An abnormal data display method, device, equipment, medium and product

Abstract

This invention discloses an anomaly data display method, apparatus, device, medium, and product. The method includes: acquiring multiple power grid data tables; splicing portions of the multiple power grid data tables based on multiple splicing rules to obtain multiple target tables; extracting anomaly data from each target table according to anomaly detection rules corresponding to each target table; generating an anomaly data display interface based on the anomaly data in each target table; displaying the anomaly data display interface in a first area of ​​the main interface, and adding the identifier information of the anomaly data display interface to the menu list of the main interface. Through the technical solution of this invention, anomaly data can be displayed, allowing inspectors to directly verify the anomaly data, significantly reducing the inspection time and improving the accuracy of inspections.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, apparatus, device, medium and product for displaying abnormal data. Background Technology

[0002] Marketing inspection is an important supervisory link and guarantee for electricity marketing work. It undertakes the important functions of supervising the quality of marketing business work throughout the process, handling complaints and reports, investigating and punishing illegal electricity use, cracking down on electricity theft, and rectifying the order of the electricity market.

[0003] In existing marketing systems, marketing auditors must manually review samples and extract abnormal data. This current method of marketing auditing requires significant manpower and suffers from low automation, low accuracy, and low efficiency. Summary of the Invention

[0004] This invention provides an abnormal data display method, apparatus, equipment, medium, and product that can display abnormal data, allowing inspectors to directly verify the abnormal data, significantly reducing the inspection time and improving the accuracy of inspections.

[0005] According to one aspect of the present invention, an abnormal data display method is provided, comprising:

[0006] Retrieve multiple power grid data tables;

[0007] Based on multiple splicing rules, some power grid data tables from the multiple power grid data tables are spliced ​​together to obtain multiple target tables;

[0008] Based on the anomaly detection rules corresponding to each target table, extract the abnormal data from each target table;

[0009] Based on the abnormal data in each of the target tables, an abnormal data display interface is generated;

[0010] The abnormal data display interface is displayed in the first area of ​​the main interface, and the identification information of the abnormal data display interface is added to the menu list of the main interface.

[0011] According to another aspect of the present invention, an anomaly data display device is provided, the anomaly data display device comprising:

[0012] The data table acquisition module is used to acquire multiple power grid data tables.

[0013] The target table determination module is used to concatenate a portion of the power grid data tables from the multiple power grid data tables based on multiple concatenation rules to obtain multiple target tables;

[0014] The abnormal data extraction module is used to extract abnormal data from each target table according to the abnormal detection rules corresponding to each target table.

[0015] An abnormal data display interface generation module is used to generate an abnormal data display interface based on the abnormal data in each of the target tables;

[0016] The display module is used to display the abnormal data display interface in the first area of ​​the main interface and add the identification information of the abnormal data display interface to the menu list of the main interface.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the abnormal data display method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the abnormal data display method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is provided, which, when executed by a processor, implements the abnormal data display method as described in any of the embodiments of the present invention.

[0023] This invention first concatenates a portion of the power grid data tables from multiple power grid data tables based on multiple concatenation rules to obtain multiple target tables; then, according to the anomaly detection rules corresponding to each target table, it extracts abnormal data from each target table; based on the abnormal data in each target table, it generates an anomaly data display interface; the anomaly data display interface is displayed in a first area of ​​the main interface, and the identification information of the anomaly data display interface is added to the menu list of the main interface, enabling the display of abnormal data. Inspectors can directly verify the abnormal data, significantly reducing the verification time for inspectors and improving the accuracy of inspections.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of an abnormal data display method according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of the structure of an abnormal data display device according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0032] Example 1

[0033] Figure 1This is a flowchart illustrating an abnormal data display method provided in an embodiment of the present invention. This embodiment is applicable to situations involving abnormal data display. The method can be executed by the abnormal data display device in this embodiment, which can be implemented in software and / or hardware, such as... Figure 1 As shown, the method specifically includes the following steps:

[0034] S110, retrieve multiple power grid data tables.

