnormality detection device in a transformer cutover scenario provided by Embodiment 2 of the present invention;

[0022] Figure 3 It is a structural schematic diagram of an electronic device for implementing the abnormality detection method in the scenario of cell area household transformer cutover according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] 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 addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Embodiment 1

[0026] Figure 1This is a flowchart of an abnormality detection method in a transformer substation cutover scenario provided by Embodiment 1 of the present invention. This embodiment is applicable to the case of abnormality detection of household transformer data in a transformer substation cutover scenario. The household transformer data can be understood as the correspondence between the transformer and the user. The method can be executed by an abnormality detection device in a transformer substation cutover scenario. The abnormality detection device in a transformer substation cutover scenario can be implemented in the form of hardware and / or software. The abnormality detection device in a transformer substation cutover scenario can be configured in an electronic device, such as a mobile terminal such as a mobile phone, a tablet computer, a computer, or a server. Figure 1 As shown, the method includes:

[0027] S110. Acquire the substation data before and after the substation household transformer cutover, wherein the substation data before the substation household transformer cutover includes the transformer data and user data before the substation household transformer cutover, and the substation data after the substation household transformer cutover includes the transformer data and user data after the substation household transformer cutover.

[0028] In this embodiment, the area refers to an area or range powered by a transformer, and is usually used to describe the link in the distribution network that is directly connected to the user and distributes electric energy to the user. The area can be divided into several categories such as urban residential areas, commercial areas, industrial areas and agricultural areas according to different load types. Each area is an independent unit in the power system. There is no inclusion relationship between them, but they are connected and powered by transformers in the distribution network. Household transformer switching can be understood as switching the user's power connection from one transformer to another in order to balance the load of different transformers in the power system. Area data can be related information such as power use, distribution, monitoring and maintenance in the power supply area of ​​a transformer, such as basic information and operating status of the transformer, basic information of the user, etc. Optionally, the area data includes transformer data and user data in the area. Transformer data refers to a series of technical parameters and operating information related to the transformer, which is crucial for the design, manufacture, operation and maintenance and performance evaluation of the transformer. For example, transformer data can be the identification, location information, rated voltage and rated current of the transformer. Transformer data can be obtained through smart sensors and monitoring equipment installed on the transformer. The data of each transformer in the substation is not exactly the same. Among them, transformer data such as identification and location information can also be pre-stored, indicating that a transformer can be uniquely marked. The location information can be, for example, the coordinate information or address information of the transformer. User data refers to a series of information related to power users, such as the user's name, location information, and contact information. User data can be obtained through the database of the power company, and the data of each user in the substation is not exactly the same. By obtaining the substation data composed of transformer data and user data before and after the substation cutover, the transformer information and user information in the substation can be fully understood, providing data support for subsequent power maintenance and abnormal detection.

[0029] Optionally, the transformer data includes transformer coordinate information of the substation, and the user data includes user coordinate information. Optionally, the transformer data includes transformer structured address information, and the user data includes user structured address information. Optionally, the transformer data includes transformer coordinate information of the substation and transformer structured address information; the user data includes user coordinate information and user structured address information.

[0030] It is understandable that one or more of the transformer coordinate information and the transformer address information are pre-stored in the server; as well as one or more of the user address information or the user coordinate information. In some embodiments, the server stores the transformer coordinate information and the user address information. The coordinate information and the address information can be converted to each other, and the required transformer data and user data can be determined through the coordinate information and the address information.

[0031] Among them, obtaining the substation data before and after the substation household transformer cutover includes: obtaining the coordinate information of the substation transformer before and after the substation household transformer cutover, calling the electronic map interface, determining the substation address information corresponding to the coordinate information of the substation transformer, and converting the address information into transformer structured address information; and / or obtaining the user address information before and after the substation household transformer cutover, converting the user address information into user structured address information, and calling the electronic map interface to determine the user coordinate information corresponding to the user structured address information.

