Remote acquisition and processing method for power utilization data of power grid

Abstract

The invention, which relates to the technical field of data processing, discloses a remote power grid power consumption data acquisition and processing method comprising the following steps: acquiring original power grid power consumption data of target power consumption equipment; determining the abnormal original power grid power consumption data as target power grid power consumption data; searching a historical power grid power utilization sequence of the target power utilization equipment from a preset power utilization database; determining the electric equipment corresponding to the power grid power utilization sequence with the maximum similarity with the historical power grid power utilization sequence change curve as approximate electric equipment; determining estimated power utilization data of the power grid according to the historical power utilization sequence of the power grid and the power utilization data of the power grid of the approximate power utilization equipment; and replacing the target power grid power consumption data with the estimated power grid power consumption data. According to the method, the target power grid power consumption data is filled with the estimated power grid power consumption data, so that a vacancy value at the abnormal power grid power consumption data in the power consumption information acquisition system is avoided, and the integrity of the power grid power consumption data in the power consumption information acquisition system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for remotely collecting and processing power consumption data of a power grid. Background Art

[0002] The power grid, also known as the knowledge-based grid or modern grid, is a new type of grid formed by the integration of advanced sensing and measurement technologies, information and communication technologies, and control technologies with existing transmission and distribution infrastructure. The open nature of the power distribution network has led to the widespread use and access of a large number of power collection terminals and mobile devices. This has posed new challenges to the integrity, confidentiality, anti-attack, and privacy protection of data transmission within the power distribution network.

[0003] The electricity consumption information collection system consists of a collection master station, collection equipment, electricity meters and the communication between them. The collection master station is the management and control center of the collection system. Its main function is to manage data transmission, data processing, data application, system security and operation. The collection equipment refers to field equipment such as collection terminals, concentrators, collectors, etc. These devices are mainly distributed in the areas under the jurisdiction of various power supply companies. Their main function is to read and receive electricity meter data. Remote communication is the data transmission channel between the collection master station and the collection equipment. Its communication methods include optical fiber, 230MHZ wireless and GPRS / CDMA wireless public network.

[0004] Failures in the collection of power consumption information from power users typically manifest in two ways at the collection master station: offline terminals (concentrators) and online terminals (concentrators). Online terminal failures can be further categorized as complete or partial meter data failures. When these failures occur, the power consumption of the electrical equipment is actually being consumed, but the collection master station is unable to collect statistics on the amount consumed. This means that the grid power consumption data in the power consumption information collection system is incomplete.

[0005] Therefore, how to improve the integrity of power grid electricity consumption data in the electricity consumption information collection system has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The technical problem solved by the present invention is that the power consumption data of the power grid in the power consumption information collection system is not complete.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for remotely collecting and processing power grid electricity consumption data, comprising: collecting original power grid electricity consumption data of target power-consuming devices; determining abnormal original power grid electricity consumption data as target power grid electricity consumption data; searching for the historical power grid electricity consumption sequence of the target power-consuming device from a preset power consumption database; determining the power-consuming device corresponding to the power grid electricity consumption sequence with the greatest similarity to the change curve of the historical power grid electricity consumption sequence as the approximate power-consuming device; determining estimated power grid electricity consumption data based on the historical power grid electricity consumption sequence and the power grid electricity consumption data of the approximate power device; and replacing the target power grid electricity consumption data with the estimated power grid electricity consumption data.

[0008] As a preferred solution of the method for remotely collecting and processing power grid electricity consumption data described in the present invention, the method for remotely collecting and processing power grid electricity consumption data further includes: collecting original temperature data and original time data corresponding to the original power grid electricity consumption data; determining the abnormal original power grid electricity consumption data as the target power grid electricity consumption data includes: if the data value of the original power grid electricity consumption data is a null value, determining the original power grid electricity consumption data as the target power grid electricity consumption data; replacing the target power grid electricity consumption data with the estimated power grid electricity consumption data includes: determining the temperature data corresponding to the time data with the smallest distance value from the original time data in the approximate power consumption device as the approximate temperature data; if the difference between the original temperature data and the approximate temperature data is less than or equal to a preset threshold, replacing the target power grid electricity consumption data with the estimated power grid electricity consumption data.