[0035] In this embodiment, the power grid data table can be a power grid marketing data table. The multiple power grid data tables may include: metering point table, electricity user table, user meter field relationship table, system organization table, electricity meter asset table, operating electricity meter, operating electricity meter power consumption table, photovoltaic user association information table, business expansion work order basic information table, electricity meter installation and removal record table, user meter field relationship table, system organization table, voltage measurement point daily power consumption data table, meter reading information table, and photovoltaic user file information table, etc.

[0036] S120, based on multiple splicing rules, splice some of the power grid data tables in the multiple power grid data tables to obtain multiple target tables.

[0037] In this embodiment, the splicing rules include at least one of the following: splicing power grid data tables whose table names include any keyword from the first set of keywords, splicing power grid data tables whose table names include any keyword from the second set of keywords, and splicing power grid data tables whose table names include any keyword from the third set of keywords.

[0038] In this embodiment, the method of concatenating partial power grid data tables from multiple power grid data tables to obtain multiple target tables based on multiple concatenation rules can be as follows: concatenating power grid data tables whose table names include any keyword from the first keyword set to obtain a first target table; concatenating power grid data tables whose table names include any keyword from the second keyword set to obtain a second target table; and concatenating power grid data tables whose table names include any keyword from the third keyword set to obtain a third target table. Alternatively, the method of concatenating partial power grid data tables from multiple power grid data tables to obtain multiple target tables can be as follows: concatenating partial power grid data tables from multiple power grid data tables using a join query statement to obtain multiple target tables.

[0039] Optionally, based on multiple splicing rules, some power grid data tables from the multiple power grid data tables are spliced ​​to obtain multiple target tables, including:

[0040] The first target table is obtained by concatenating the power grid data tables whose table names include any keyword from the first keyword set.

[0041] In this embodiment, the first set of keywords includes at least one of the following: metering point, electricity user, meter field relationship, system organization, electricity meter asset, operating electricity meter, operating electricity meter power, and photovoltaic user association information.

[0042] In this embodiment, the method for concatenating power grid data tables whose table names include any keyword from the first keyword set to obtain the first target table can be as follows: The first concatenation rule is used to concatenate power grid data tables whose table names include any keyword from the first keyword set. The first concatenation rule includes: concatenating the metering point table and the electricity user table using user IDs; concatenating the user field relationship table using metering point IDs; concatenating the system organization table using power supply organization IDs; concatenating the electricity consumption of operating electricity meters using energy identifiers and time conditions; and concatenating the photovoltaic user association information table using user IDs and meter asset IDs. It should be noted that the electricity meter asset table and the operating electricity meter table use subqueries and window functions to select the latest electricity meter asset information. This latest selected electricity meter asset information is then concatenated with information from other tables.

[0043] In this embodiment, the metering point table and the electricity user table are concatenated using user IDs. That is, rows in the metering point table that have the same user ID as the electricity user table are concatenated to obtain the concatenated table. Other concatenation rules follow the same principle and will not be elaborated here.

[0044] By concatenating power grid data tables whose table names include any keyword from the second keyword set, a second target table is obtained.

[0045] In this embodiment, the second set of keywords includes at least one of the following: basic information of business expansion work orders, electricity meter installation and removal records, relationships between user meter fields, system organization, daily electricity consumption data of voltage measurement points, and meter reading information.

[0046] In this embodiment, the method for concatenating power grid data tables whose table names include any keyword from the second keyword set to obtain the second target table can be as follows: Based on the second concatenation rules, power grid data tables whose table names include any keyword from the second keyword set are concatenated to obtain the second target table. The second concatenation rules include: concatenating the business expansion work order basic information table and the electricity meter installation and removal record table using the work order number; concatenating the user field relationship table using the asset number; concatenating the system organization table using the power supply bureau code; and concatenating the voltage measurement point daily electricity data table using a subquery. It should be noted that in this embodiment, multiple voltage measurement point daily electricity data tables are merged using subqueries, and window functions are used to select the earliest data time for each meter.

[0047] By concatenating power grid data tables whose table names include any keyword from the third keyword set, a third target table is obtained, wherein some keywords from the first keyword set, the second keyword set, and the third keyword set are the same.