[0032] Specifically, transformer coordinate information usually refers to the specific location of the transformer in geographic space, which can be obtained through on-site surveys, querying the database of the power company, etc. For example, transformer coordinate information may involve specific provinces (autonomous regions, municipalities directly under the central government), counties (autonomous counties, county-level cities, districts), townships (towns), administrative townships (residents' committees), and roads. Transformer structured address information can be address information of a corresponding form converted from transformer coordinate information, which is convenient for subsequent information processing. For example, transformer coordinate information can be obtained through an electronic map interface, and transformer coordinate information is converted into corresponding substation address information through the electronic map interface, and then the converted substation address information is converted into transformer structured address information. Optionally, the electronic map interface can utilize an Xpath parsing method, using the lxml library, and after multiple traversals, convert transformer coordinate information into transformer structured address information.

[0033] User address information usually refers to the user's geographical location information, which can be obtained through on-site surveys, querying the address information provided by the user when registering with the power company, etc. For example, the user address information may involve specific provinces (autonomous regions, municipalities directly under the central government), counties (autonomous counties, county-level cities, districts), townships (towns), administrative townships (residents' committees), roads, communities, buildings, unit numbers and household rooms. User structured address information can be address information in a corresponding form converted from user address information. User coordinate information usually refers to the specific location of the user in the geographic space, such as longitude and latitude, which can be obtained by converting the user structured address information. For example, the user address information can be converted into user structured address information, and the user structured address information can be converted into corresponding user coordinate information by calling the electronic map interface. Optionally, the electronic map interface can use the Xpath parsing method, use the lxml library, and after multiple traversals, convert the user structured address information into user coordinate information. Optionally, the transformer coordinate information and the user coordinate information have the same data format. Optionally, the transformer structured address information and the user structured address information have the same data format.

[0034] S120: Determine first household transformer matching data before the household transformer cutover and second household transformer matching data after the household transformer cutover based on the station area data before and after the household transformer cutover.

[0035] In this embodiment, the first household transformer matching data may be matching data determined based on the transformer data and user data before the cutover of the household transformer in the substation area. The size of the first household transformer matching data may represent the degree of matching between the transformer data and the user data before the cutover of the household transformer in the substation area. Optionally, the similarity data between the transformer data and the user data before the cutover of the household transformer in the substation area may be determined as the first household transformer matching data. For example, the first household transformer matching data may be determined by calculating the distance between the transformer coordinate information and the user coordinate information before the cutover of the household transformer in the substation area to obtain the coordinate distance information, and determining the first household transformer matching data according to the coordinate distance information, wherein the first household transformer matching data is negatively correlated with the coordinate distance information, and the larger the coordinate distance information determined based on the transformer coordinate information and the user coordinate information, the smaller the first household transformer matching data is, representing that the degree of matching determined by the transformer data and the user data is smaller. The first household transformer matching data can also be determined by performing similarity calculation on the transformer structured address information and the user structured address information before the household transformer in the substation is cut over, obtaining address similarity data, and determining the first household transformer matching data based on the address similarity data, wherein the first household transformer matching data is positively correlated with the address similarity data, and the larger the address similarity data, the larger the first household transformer matching data, and the greater the matching degree determined by the transformer data and the user data. Optionally, the address similarity data of the transformer structured address information and the user structured address information is used as the first household transformer matching data. The first household transformer matching data can also be determined by performing distance calculation on the transformer coordinate information and the user coordinate information before the household transformer in the substation is cut over, and performing similarity calculation on the transformer structured address information and the user structured address information before the household transformer in the substation is cut over, obtaining coordinate distance information and address similarity data, and performing weighted processing on the coordinate distance information and the address similarity data to obtain the first household transformer matching data.