[0009] As a preferred solution of the method for remotely collecting and processing power grid electricity consumption data described in the present invention, wherein: the power-consuming equipment corresponding to the power grid electricity consumption data having the greatest similarity to the change curve of the historical power grid electricity consumption sequence is determined as the approximate power-consuming equipment, including: performing derivative processing on the historical power grid electricity consumption sequence to obtain a derivative sequence of the historical power grid electricity consumption sequence; and determining the power-consuming equipment corresponding to the derivative sequence having the greatest similarity to the derivative sequence of the historical power grid electricity consumption sequence as the approximate power-consuming equipment.

[0010] As a preferred solution of the method for remotely collecting and processing power grid power consumption data described in the present invention, wherein: the historical power grid power consumption sequence is a sequence formed by data pairs of time data, temperature data and power grid power consumption data. Before performing derivation processing on the historical power grid power consumption sequence to obtain a derivative sequence of the historical power grid power consumption sequence, the method further includes: determining independent variable data based on the time data and temperature data of the historical power grid power consumption sequence; determining the power grid power consumption data of the historical power grid power consumption sequence as dependent variable data; and determining the derivative of the dependent variable data with respect to the independent variable data to obtain the derivative sequence.

[0011] As a preferred solution of the method for remotely collecting and processing power grid electricity consumption data described in the present invention, the independent variable data is determined based on the time data and temperature data of the historical power grid electricity consumption sequence, including: determining the season information corresponding to the time data, the year information corresponding to the time data, the month information corresponding to the time data and the moment information corresponding to the time data, as well as the degree of influence of the temperature data on the dependent variable data when it is used as an influencing factor; and determining the independent variable data based on the influencing factor whose degree of influence is greater than or equal to a preset degree.

[0012] As a preferred solution of the method for remote collection and processing of power grid electricity consumption data described in the present invention, before determining the power equipment corresponding to the derivative sequence with the greatest similarity to the derivative sequence of the historical power grid electricity consumption sequence as the approximate power equipment, the method includes: obtaining the target power type information of the target power equipment; screening out power equipment whose power type information is consistent with the target power type information from the power consumption database as the power equipment to be selected; determining the power equipment corresponding to the derivative sequence with the greatest similarity to the derivative sequence of the historical power grid electricity consumption sequence as the approximate power equipment includes: determining the power equipment corresponding to the derivative sequence with the greatest similarity to the derivative sequence of the historical power grid electricity consumption sequence among the power equipment to be selected as the approximate power equipment.

[0013] As a preferred solution of the method for remotely collecting and processing power grid electricity consumption data described in the present invention, wherein: the power consumption device corresponding to the derivative sequence of the power equipment to be selected having the greatest similarity with the derivative sequence of the historical power grid electricity consumption sequence is determined as an approximate power consumption device, including: determining the power grid electricity consumption sequence to be selected from the power grid electricity consumption data of the power equipment to be selected based on the data range of the independent variable data and the dependent variable data; and determining the power consumption device corresponding to the derivative sequence of the power grid electricity consumption sequence of each power equipment to be selected having the greatest similarity with the derivative sequence of the historical power grid electricity consumption sequence as an approximate power consumption device.

[0014] As a preferred solution of the method for remote collection and processing of power grid electricity consumption data described in the present invention, wherein: the determining of the estimated power grid electricity consumption data based on the historical power grid electricity consumption sequence and the power grid electricity consumption data of the approximate power consumption equipment includes: determining the difference between the average value of the power grid electricity consumption data of the historical power grid electricity consumption sequence and the average value of the power grid electricity consumption data of the selected power grid electricity consumption sequence of the approximate power consumption equipment; obtaining the current power grid electricity consumption data of the approximate power consumption equipment under the time data; and determining the estimated power grid electricity consumption data based on the difference and the current power grid electricity consumption data.