[0048] In this embodiment, the third set of keywords includes at least one of the following: photovoltaic user profile information, system organization, photovoltaic user association information, and power plant meter reading information.

[0049] In this embodiment, the method for concatenating power grid data tables whose table names include any keyword from the third keyword set to obtain the third target table can be as follows: Based on the third concatenation rules, power grid data tables whose table names include any keyword from the third keyword set are concatenated to obtain the third target table. The third concatenation rules include: concatenating the photovoltaic user file information table and the system organization table using the power supply organization number; concatenating the photovoltaic user file information table and the photovoltaic user association information table using the user number; and concatenating multiple power plant meter reading information tables using subqueries.

[0050] S130, Extract abnormal data from each target table according to the anomaly detection rules corresponding to each target table.

[0051] In this embodiment, the anomaly detection rules corresponding to the first target table include: if the total reverse active power is greater than a first value, the electricity user's corresponding electricity purchaser is empty, and the meter identifier's corresponding electricity purchaser is not empty, then the data is determined to be abnormal; if the total reverse active power is greater than a second value, the electricity user's corresponding electricity purchaser is empty, and the total reverse active power is greater than the total forward active power, then the data is determined to be abnormal. The anomaly detection rules corresponding to the second target table include: if the earliest data time of the metering system is earlier than the connection and power supply time, and the earliest forward active power meter code is greater than a third value, then the data is determined to be abnormal; if the monthly power generation of the photovoltaic power generation household is less than the grid-connected power generation, then the data is determined to be abnormal; if the annual power generation is greater than the annual preset power generation, then the data is determined to be abnormal.

[0052] In this embodiment, the method for extracting abnormal data from each target table according to the anomaly detection rules corresponding to each target table can be as follows: extract data from each target table according to the key field set corresponding to each target table to obtain a partial table of each target table; perform anomaly detection on the data in the partial table of each target table according to the anomaly detection rules corresponding to each target table; and extract the abnormal data from the partial table of each target table.

[0053] Optionally, based on the anomaly detection rules corresponding to each target table, extract the abnormal data from each target table, including:

[0054] Data from each target table is extracted based on the set of key fields corresponding to each target table, resulting in a partial table for each target table.

[0055] In this embodiment, the key field set corresponding to the first target table includes: total positive active power, total negative active power, electricity purchaser corresponding to the electricity user, and electricity purchaser corresponding to the meter identifier. The key field set corresponding to the second target table includes: the earliest data time of the metering system, the connection and power supply time, and the total positive active power meter code. The key field set corresponding to the third target table includes: monthly power generation of photovoltaic power generation households, grid-connected power generation, annual power generation, and contracted capacity.

[0056] Based on the anomaly detection rules corresponding to each target table, anomaly detection is performed on the data in some tables of each target table.

[0057] In this embodiment, based on the anomaly detection rules corresponding to each target table, anomaly detection is performed on the data in a portion of each target table. This includes: if the total reverse active power is greater than a first value, the electricity user's corresponding electricity purchaser is empty, and the meter identifier's corresponding electricity purchaser is not empty, then the data is determined to be abnormal; if the total reverse active power is greater than a second value, the electricity user's corresponding electricity purchaser is empty, and the total reverse active power is greater than the total forward active power, then the data is determined to be abnormal. The anomaly detection rules corresponding to the second target table include: if the earliest data time of the metering system is earlier than the connection and power supply time, and the earliest forward active power meter code is greater than a third value, then the data is determined to be abnormal; if the monthly power generation of the photovoltaic power generation household is less than the grid-connected power generation, then the data is determined to be abnormal; if the annual power generation is greater than the annual preset power generation, then the data is determined to be abnormal.

[0058] Extract abnormal data from a portion of each target table.

[0059] In this embodiment, the method for extracting abnormal data from a portion of each target table can be: obtaining data carrying abnormal markers from each target table, wherein the abnormal markers include any one of a first marker, a second marker, and a third marker.

