Electric quantity data processing method and device

Abstract

The invention discloses an electric quantity data processing method and device, and the method comprises the steps: carrying out the logic verification of cleaned electric quantity data, and determining whether the electric quantity data is abnormal or not; under the condition that the electric quantity data is abnormal, recording the abnormal data and accumulating the number of times that the abnormal data appear in a first preset period; performing variable quantity analysis on each abnormal data recorded in the first preset period, and determining an abnormal data type based on an analysis result; under the condition that the type of the abnormal data is a first type, determining regression calculation starting time according to current time and accumulated times of the abnormal data; and determining the electric quantity data corresponding to the starting time as intermediate verification data, and performing regression calculation to obtain a calculation result. According to the electric quantity data processing method, the type of the abnormal data can be automatically identified, regression calculation is carried out on the abnormal data generated by manual meter replacement, manual processing is not needed, and manpower resources and time can be saved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an electric power data processing method and device. Background Art

[0002] The power data of energy storage equipment may be abnormal during cumulative metering. The first type of abnormal data is: the data is manually reset or changed when the code is not full, resulting in abnormal data and making the data unavailable; the second type of abnormal data is abnormal data generated by non-human operations. At present, the abnormal data is mainly monitored and intercepted by interceptors. After the abnormal data is detected, an alarm mechanism is added for it. The operation and maintenance personnel can discover the abnormal data through the alarm mechanism. After discovering the abnormal data, the operation and maintenance personnel check the data and then notify the after-sales personnel. The after-sales personnel will ask the reason, whether it is due to data abnormality caused by misoperation or the data is reset to zero due to the replacement of the meter.

[0003] On the one hand, due to the offline data warehouse calculation, data alarms will not be discovered in a timely manner; on the other hand, both of the above-mentioned abnormal data need to be manually processed by staff, and the abnormal data also needs to be manually re-run, which not only requires high professional capabilities of the staff but also consumes a lot of manpower and time. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method and device for processing electric quantity data, which can solve at least one of the above problems existing in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An embodiment of the present invention provides a method for processing electric quantity data, wherein the method comprises:

[0007] Performing logic verification on the power data after cleaning to determine whether the power data is abnormal;

[0008] In the case where the power data is abnormal, the abnormal data is recorded and the number of times the abnormal data occurs within a first preset period is accumulated;

[0009] Performing a variation analysis on each abnormal data recorded within the first preset period, and determining the abnormal data type based on the analysis result, wherein the abnormal data type includes: a first type for indicating that the abnormality is caused by human operation, and a second type for indicating that the abnormality is caused by non-human operation;

[0010] In the case where the abnormal data type is the first type, determining the regression calculation start time according to the current time and the number of accumulated abnormal data;

[0011] The power data corresponding to the start time is determined as the intermediate verification data for regression calculation to obtain a calculation result, wherein the calculation result is a power change value within a second preset period.

[0012] Optionally, the step of performing a logic check on the cleaned power data to determine whether the power data is abnormal includes:

[0013] Comparing the power data after cleaning with the historical data verified within the first preset period to determine whether the power after cleaning is monotonically increasing;

[0014] If it is monotonically increasing, determine whether the increment value does not exceed the preset threshold;

[0015] If it does not exceed the preset threshold, determining that the power data after cleaning is correct data;

[0016] If the power data after cleaning does not increase monotonically or the increment value exceeds a preset threshold, it is determined that the power data after cleaning is abnormal data.

[0017] Optionally, the step of performing a variation analysis on each abnormal data recorded within the first preset period, and determining the type of abnormal data based on the analysis result includes:

[0018] Determine whether the change amount of each abnormal data recorded within the first preset period is monotonically increasing;

[0019] If it is monotonically increasing, determining that the abnormal data type is the first type;

[0020] If the abnormal data does not change, it is determined that the type of the abnormal data is the second type.

[0021] Optionally, when the abnormal data type is the first type, the step of determining the regression calculation start time according to the current time and the number of accumulated abnormal data includes:

[0022] In the case where the abnormal data type is the first type, determining the current time and the number of accumulated abnormal data;

[0023] Determine the regression duration based on the current time, the number of accumulated abnormal data, and the data collection time interval;

[0024] The regression calculation start time is obtained by regressing the regression time length from the current time.

[0025] Optionally, before the step of performing a logic check on the cleaned power data to determine whether the power data is abnormal, the method further includes:

[0026] The power data generated by the data source is saved by the data storage system; the power data is cleaned at the detail layer of the data warehouse to obtain the cleaned power data.

[0027] An embodiment of the present invention further provides a power data processing device, wherein the device comprises:

[0028] A determination module, used for performing a logic check on the power data after cleaning to determine whether the power data is abnormal;

[0029] A recording module, used for recording the abnormal data and accumulating the number of times the abnormal data occurs within a first preset period when the power data is abnormal;

[0030] A type determination module, configured to perform a change analysis on each abnormal data recorded within the first preset period, and determine the type of abnormal data based on the analysis result, wherein the type of abnormal data includes: a first type for indicating that the abnormality is caused by human operation, and a second type for indicating that the abnormality is caused by non-human operation;

[0031] A time determination module, used for determining the regression calculation start time according to the current time and the number of accumulated abnormal data when the abnormal data type is the first type;

[0032] The regression calculation module is used to determine the power data corresponding to the start time as the intermediate verification data to perform regression calculation to obtain a calculation result, wherein the calculation result is a power change value within a second preset period.