[0036] The second household transformer matching data may be data obtained based on the transformer coordinate information and user coordinate information after the cutover of the household transformer in the substation area and / or based on the transformer structured address information and user structured address information after the cutover of the household transformer in the substation area, which can characterize the degree of matching between the transformer data and the user data after the cutover of the household transformer in the substation area. Optionally, the similarity data of the transformer data and the user data after the cutover of the household transformer in the substation area can be determined as the second household transformer matching data. For example, the second household transformer matching data can be determined by performing a distance calculation on the transformer coordinate information and the user coordinate information after the cutover of the household transformer in the substation area, obtaining the coordinate distance information, and determining the second household transformer matching data according to the coordinate distance information. The second household transformer matching data can also be determined by performing a similarity calculation on the transformer structured address information and the user structured address information after the cutover of the household transformer in the substation area, obtaining the address similarity data, and using the address similarity data as the second household transformer matching data. The second household transformer matching data can also be determined by performing distance calculation on the transformer coordinate information and user coordinate information after the household transformer in the substation area is cut over, and performing similarity calculation on the transformer structured address information and user structured address information after the household transformer in the substation area is cut over, obtaining coordinate distance information and address similarity data, and performing weighted processing on the coordinate distance information and address similarity data to obtain the second household transformer matching data. The second household transformer matching data is negatively correlated with the coordinate distance information and positively correlated with the address similarity data.

[0037] It can be understood that the calculation method of the first household transformer matching data and the second household transformer matching data before the household transformer cutover in the substation is the same, and the abnormality detection of the household transformer cutover scenario in the substation is further performed based on the first household transformer matching data and the second household transformer matching data.

[0038] Specifically, the address similarity data is used to measure the similarity between the transformer structured address information and the user structured address information. Exemplarily, it can be obtained by calculating the Jaccard similarity coefficient between the transformer structured address information and the user structured address information. The Jaccard similarity coefficient is the ratio of the intersection size and the union size of two sets. The larger the Jaccard similarity coefficient between the transformer structured address information and the user structured address information, the higher the similarity between the transformer structured address information and the user structured address information. The coordinate distance information is used to measure the distance between the transformer coordinate information and the user coordinate information of the substation, for example, it can be the straight-line distance between the transformer in the substation and the user.

[0039] Optionally, similarity matching is performed based on the transformer structured address information and the user structured address information to determine address similarity data, including: determining a first data set based on the transformer structured address information, the first data set being an N-Gram set corresponding to the transformer structured address information; determining a second data set based on the user structured address information, the second data set being an N-Gram set corresponding to the user structured address information; determining the number of first words in the intersection of the first data set and the second data set, and the number of second words in the union of the first data set and the second data set, and determining the address similarity data based on the first word number and the second word number.

[0040] Specifically, the N-Gram algorithm is a data mining method based on text sequences. By slicing consecutive words or character fragments in the text, a series of continuous element combinations of length N are generated. These combinations are called N-Grams. Common N can take smaller values ​​such as 1, 2, and 3. Common N-Grams combinations include bigrams, trigrams, and quadruple tuples. The N-Gram set can be a set consisting of continuous element combinations determined by the N-Gram algorithm.

[0041] The first data set is an N-Gram set corresponding to the transformer structured address information, including continuous elements of length N in the transformer structured address information, for example, N can be 2. The second data set is an N-Gram set corresponding to the user structured address information, including continuous elements of length N in the user structured address information, for example, N can be 2. Optionally, N in the first data set and N in the second data set have the same value. The first word number is the number of continuous elements in the intersection of the first data set and the second data set, and the second word number is the number of continuous elements in the union of the first data set and the second data set. Optionally, the address similarity data is obtained by calculating the ratio of the first word number to the second word number. For example, the transformer structured address information is "A City", "B District", "C Road", "No. 123", and the user structured address information is "A City", "B District", "C Road", "No. 123", "Building B". When N is 2, the first data set includes "A City B District", "B District C Road", "No. 123 C Road", and the second data set includes "A City B District", "B District C Road", "No. 123 C Road", "Building B No. 123". The number of first words is 3, and the number of second words is 4. By analogy, the combination words when N is 3, 4, and 5 can be determined respectively, and the address similarity data can be determined based on the number of words of the combination words in the first data set and the second data set, that is, the number of first words and the number of second words.