[0015] As a preferred solution of the method for remote collection and processing of power grid electricity consumption data described in the present invention, wherein: replacing the target power grid electricity consumption data with the estimated power grid electricity consumption data includes: replacing the data value of the target power grid electricity consumption data with the data value of the estimated power grid electricity consumption data; after replacing the target power grid electricity consumption data with the estimated power grid electricity consumption data, the method also includes: writing the target power grid electricity consumption data of the target power-consuming device into the power consumption database.

[0016] As a preferred solution of the method for remotely collecting and processing power grid electricity consumption data described in the present invention, after writing the target power grid electricity consumption data of the target power-consuming device into the power consumption database, the method further includes: determining the current derivative value of the target power grid electricity consumption data under the corresponding time data; and writing the current derivative value into the power consumption database.

[0017] The beneficial effects of the present invention are as follows: by filling in the collected abnormal original power grid power consumption data with estimated power grid power consumption data, the integrity of the power grid power consumption data in the power consumption information collection system is improved; by determining the power consumption equipment corresponding to the power grid power consumption sequence with the greatest similarity to the historical power grid power consumption sequence change curve as the approximate power consumption equipment, and determining the estimated power grid power consumption data based on the historical power grid power consumption sequence and the power grid power consumption data of the approximate power consumption equipment, the accuracy of the determination of the estimated power grid power consumption data can be improved, thereby improving the accuracy of the power grid power consumption data in the power consumption information collection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the basic flow of a method for remotely collecting and processing power consumption data of a power grid provided by one embodiment of the present invention; Figure 2 A flowchart of a method for processing power consumption information collection failure in a power consumption information collection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0020] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides a method for remotely collecting and processing power consumption data of a power grid, comprising: S110, collecting original grid power consumption data of target power-consuming equipment.

[0021] S120: Determine the abnormal original power grid power consumption data as target power grid power consumption data.

[0022] S130 , searching a preset power consumption database for a historical power grid power consumption sequence of the target power-consuming device.

[0023] S140 , determining the power-consuming device corresponding to the power grid power consumption sequence having the greatest similarity to the historical power grid power consumption sequence change curve as the approximate power-consuming device.

[0024] S150 , determining estimated grid power consumption data based on historical grid power consumption sequences and grid power consumption data of similar power-consuming devices.

[0025] S160: Replace the target power grid power consumption data with the estimated power grid power consumption data.

[0026] To provide a detailed description of the method for remotely collecting and processing power consumption data of a power grid, the method further includes the following steps S210 to S262: S210 , collecting original grid power consumption data of a target electrical device, and original temperature data and original time data corresponding to the original grid power consumption data.

[0027] The power grid electricity consumption data is remotely collected from the electric energy meter data. The electric energy meter is a smart meter that can simultaneously collect statistics on electricity consumption data and collect temperature data in the environment where the smart meter is installed.

[0028] The target electrical equipment includes all electrical equipment that can be counted by a target electrical energy meter. For example, a household electrical energy meter counts the electricity consumption of each household, and all electrical equipment in a household are collectively referred to as a target electrical equipment.

[0029] S220: If the data value of the original power grid power consumption data is a null value, the original power grid power consumption data is determined as the target power grid power consumption data.

[0030] When there is a failure in collecting electricity consumption information of power users, you can refer to Figure 2 The information returned by the fault terminal to the acquisition master station is fault information. Therefore, the power grid electricity consumption data corresponding to the fault terminal obtained by the acquisition master station is a null value. Therefore, the data value of the collected original power grid electricity consumption data can be conditionally judged to see whether it is a null value to determine whether the method provided in the embodiment of the present application can be used for processing.