[0060] Optionally, based on the anomaly detection rules corresponding to the first target table, anomaly detection is performed on data in a portion of the first target table, including:

[0061] If the total reverse active power is greater than the first value, the electricity user's corresponding electricity purchaser is empty, and the meter identifier's corresponding electricity purchaser is not empty, then the data is determined to be abnormal, and the first mark is added.

[0062] In this embodiment, the first marker is: reverse electricity is not zero, and no electricity purchaser is associated.

[0063] In this embodiment, the first value is a preset value, for example, the first value can be 0.

[0064] If the total reverse active power is greater than the second value, the electricity user's corresponding electricity purchaser is empty, and the total reverse active power is greater than the total forward active power, then the data is determined to be abnormal, and a second mark is added.

[0065] In this embodiment, the second marker is: the reverse charge is not zero, the energy meter is faulty, or the energy meter is incorrectly wired.

[0066] In this embodiment, the first value and the second value are different. Both the first value and the second value are preset values. For example, the second value can be 10.

[0067] Optionally, based on the anomaly detection rules corresponding to the second target table, anomaly detection is performed on data in a portion of the second target table, including:

[0068] If the earliest data time in the metering system is earlier than the power connection time, and the earliest positive active power meter value is greater than the third value, then the data is determined to be abnormal, and a third mark is added.

[0069] In this embodiment, the third marker is: the actual power consumption time is earlier than the connection and power supply time, the earliest positive active power meter code is greater than 1, and the time logic is abnormal.

[0070] In this embodiment, the time logic anomaly can be an external loop.

[0071] In this embodiment, the first value, the second value, and the third value are different. The first value, the second value, and the third value are all preset values. For example, the third value can be 1.

[0072] Optionally, based on the anomaly detection rules corresponding to the third target table, anomaly detection is performed on data in a portion of the third target table, including:

[0073] If the monthly power generation of a photovoltaic power generation household is less than the power generated on the grid, the data is identified as abnormal and a fourth flag is added.

[0074] In this embodiment, the fourth marker is: the monthly power generation of a photovoltaic power generation household is less than the power generated on the grid, indicating an abnormal capacity.

[0075] The annual preset electricity volume is determined based on the contracted capacity.

[0076] In this embodiment, the capacity anomaly can be caused by unauthorized capacity increases.

[0077] In this embodiment, the annual preset electricity consumption can be determined based on the contracted capacity using the following formula:

[0078] Annual preset electricity consumption = Contract capacity × 12 × 365.

[0079] If the annual power generation exceeds the annual preset power generation, the data is determined to be abnormal, and a fifth marker is added. The fifth marker is: annual power generation exceeds the annual preset power generation, indicating a capacity abnormality.

[0080] S140, Based on the abnormal data in each target table, generate an abnormal data display interface.

[0081] S150, the abnormal data display interface is displayed in the first area of ​​the main interface, and the identification information of the abnormal data display interface is added to the menu list of the main interface.

[0082] In this embodiment of the invention, the Alpine Data tool is used to obtain the required power grid data table, big data analysis is performed according to the pre-determined filtering rules to obtain the abnormal data table, then the cloud reporting tool is used to convert the abnormal data table into a cloud report, then the dashboard tool is used to create the rule dashboard, and finally the rule dashboard is added to the main dashboard for display.

[0083] In a specific example, the abnormal data display process includes:

[0084] The first step is to use the dashboard tool to create the main dashboard for the marketing audit and monitoring application, which is the main interface. The main interface includes a title text, a list menu, and a menu slideshow area.

[0085] The second step is to use SQL programming in the Alpine Data tool to obtain the required data table, and perform operations such as querying, comparing, aggregating, and grouping according to the pre-determined exception rules to obtain the exception data table.

[0086] 1. Rule-based metering management - Reverse electricity consumption by public transformer users is not zero. Use the CREATE TABLE...AS SELECT statement to select and combine data from multiple tables to create a new table.

[0087] Extract the following fields from each table: power supply bureau, power supply station, user number, user name, metering point number, meter identifier, electricity usage time, electricity user corresponding to electricity purchaser, electricity meter identifier corresponding to electricity purchaser, total positive active power, and total negative active power.