[0042] Optionally, determining household change matching data based on address similarity data and / or coordinate distance information includes: performing weighted processing on the address similarity data and coordinate distance information to obtain household change matching data.

[0043] Specifically, the address similarity data is positively correlated with the household change matching data, that is, the larger the address similarity data, the larger the corresponding household change matching data. For example, the address similarity data can be directly used to determine the household change matching data. The coordinate distance information is negatively correlated with the address similarity data, that is, the larger the coordinate distance information, the smaller the corresponding address similarity data. For example, the reciprocal of the coordinate distance information can be used as the household change matching data. The address similarity data and the coordinate distance information can also be weighted. For example, the address similarity data and the reciprocal of the coordinate distance information can be weighted and summed to obtain the household change matching data. By weighting the address similarity data and the coordinate distance information, the accuracy of the household change matching data is improved.

[0044] S130: Determine an abnormality detection result in a subscriber transformer cutover scenario based on the first subscriber transformer matching data and the second subscriber transformer matching data.

[0045] In this embodiment, the abnormality detection result is to determine whether there is an abnormality in the household transformer cutover scenario in the substation area based on the first household transformer matching data and the second household transformer matching data. Due to reasons such as excessive transformer load, it is necessary to perform household transformer cutover on the users in the substation area to avoid failure due to excessive transformer load. The household transformer cutover scenario in the substation area includes power line cutover and record cutover of household transformer data. Power line cutover can be understood as completing the household transformer cutover on the power system line after the user in the substation area is cutover from one transformer to another transformer, and the record cutover of household transformer data can be understood as updating the correspondence between the transformer in the substation area and the user in the database. When there is a mismatch between the power line cutover and the record cutover of household transformer data, there is an abnormality in the household transformer cutover scenario in the substation area.

[0046] In this embodiment, in order to avoid the situation where the household transformer data may be inaccurate during the cutover process of the record of the household transformer data, anomaly detection can be performed by analyzing the household transformer matching data before and after the cutover of the area household transformer. It can be understood that before the area household transformer cutover occurs, since the correspondence between the transformer data and the user data remains unchanged, the household transformer matching data remains unchanged. In the area household transformer cutover scenario, the correspondence between the transformer data and the user data changes. Therefore, the first household transformer matching data and the second household transformer matching data before and after the area household transformer cutover are different. The difference between the first household transformer matching data and the second household transformer matching data is caused by the household transformer relationship that has changed in the area household transformer cutover scenario, that is, the more household transformer relationships that have changed, the greater the difference between the first household transformer matching data and the second household transformer matching data.

[0047] In this embodiment, whether there is an abnormality in the area household change cutover scenario is determined by the change data of the first household change matching data and the second household change matching data and the actual household change relationship change data. The above change data may be a change ratio or a change amount.

[0048] Optionally, the household change change ratio is determined based on the first household change matching data and the second household change matching data; and the abnormal detection result in the area household change cutover scenario is determined based on the area household change cutover ratio and the household change change ratio.

[0049] Specifically, the household transformer cutover ratio is the proportion of users who have changed in the actual household transformer cutover scenario. The household transformer change ratio is the proportion of users who have changed calculated based on the first household transformer matching data and the second household transformer matching data. By calculating the household transformer cutover ratio and the household transformer change ratio in the area, the theoretical value and actual value of the proportion of users who have changed can be determined.

[0050] Optionally, the abnormality detection results in the household transformer cutover scenario in the substation are determined based on the household transformer cutover ratio and the household transformer change ratio in the substation, including: when the household transformer cutover ratio and the household transformer change ratio in the substation are consistent, determining that there is no abnormality in the household transformer cutover scenario in the substation; when the household transformer cutover ratio and the household transformer change ratio in the substation are inconsistent, determining that there is an abnormality in the household transformer cutover scenario in the substation.