[0031] S230: Searching for the historical power consumption sequence of the target power-consuming device from a preset power consumption database.

[0032] Among them, the electricity consumption database is a database for storing data at the collection master station, and can also be a database for storing data by the electricity information collection system; the historical power grid electricity consumption sequence is a sequence formed by data pairs of time data, temperature data and power grid electricity consumption data, among which the number of data pairs in the historical power grid electricity consumption sequence can be set according to actual usage needs. For example, twenty data pairs of the latest date are obtained to form a historical power grid electricity consumption sequence.

[0033] The electricity consumption data before the target electrical device fails is searched from the electricity consumption database. For example, if the target electrical device fails at 11:00 on May 26, 2025 (abbreviated as "2025.05.26.11.00"), sequence data such as (2025.05.26.10.00, 24°C, 110kW·H), (2025.05.26.09.00, 22°C, 108kW·H)... can be obtained.

[0034] S240 , determining the power-consuming device corresponding to the power grid power consumption sequence having the greatest similarity to the historical power grid power consumption sequence change curve as the approximate power-consuming device.

[0035] Among them, if the similarity between the power consumption sequence of a certain power grid and the change curve of the historical power consumption sequence is the greatest, it can be characterized that the change trend of the power consumption data of the power consumption sequence of the power grid is closest to the change trend of the historical power consumption sequence, that is, the power consumption pattern of the approximate power consumption equipment corresponding to the power consumption sequence of the power grid and the target power consumption equipment tends to be consistent. Subsequently, the power consumption of the target power consumption equipment that is missing from the statistics can be estimated based on the power consumption of the approximate power consumption equipment, thereby improving the accuracy of the estimation.

[0036] Specifically, the approximate electrical equipment may be determined through S241 to S244.

[0037] S241, determining independent variable data based on time data and temperature data of historical power grid power consumption sequences.

[0038] Preferably, S241 further includes S241a~S241b.

[0039] S241a, determining the degree of influence of the season information corresponding to the time data, the year information corresponding to the time data, the month information corresponding to the time data, the moment information corresponding to the time data, and the temperature data as influencing factors on the dependent variable data.

[0040] Electricity consumption can be categorized into urban, rural, commercial, and industrial. Urban electricity consumption refers to household appliances used by urban residents, which has an annual growth trend and significant seasonal fluctuations. Rural electricity consumption refers to electricity used by rural residents and the clothing industry. Commercial electricity consumption refers to lighting, air conditioning, power, and other electricity used by commercial sectors. It covers a large area and has stable growth, but commercial loads also have seasonal fluctuations. Industrial electricity consumption refers to electricity used for industrial production, and generally accounts for the largest proportion of industrial electricity consumption.

[0041] If the derivative of the grid electricity consumption data with respect to the time information is directly calculated based on the time information, it is easy to ignore the impact of annual growth, seasonal fluctuations, etc. on the grid electricity consumption data, resulting in multiple approximate power consumption devices, making the accuracy of the approximate power consumption devices insufficient.

[0042] Therefore, when considering the derivative of grid power consumption data with respect to the independent variable, the independent variable can be determined based on multiple factors that have a significant impact on the dependent variable data. Specifically, a regression analysis is performed on the historical grid power consumption series of the target electrical equipment to determine the significance of factors such as the season information corresponding to the time data, the year information corresponding to the time data, the month information corresponding to the time data, the time information corresponding to the time data, and the temperature data on the grid power consumption data. If the significance of the influencing factor exceeds a preset significance threshold, it can be considered that the influencing factor has a high degree of influence on the dependent variable data.

[0043] S241b, determining independent variable data according to influencing factors whose influence degree is greater than or equal to a preset degree.

[0044] For example, the magnitude of the influence can be explained through the coefficient of certainty of an equation. The coefficient of certainty of an equation indicates the degree to which variable X in the equation explains Y. The coefficient of certainty of an equation ranges from 0 to 1. The closer it is to 1, the stronger the ability of X to explain Y in the equation. The default value can be set to 0.8.