[0088] Use the CASE statement for anomaly detection: If the total reverse active power is greater than 0, the electricity user's corresponding electricity purchaser is empty, and the meter identifier's corresponding electricity purchaser is not empty, then the data is determined to be abnormal and marked as "reverse power is not zero, suspected of not being associated with an electricity purchaser".

[0089] If the total reverse active power is greater than 10, the corresponding electricity purchaser for the electricity user is empty, and the total reverse active power is greater than the total forward active power, then the data is determined to be abnormal and marked as "reverse power is not zero, suspected electricity meter fault / incorrect wiring".

[0090] 2. For business expansion applications, the actual electricity usage time must be earlier than the connection time. Use the `CREATE TABLE...AS SELECT` statement to select and combine data from multiple tables to create a new table.

[0091] Extract fields from each table, including power supply bureau, power supply station, work order number, acceptance time, user number, connection and power supply time, meter identifier, earliest data time of the metering system (from the daily electricity data table of voltage measurement points), earliest positive active power total meter code (from the daily electricity data table of voltage measurement points), and earliest reverse active power total meter code (from the daily electricity data table of voltage measurement points).

[0092] Use the CASE statement for anomaly detection: If the earliest data time in the metering system is earlier than the power connection time, and the earliest positive active power meter code is greater than 1, then the data is considered abnormal. Add a flag: "Actual power consumption time is earlier than power connection time, earliest positive active power meter code is greater than 1, suspected off-line circulation." It should be noted that off-line circulation refers to the behavior of not following the business process, such as initiating an order in the system only after the meter has been installed on-site. This is an abnormal process; the normal process is to initiate the order first and then install the meter.

[0093] 3. Distributed photovoltaic power generation rule - unauthorized capacity expansion by photovoltaic users. Use the CREATE TABLE...AS SELECT statement to select and combine data from multiple tables to create a new table.

[0094] Extract the following fields from multiple tables: power supply bureau, power supply station, photovoltaic power generation user ID, photovoltaic project name, project address, contract capacity, installed capacity, intended consumption method, electricity consumption time (from the sub-query power plant meter reading information table), photovoltaic power generation, power generation in the past 12 months, electricity purchaser user ID, on-grid electricity, and contract capacity.

[0095] Use the CASE statement for anomaly detection: If the monthly power generation of a photovoltaic power generation household is less than the power generation connected to the grid, mark it as "The monthly power generation of a photovoltaic power generation household is less than the power generation connected to the grid, suspected of unauthorized capacity expansion"; if the annual power generation is greater than the maximum theoretical power (contract capacity × 12 × 365), mark it as "The annual power generation is greater than the annual preset power, suspected of unauthorized capacity expansion".

[0096] An anomaly data table is generated based on the above-mentioned anomaly data.

[0097] The third step is to use a cloud reporting tool to convert the abnormal data table obtained in step two into an abnormal data cloud report. The abnormal data cloud report includes: adding field names, field data, filters, and an export button.

[0098] Step 4: Use the dashboard tool to convert the abnormal data cloud report from Step 3 into an abnormal data dashboard (i.e., an abnormal data display interface). The abnormal data dashboard includes: a report display area and a filter display area.

[0099] Step 5: Add the abnormal data dashboard generated in step 4 to the menu slide area of ​​the main dashboard in step 1, add the abnormal rule name in the LIST menu, and associate the abnormal rule name with the abnormal data dashboard.

[0100] The technical solution of this embodiment first concatenates a portion of the power grid data tables from multiple power grid data tables based on multiple concatenation rules to obtain multiple target tables; then, according to the anomaly detection rules corresponding to each target table, it extracts abnormal data from each target table; based on the abnormal data in each target table, it generates an anomaly data display interface; the anomaly data display interface is displayed in the first area of ​​the main interface, and the identification information of the anomaly data display interface is added to the menu list of the main interface, which enables the display of abnormal data, allowing inspectors to directly verify the abnormal data, significantly reducing the verification time of inspectors and improving the accuracy of inspections.