[0051] Specifically, when the household transformer cutover ratio and the household transformer change ratio are consistent, it means that the proportion of users who have changed in the actual household transformer cutover scenario in the area is consistent with the calculated proportion of users who have changed, and there is no abnormality in the household transformer cutover scenario in the area; when the household transformer cutover ratio and the household transformer change ratio are inconsistent, for example, the household transformer change ratio may be smaller than the household transformer cutover ratio, it means that a part of users should have changed but actually did not change, that is, there is an abnormality in the household transformer cutover scenario in the area.

[0052] Optionally, the abnormal detection results in the household transformer cutover scenario in the area are determined based on the household transformer cutover ratio and the household transformer change ratio in the area, including: determining the overall change trend of the first household transformer matching data and the second household transformer matching data; when the change trend of the matching data corresponding to any user is different from the overall change trend, determining the user as an abnormal user.

[0053] Specifically, the first household transformer matching data is based on the similarity obtained between the transformer structured address information and the user structured address information before the household transformer cutover in the substation area, and the second household transformer matching data is based on the similarity obtained between the transformer structured address information and the user structured address information after the household transformer cutover in the substation area. The overall change trend of the first household transformer matching data and the second household transformer matching data is decreasing, and the change trend of the matching data corresponding to the user is increasing, indicating that there is an abnormality with the user.

[0054] The technical solution of this embodiment can obtain accurate address similarity data based on transformer structured address information and user structured address information by obtaining transformer coordinate information and / or transformer structured address information and user coordinate information and / or user structured address information before the substation household transformer cutover and obtaining transformer coordinate information and / or transformer structured address information and user coordinate information and / or user structured address information after the substation household transformer cutover, obtain coordinate distance information based on the substation transformer coordinate information and user coordinate information, determine the first household transformer matching data or the second household transformer matching data based on the address similarity data and / or coordinate distance information, determine the abnormal detection result in the substation household transformer cutover scenario through the first household transformer matching data and the second household transformer matching data, and judge whether there is an abnormal situation in the substation household transformer cutover scenario, which is conducive to locating the abnormal user list of users in this substation, and adjust it after verification, saving manpower and material resources.

[0055] Embodiment 2

[0056] Figure 2 1 is a schematic diagram of the structure of an abnormality detection device in a transformer cutover scenario provided by Embodiment 2 of the present invention. Figure 2 As shown, the device comprises:

[0057] The substation data acquisition module 210 is used to acquire substation data before and after the substation household transformer cutover, wherein the substation data before the substation household transformer cutover includes transformer data and user data before the substation household transformer cutover, and the substation data after the substation household transformer cutover includes transformer data and user data after the substation household transformer cutover;

[0058] The household transformer matching data acquisition module 220 is used to determine the first household transformer matching data before the household transformer cutover and the second household transformer matching data after the household transformer cutover respectively based on the station area data before and after the household transformer cutover;

[0059] The anomaly detection module 230 is used to determine an anomaly detection result in a station area household transformer cutover scenario based on the first household transformer matching data and the second household transformer matching data.

[0060] On the basis of the above embodiment, optionally, the substation data acquisition module 210 is further used to obtain the coordinate information of the substation transformer before and after the substation household transformer cutover, call the electronic map interface, determine the substation address information corresponding to the coordinate information of the substation transformer, and convert the address information into transformer structured address information; and / or,

[0061] The user address information before and after the cutover of the transformer in the substation area is obtained, and the user address information is converted into user structured address information, and the electronic map interface is called to determine the user coordinate information corresponding to the user structured address information.