[0045] An example is given to illustrate the specific method of determining the independent variable data: if it is found that the significance of factors such as the season information corresponding to the time data, the year information corresponding to the time data, the month information corresponding to the time data, the moment information corresponding to the time data, and the temperature data on the power consumption data of the power grid exceeds the preset significance threshold, then the year information, month information, moment information, and temperature data can be normalized respectively, and then the equation coefficients of the above multiple influencing factors obtained by regression analysis are normalized to determine the influence weight of each influencing factor among the multiple influencing factors, and the numerical value of each influencing factor is weighted and summed based on this influence weight to obtain the independent variable data.

[0046] S242: Determine the power grid consumption data of the historical power grid consumption sequence as dependent variable data.

[0047] S243, determining the derivative of the dependent variable data with respect to the independent variable data, and obtaining a derivative sequence.

[0048] Among them, determining the derivative of the dependent variable data with respect to the independent variable data is to derive the discrete sequence, and the derivative sequence can be obtained by running a preset difference method derivative program, a gradient method derivative program, etc.

[0049] S244: Determine the electrical equipment corresponding to the derivative sequence having the greatest similarity to the derivative sequence of the historical power grid power consumption sequence as the approximate electrical equipment.

[0050] The similarity between two discrete sequences can be determined based on spatial distance calculation formulas, such as Euclidean distance and Manhattan distance.

[0051] In order to reduce the calculation time for determining the approximate electrical equipment in S244 and improve program efficiency, S244 further includes S244a to S244d: S244a: Obtain target power consumption type information of the target power-consuming device.

[0052] The target electricity consumption type information can be divided into urban electricity consumption, rural electricity consumption, commercial electricity consumption and industrial electricity consumption.

[0053] S244b, filtering out electrical equipment whose electricity usage type information is consistent with the target electricity usage type information from the electricity usage database as electrical equipment to be selected.

[0054] S244c, determining a candidate power grid power consumption sequence from the power grid power consumption data of the candidate power equipment according to the data range of the independent variable data and the dependent variable data.

[0055] S244d, determining the electrical device corresponding to the derivative sequence of each electrical device to be selected, which has the greatest similarity with the derivative sequence of the historical electrical network power consumption sequence, as an approximate electrical device.

[0056] In S244a-S244d, the massive power grid power consumption sequences in the power consumption database can be screened by the target power consumption type information, the independent variable data and the data range of the dependent variable data, thereby greatly reducing the time for determining the approximate power consumption equipment.

[0057] S250 , determining estimated grid power consumption data based on historical grid power consumption sequences and grid power consumption data of similar power-consuming devices.

[0058] Specifically, the calculation method for estimating grid power consumption data can refer to S251 to S253: S251 , determining a difference between an average value of grid power consumption data of a historical grid power consumption sequence and an average value of grid power consumption data of a candidate grid power consumption sequence of an approximate power-consuming device.

[0059] If the change curve of the candidate power consumption sequence of the approximate power consumer is consistent with the historical power consumption sequence of the target power consumer, it means that the power consumption pattern of the approximate power consumer is consistent with the power consumption pattern of the target power consumer, that is, the degree of change remains consistent. However, the specific power consumption data of the two may be quite different. Therefore, the difference between the power consumption data of the two can be determined first, and then the estimated power consumption data can be determined based on this difference.

[0060] S252, obtaining the current power consumption data of the power-consuming device in the current time data.

[0061] The current power consumption data of the power grid can be read directly from the power consumption database.

[0062] S253: Determine estimated grid power consumption data based on the difference and current grid power consumption data.

[0063] S261 , determining the temperature data corresponding to the time data of the approximate electrical device with the smallest distance from the original time data as the approximate temperature data.