[0101] Example 2

[0102] Figure 2 This is a schematic diagram of an anomaly data display device provided in an embodiment of the present invention. This embodiment is applicable to anomaly data display scenarios. The device can be implemented using software and / or hardware methods and can be integrated into any device that provides anomaly data display functionality, such as… Figure 2 As shown, the abnormal data display device specifically includes: a data table acquisition module 210, a target table determination module 220, an abnormal data extraction module 230, an abnormal data display interface generation module 240, and a display module 250.

[0103] The data table acquisition module is used to acquire multiple power grid data tables.

[0104] The target table determination module is used to concatenate a portion of the power grid data tables from the multiple power grid data tables based on multiple concatenation rules to obtain multiple target tables;

[0105] The abnormal data extraction module is used to extract abnormal data from each target table according to the abnormal detection rules corresponding to each target table.

[0106] An abnormal data display interface generation module is used to generate an abnormal data display interface based on the abnormal data in each of the target tables;

[0107] The display module is used to display the abnormal data display interface in the first area of ​​the main interface and add the identification information of the abnormal data display interface to the menu list of the main interface.

[0108] The above-described products can perform the methods provided in any embodiment of the present invention, and have the corresponding functional modules and beneficial effects for performing the methods.

[0109] The technical solution of this embodiment first concatenates a portion of the power grid data tables from multiple power grid data tables based on multiple concatenation rules to obtain multiple target tables; then, according to the anomaly detection rules corresponding to each target table, it extracts abnormal data from each target table; based on the abnormal data in each target table, it generates an anomaly data display interface; the anomaly data display interface is displayed in the first area of ​​the main interface, and the identification information of the anomaly data display interface is added to the menu list of the main interface, which enables the display of abnormal data, allowing inspectors to directly verify the abnormal data, significantly reducing the verification time of inspectors and improving the accuracy of inspections.

[0110] Example 3

[0111] Figure 3 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0112] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0113] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0114] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the abnormal data display method.

[0115] In some embodiments, the anomaly data display method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the anomaly data display method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the anomaly data display method by any other suitable means (e.g., by means of firmware).

[0116] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0117] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0119] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0120] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0121] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0122] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0123] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the abnormal data display method according to any embodiment of the invention.

[0124] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for displaying abnormal data, characterized in that, include: Retrieve multiple power grid data tables; Based on multiple splicing rules, some power grid data tables from the multiple power grid data tables are spliced ​​together to obtain multiple target tables; Based on the anomaly detection rules corresponding to each target table, extract the abnormal data from each target table; Based on the abnormal data in each of the target tables, an abnormal data display interface is generated; The abnormal data display interface is displayed in the first area of ​​the main interface, and the identification information of the abnormal data display interface is added to the menu list of the main interface; Among them, based on multiple splicing rules, some power grid data tables from the multiple power grid data tables are spliced ​​to obtain multiple target tables, including: By concatenating power grid data tables whose table names include any keyword from the first keyword set, the first target table is obtained; By concatenating power grid data tables whose table names include any keyword from the second keyword set, a second target table is obtained. The power grid data tables whose table names include any keyword from the third keyword set are concatenated to obtain the third target table, wherein some keywords in the first keyword set, the second keyword set, and the third keyword set are the same. Specifically, based on the anomaly detection rules corresponding to each target table, abnormal data is extracted from each target table, including: Data from each target table is extracted based on the key field set corresponding to each target table, resulting in partial tables for each target table. The key field set corresponding to the first target table includes: total positive active power, total negative active power, electricity purchaser corresponding to the electricity user, and electricity purchaser corresponding to the meter identifier. The key field set corresponding to the second target table includes: the earliest data time of the metering system, the connection and power supply time, and the total positive active power meter code. The key field set corresponding to the third target table includes: monthly power generation of photovoltaic power generation users, grid-connected power generation, annual power generation, and contracted capacity. Based on the anomaly detection rules corresponding to each target table, anomaly detection is performed on the data in some tables of each target table; Extract abnormal data from a portion of each target table; wherein, the method for extracting abnormal data from a portion of each target table is: obtaining data carrying abnormal markers in each target table, wherein the abnormal markers include any one of a first marker, a second marker, and a third marker.