[0062] Optionally, the household change matching data acquisition module 220 is also used to perform similarity matching based on the transformer structured address information and the user structured address information to determine the address similarity data; perform distance calculation based on the substation transformer coordinate information and the user coordinate information to obtain coordinate distance information; determine the household change matching data based on the address similarity data and / or coordinate distance information, and the household change matching data is the first household change matching data or the second household change matching data.

[0063] Optionally, the user-change matching data acquisition module 220 is also used to determine a first data set based on the transformer structured address information, the first data set being an N-Gram set corresponding to the transformer structured address information; determine a second data set based on the user structured address information, the second data set being an N-Gram set corresponding to the user structured address information; determine the number of first words in the intersection of the first data set and the second data set, and the number of second words in the union of the first data set and the second data set, and determine address similarity data based on the number of first words and the number of second words.

[0064] Optionally, the household change matching data acquisition module 220 is further used to perform weighted processing on the address similarity data and the coordinate distance information to obtain household change matching data.

[0065] Optionally, the anomaly detection module 230 is further used to determine the household change change ratio based on the first household change matching data and the second household change matching data; and determine the anomaly detection result in the area household change cutover scenario based on the area household change cutover ratio and the household change change ratio.

[0066] Optionally, the anomaly detection module 230 is also used to determine that there is no anomaly in the household transformer cutover scenario in the substation when the household transformer cutover ratio and the household transformer change ratio are consistent; and to determine that there is an anomaly in the household transformer cutover scenario in the substation when the household transformer cutover ratio and the household transformer change ratio are inconsistent.

[0067] Optionally, the anomaly detection module 230 is further used to determine the overall change trend of the first household change matching data and the second household change matching data; when the change trend of the matching data corresponding to any user is different from the overall change trend, the user is determined as an abnormal user.

[0068] The anomaly detection device in the substation household transformer cutover scenario provided by the embodiment of the present invention can execute the anomaly detection method in the substation household transformer cutover scenario provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0069] Embodiment 3

[0070] Figure 3It is a structural schematic diagram of an electronic device for implementing the anomaly detection method in the scenario of the area household change cutover of an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0071] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0072] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0073] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the XX method.

[0074] In some embodiments, the anomaly detection method in the scenario of the transformer-to-user switch cutover in the area may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the anomaly detection method in the scenario of the transformer-to-user switch cutover described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the anomaly detection method in the scenario of the transformer-to-user switch cutover in the area by any other appropriate means (e.g., by means of firmware).

[0075] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0076] The computer program for implementing the anomaly detection method in the scenario of the transformer switching of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or completely on a remote machine or server.

[0077] Embodiment 4

[0078] Embodiment 4 of the present invention further provides a computer-readable storage medium, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute an anomaly detection method in a transformer cutover scenario, the method comprising:

[0079] Obtaining the data of the substation area before and after the substation area household transformer cutover, wherein the substation area data before the substation area household transformer cutover includes the transformer data and user data before the substation area household transformer cutover, and the substation area data after the substation area household transformer cutover includes the transformer data and user data after the substation area household transformer cutover;

[0080] Determine first household transformer matching data before the household transformer cutover in the area and second household transformer matching data after the household transformer cutover in the area based on the area data before and after the household transformer cutover in the area;

[0081] An abnormality detection result in a household transformer cutover scenario in a substation is determined based on the first household transformer matching data and the second household transformer matching data.

[0082] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0083] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0084] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0085] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0086] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0087] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting anomalies in a cutover scenario of a transformer substation, characterized in that: include: Acquire the data of the substation area before and after the cutover of the substation area household transformer, wherein the substation area data before the cutover of the substation area household transformer includes the transformer data and user data before the cutover of the substation area household transformer, and the substation area data after the cutover of the substation area household transformer includes the transformer data and user data after the cutover of the substation area household transformer; Determine first household transformer matching data before the household transformer cutover in the area and second household transformer matching data after the household transformer cutover in the area based on the area data before and after the household transformer cutover in the area; An abnormality detection result in a subscriber transformation cutover scenario is determined based on the first subscriber transformation matching data and the second subscriber transformation matching data.