[0064] Furthermore, when a fault occurs in the target grid power-consuming equipment, that is, when the returned original grid power consumption data is a null value, it is necessary to further determine whether the actual target grid power-consuming equipment corresponding to the target grid power-consuming equipment is disabled, that is, to exclude the situation where the power-consuming equipment does not actually consume electricity. When a fault occurs in the target grid power-consuming equipment, its circuit will usually still maintain abnormal operation. If the target grid power-consuming equipment does not generate an operating temperature, it means that the power-consuming equipment does not actually consume electricity. Therefore, the judgment conditions for the operating temperature of the target grid power-consuming equipment can be further set.

[0065] S262: If the difference between the original temperature data and the approximate temperature data is less than or equal to a preset threshold, the data value of the target grid power consumption data is replaced with the data value of the estimated grid power consumption data.

[0066] The power consumption patterns of the target power grid electrical equipment and the similar power consumption equipment are similar. The similar power consumption patterns can indicate a strong consistency in the ambient temperature. Therefore, it can be determined whether the original temperature data is abnormal based on the approximate temperature data of the similar power consumption equipment. The preset threshold can be set according to the specific usage situation, for example, set to a floating range of 2°C.

[0067] If multiple target power grid power consumption devices appear simultaneously in the power consumption information collection system, then in order to reduce the time for replacing a single target power grid power consumption data, further, after S262, the target power grid power consumption data of the target power consumption device can be written into the power consumption database, and the current derivative value of the target power grid power consumption data under the corresponding time data can be determined, and the current derivative value can be written into the power consumption database, so as to reduce the processing time of the target power grid power consumption data by pre-writing the derivative value.

[0068] The present application fills the collected abnormal original power grid power consumption data with estimated power grid power consumption data to improve the integrity of the power grid power consumption data in the power consumption information collection system; by determining the power consumption equipment corresponding to the power grid power consumption sequence with the greatest similarity to the historical power grid power consumption sequence change curve as the approximate power consumption equipment, and determining the estimated power grid power consumption data based on the historical power grid power consumption sequence and the power grid power consumption data of the approximate power consumption equipment, the accuracy of the determination of the estimated power grid power consumption data can be improved, thereby improving the accuracy of the power grid power consumption data in the power consumption information collection system. Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for remotely collecting and processing power grid electricity consumption data, characterized in that: include: Collect the original grid power consumption data of the target power-consuming equipment; Determine the abnormal original power grid power consumption data as the target power grid power consumption data; Searching for a historical grid power consumption sequence of the target power-consuming device from a preset power consumption database; Determine the power consumption equipment corresponding to the power grid power consumption sequence having the greatest similarity to the historical power grid power consumption sequence change curve as the approximate power consumption equipment; Determining estimated grid power consumption data based on the historical grid power consumption sequence and the grid power consumption data of the approximate power-consuming devices; The target power grid power consumption data is replaced by the estimated power grid power consumption data.

2. The method for remotely collecting and processing power grid electricity consumption data according to claim 1, characterized in that: The method for remotely collecting and processing power grid electricity consumption data further includes: Collecting original temperature data and original time data corresponding to the original grid power consumption data; The step of determining the abnormal original power grid power consumption data as the target power grid power consumption data includes: If the data value of the original power grid power consumption data is a null value, determining the original power grid power consumption data as the target power grid power consumption data; The replacing the target power grid power consumption data with the estimated power grid power consumption data includes: Determining the temperature data corresponding to the time data of the approximate electrical device with the smallest distance from the original time data as the approximate temperature data; If the difference between the original temperature data and the approximate temperature data is less than or equal to a preset threshold, the target grid power consumption data is replaced with the estimated grid power consumption data.

3. The method for remotely collecting and processing power grid electricity consumption data according to claim 2, characterized in that: The step of determining the power consumption device corresponding to the power grid power consumption data having the greatest similarity to the historical power grid power consumption sequence change curve as the approximate power consumption device includes: Performing a derivative process on the historical power grid power consumption sequence to obtain a derivative sequence of the historical power grid power consumption sequence; The electric device corresponding to the derivative sequence having the greatest similarity to the derivative sequence of the historical power grid power consumption sequence is determined as the approximate electric device.