2. The method according to claim 1, characterized in that, Based on the anomaly detection rules corresponding to the first target table, anomaly detection is performed on data in a portion of the first target table, including: If the total reverse active power is greater than the first value, the electricity user's corresponding electricity purchaser is empty, and the electricity meter identifier's corresponding electricity purchaser is not empty, then the data is determined to be abnormal, and a first mark is added. The first mark is: the reverse power is not zero and no electricity purchaser is associated. If the total reverse active power is greater than the second value, the corresponding electricity purchaser for the electricity user is empty, and the total reverse active power is greater than the total forward active power, then the data is determined to be abnormal, and a second mark is added. The second mark indicates that the reverse power is not zero, the electricity meter is faulty, or the electricity meter is incorrectly wired.

3. The method according to claim 1, characterized in that, Based on the anomaly detection rules corresponding to the second target table, anomaly detection is performed on data in a portion of the second target table, including: If the earliest data time in the metering system is earlier than the connection and power supply time, and the earliest positive active power meter code is greater than the third value, then the data is determined to be abnormal, and a third mark is added. The third mark is: the actual power consumption time is earlier than the connection and power supply time, the earliest positive active power meter code is greater than 1, and the time logic is abnormal.

4. The method according to claim 1, characterized in that, Based on the anomaly detection rules corresponding to the third target table, anomaly detection is performed on data in a portion of the third target table, including: If the monthly power generation of a photovoltaic power generation household is less than the power generation connected to the grid, the data is identified as abnormal and a fourth flag is added. The fourth flag indicates that the monthly power generation of a photovoltaic power generation household is less than the power generation connected to the grid, indicating a capacity abnormality. The annual preset electricity volume is determined based on the contracted capacity; If the annual power generation exceeds the annual preset power generation, the data is determined to be abnormal, and a fifth marker is added. The fifth marker is: annual power generation exceeds the annual preset power generation, indicating a capacity abnormality.

5. An anomaly data display device, characterized in that, include: The data table acquisition module is used to acquire multiple power grid data tables. The target table determination module is used to concatenate a portion of the power grid data tables from the multiple power grid data tables based on multiple concatenation rules to obtain multiple target tables; The abnormal data extraction module is used to extract abnormal data from each target table according to the abnormal detection rules corresponding to each target table. An abnormal data display interface generation module is used to generate an abnormal data display interface based on the abnormal data in each of the target tables; The display module is used to display the abnormal data display interface in the first area of ​​the main interface, and to add the identification information of the abnormal data display interface to the menu list of the main interface; Among them, based on multiple splicing rules, some power grid data tables from the multiple power grid data tables are spliced ​​to obtain multiple target tables, including: By concatenating power grid data tables whose table names include any keyword from the first keyword set, the first target table is obtained; By concatenating power grid data tables whose table names include any keyword from the second keyword set, a second target table is obtained. The power grid data tables whose table names include any keyword from the third keyword set are concatenated to obtain the third target table, wherein some keywords in the first keyword set, the second keyword set, and the third keyword set are the same. Specifically, based on the anomaly detection rules corresponding to each target table, abnormal data is extracted from each target table, including: Data from each target table is extracted based on the key field set corresponding to each target table, resulting in partial tables for each target table. The key field set corresponding to the first target table includes: total positive active power, total negative active power, electricity purchaser corresponding to the electricity user, and electricity purchaser corresponding to the meter identifier. The key field set corresponding to the second target table includes: the earliest data time of the metering system, the connection and power supply time, and the total positive active power meter code. The key field set corresponding to the third target table includes: monthly power generation of photovoltaic power generation users, grid-connected power generation, annual power generation, and contracted capacity. Based on the anomaly detection rules corresponding to each target table, anomaly detection is performed on the data in some tables of each target table; Extract abnormal data from a portion of each target table; wherein, the method for extracting abnormal data from a portion of each target table is: obtaining data carrying abnormal markers in each target table, wherein the abnormal markers include any one of a first marker, a second marker, and a third marker.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the abnormal data display method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the abnormal data display method according to any one of claims 1-4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the abnormal data display method according to any one of claims 1-4.

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