2. The method according to claim 1, characterized in that The transformer data includes transformer coordinate information and / or transformer structured address information; the user data includes user coordinate information and / or user structured address information; Among them, the data of the area before and after the cutover of the area household transformer is obtained, including: Obtaining the coordinate information of the transformer in the area before and after the cutover of the area transformer, calling the electronic map interface, determining the area address information corresponding to the coordinate information of the transformer in the area, and converting the address information into transformer structured address information; and / or, The user address information before and after the cutover of the transformer is obtained, and the user address information is converted into the user structured address information, and the electronic map interface is called to determine the user coordinate information corresponding to the user structured address information.

3. The method according to claim 1, characterized in that The method of determining first household transformer matching data before the household transformer cutover and second household transformer matching data after the household transformer cutover based on the station area data before and after the household transformer cutover comprises: For the station area data before the station area household transformer cutover or the station area data after the station area household transformer cutover, the following steps are performed respectively to obtain the first household transformer matching data or the second household transformer matching data: Performing similarity matching based on the transformer structured address information and the user structured address information to determine address similarity data; Perform distance calculation based on the transformer coordinate information and the user coordinate information to obtain coordinate distance information; The household change matching data is determined based on the address similarity data and / or the coordinate distance information, and the household change matching data is the first household change matching data or the second household change matching data.

4. The method according to claim 3, characterized in that The performing similarity matching based on the transformer structured address information and the user structured address information to determine address similarity data includes: Determine a first data set based on the transformer structured address information, where the first data set is an N-Gram set corresponding to the transformer structured address information; Determine a second data set based on the user structured address information, wherein the second data set includes an N-Gram set corresponding to the user structured address information; Determine the number of first words in the intersection of the first data set and the second data set, and the number of second words in the union of the first data set and the second data set, and determine the address similarity data based on the first number of words and the second number of words.

5. The method according to claim 3, characterized in that: The determining of household change matching data based on the address similarity data and / or coordinate distance information includes: The address similarity data and the coordinate distance information are weighted to obtain the household change matching data.

6. The method according to claim 1, characterized in that The determining, based on the first household change matching data and the second household change matching data, an abnormality detection result in a station area household change cutover scenario includes: determining a household change ratio based on the first household change matching data and the second household change matching data; The abnormality detection result in the household transformer cutover scenario in the transformer area is determined based on the household transformer cutover ratio in the transformer area and the household transformer change ratio.

7. The method according to claim 6, characterized in that The determining of the abnormal detection result in the scenario of household transformer cutover in the transformer area based on the household transformer cutover ratio in the transformer area and the household transformer change ratio includes: When the cutover ratio of household transformers in the transformer area is consistent with the change ratio of household transformers, determining that there is no abnormality in the cutover scenario of household transformers in the transformer area; When the household transformer cutover ratio in the substation area is inconsistent with the household transformer change ratio, it is determined that an abnormality exists in the household transformer cutover scenario in the substation area.

8. A method and device for detecting anomalies in a cutover scenario of a transformer substation, characterized in that: include: The substation data acquisition module is used to acquire the substation data before and after the substation household transformer cutover, wherein the substation data before the substation household transformer cutover includes the transformer data and user data before the substation household transformer cutover, and the substation data after the substation household transformer cutover includes the transformer data and user data after the substation household transformer cutover; A household transformer matching data acquisition module, used to determine first household transformer matching data before the household transformer cutover in the station area and second household transformer matching data after the household transformer cutover in the station area based on the station area data before and after the household transformer cutover in the station area; The anomaly detection module is used to determine an anomaly detection result in a station area household transformer cutover scenario based on the first household transformer matching data and the second household transformer matching data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the anomaly detection method in the substation user-to-user switchover scenario described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the anomaly detection method in a transformer switching scenario according to any one of claims 1 to 7 when executed.

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