4. The method for remotely collecting and processing power grid electricity consumption data according to claim 3, characterized in that: The historical power grid power consumption sequence is a sequence formed by data pairs of time data, temperature data, and power grid power consumption data. Before performing a derivation process on the historical power grid power consumption sequence to obtain a derivative sequence of the historical power grid power consumption sequence, the method further includes: Determining independent variable data based on the time data and temperature data of the historical power grid power consumption sequence; Determining the power grid electricity consumption data of the historical power grid electricity consumption sequence as dependent variable data; Derivatives of the dependent variable data with respect to the independent variable data are determined to obtain the derivative sequence.

5. The method for remotely collecting and processing power grid electricity consumption data according to claim 4, characterized in that: The determining of independent variable data based on the time data and temperature data of the historical power grid power consumption sequence includes: Determine the degree of influence of the season information corresponding to the time data, the year information corresponding to the time data, the month information corresponding to the time data, the moment information corresponding to the time data, and the temperature data as influencing factors on the dependent variable data; The independent variable data is determined according to the influencing factors whose influencing degree is greater than or equal to a preset degree.

6. The method for remotely collecting and processing power grid electricity consumption data according to claim 5, characterized in that: Before determining the electric device corresponding to the derivative sequence having the greatest similarity to the derivative sequence of the historical power grid power consumption sequence as the approximate electric device, the method includes: Obtain target power consumption type information of the target power-consuming device; Filtering out electrical equipment whose electricity usage type information is consistent with the target electricity usage type information from the electricity usage database as electrical equipment to be selected; The step of determining the electrical device corresponding to the derivative sequence having the greatest similarity to the derivative sequence of the historical power grid power consumption sequence as the approximate electrical device includes: The electrical device corresponding to the derivative sequence having the greatest similarity with the derivative sequence of the historical power grid power consumption sequence among the electrical devices to be selected is determined as the approximate electrical device.

7. The method for remotely collecting and processing power grid electricity consumption data according to claim 6, characterized in that: The step of determining, among the electrical devices to be selected, the electrical device corresponding to the derivative sequence having the greatest similarity to the derivative sequence of the historical power grid power consumption sequence as the approximate electrical device includes: Determine a grid power usage sequence to be selected from grid power usage data of the grid power equipment to be selected according to the data range of the independent variable data and the dependent variable data; The electric device corresponding to the derivative sequence of each electric device to be selected having the greatest similarity with the derivative sequence of the historical electric device to be selected is determined as the approximate electric device.

8. The method for remotely collecting and processing power grid electricity consumption data according to claim 7, characterized in that: The determining of the estimated grid power consumption data based on the historical grid power consumption sequence and the grid power consumption data of the approximate power-consuming devices includes: Determine a difference between an average value of the grid power consumption data of the historical grid power consumption sequence and an average value of the grid power consumption data of the candidate grid power consumption sequence of the approximate power-consuming device; Obtaining current power consumption data of the power grid of the approximate power-consuming device at the time data; The estimated grid power consumption data is determined according to the difference and the current grid power consumption data.

9. The method for remotely collecting and processing power grid electricity consumption data according to claim 8, characterized in that: The replacing the target power grid power consumption data with the estimated power grid power consumption data includes: Replacing the data value of the target power grid power consumption data with the data value of the estimated power grid power consumption data; After replacing the target grid power consumption data with the estimated grid power consumption data, the method further includes: The target power grid power consumption data of the target power-consuming device is written into the power consumption database.

10. The method for remotely collecting and processing power grid electricity consumption data according to claim 9, characterized in that: After writing the target power grid power consumption data of the target power-consuming device into the power consumption database, the method further includes: Determining a current derivative value of the target power grid power consumption data at corresponding time data; The current derivative value is written into the power usage database.

Images