[0033] Optionally, the determining module includes:

[0034] The first submodule is used to compare the power data after cleaning with the historical data verified within the first preset period to determine whether the power data after cleaning is monotonically increasing;

[0035] The second submodule is used to determine whether the increment value does not exceed a preset threshold if the increment value is monotonically increasing;

[0036] The third submodule is used to determine that the power data after cleaning is correct data if it does not exceed a preset threshold;

[0037] The fourth submodule is used to determine that the power data after cleaning is abnormal data if the power data after cleaning does not increase monotonically or the increment value exceeds a preset threshold.

[0038] Optionally, the type determination module includes:

[0039] A fifth submodule is used to determine whether the change amount of each abnormal data recorded within the first preset period is monotonically increasing;

[0040] The sixth submodule is used to determine that the abnormal data type is the first type if it is monotonically increasing; and to determine that the abnormal data type is the second type if there is no change in each of the abnormal data.

[0041] Optionally, the time determination module includes:

[0042] A seventh submodule, for determining the current time and the number of accumulated abnormal data when the abnormal data type is the first type;

[0043] The eighth submodule is used to determine the regression time according to the current time, the number of accumulated abnormal data and the data collection time interval;

[0044] The ninth submodule is used to reverse the regression time from the current time to obtain the regression calculation start time.

[0045] Optionally, the device further comprises:

[0046] A saving module, used for saving the electric quantity data generated by the data source through the data storage system before the determination module performs a logic check on the cleaned electric quantity data to determine whether the electric quantity data is abnormal;

[0047] The cleaning module is used to clean the power data at the data warehouse detail layer to obtain the cleaned power data.

[0048] The electric quantity data processing scheme disclosed in the embodiment of the present invention performs a logical check on the cleaned electric quantity data to determine whether the electric quantity data is abnormal; in the case of abnormal electric quantity data, the abnormal data is recorded and the number of abnormal data occurrences in the first preset period is accumulated; the variation of each abnormal data recorded in the first preset period is analyzed, and the abnormal data type is determined based on the analysis result; in the case of the abnormal data type being the first type, the regression calculation start time is determined according to the current time and the number of abnormal data accumulated; the electric quantity data corresponding to the start time is determined as the intermediate verification data for regression calculation to obtain the calculation result. The electric quantity data processing scheme confirms whether the abnormal data is truly abnormal data (i.e., the second type of abnormal data) or abnormal data caused by manual table switching (i.e., the first type of abnormal data) through the state and the measurement abnormal variation. For the abnormal data caused by manual table switching, regression calculation can be performed according to the number of abnormal data occurrences, so as to achieve the purpose of reliable and efficient processing of historical data. On the one hand, the first type of abnormal data is automatically processed without manual verification and processing, which can save manpower and time costs; on the other hand, the first type of abnormal data can be regressed, and no manual rerun is required, which can further save manpower and time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart showing a method for processing electric quantity data according to an embodiment of the present application;

[0050] Figure 2 is a flowchart showing another method for processing electric quantity data according to an embodiment of the present application;

[0051] Figure 3 It is a structural block diagram of an electric quantity data processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0053] The power data processing method provided in the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0054] As attached Figure 1 As shown, the power data processing method of the embodiment of the present application includes the following steps:

[0055] Step 101: Perform a logic check on the power data after cleaning to determine whether the power data is abnormal.

[0056] The power data processing method provided in the embodiment of the present application is used to calculate the charge and discharge amount of the cabinet within a preset period. The cabinet is charged during the electricity price trough period and discharged during the electricity price peak period. By calculating the charge and discharge amount and the price difference of the electricity price during the peak and trough periods, the benefits generated by the cabinet can be calculated.

[0057] Before performing a logical check on the cleaned electricity data to determine whether the electricity data is abnormal, the following process is also included: saving the electricity data generated by the data source through the data storage system (i.e., the ODS layer); cleaning the electricity data at the data warehouse detail layer (i.e., the DWD layer) to obtain the cleaned electricity data.

[0058] The DWD (Data Warehouse Detail) layer is the core layer of data processing. Its main task is to clean, process, integrate, aggregate and perform other operations on the data in the ODS layer to build a data model that meets business needs.

[0059] The data warehouse may include the following layers: ODS, CDM, ADS, and DWD, etc. ODS is the operational data layer, CDM is the common dimensional model layer, CDM is divided into the DWD detail data layer, DWS is the summary data layer, and ADS is the application data layer.

[0060] In the actual implementation process, DWS can be used to perform logical verification on the power data after cleaning.

[0061] An optional method of performing a logic check on the cleaned power data to determine whether the power data is abnormal may be as follows:

[0062] S1: Compare the power data after cleaning with the historical data verified within the first preset period to determine whether the power after cleaning is monotonically increasing;

[0063] The first preset period is usually set to 24 hours. It should be noted that the first preset period can be flexibly set by those skilled in the art, and no specific limitation is made in the embodiments of the present application.

[0064] S2: If it is monotonically increasing, determine whether the increment value does not exceed the preset threshold; if it does not exceed the preset threshold, determine that the power data after cleaning is correct data;

[0065] The preset threshold can be set to the capacity of a single cabinet, such as 200 degrees to 250 degrees.

[0066] S3: If the power data after cleaning does not increase monotonically or the increment value exceeds a preset threshold, it is determined that the power data after cleaning is abnormal data.

[0067] Normal power data (i.e. correct power data) increases monotonically over time, while abnormal power data may be caused by jumps or full codes and does not follow the monotonically increasing law. Therefore, when judging whether the power data is abnormal data, it is sufficient to judge whether it increases monotonically and whether the increment value exceeds the preset threshold.

[0068] If the electric quantity data is abnormal, step 102 is executed. If the electric quantity data is normal (ie, correct data), the data is calculated by ADS to obtain a calculation result.

[0069] Step 102: When the power data is abnormal, the abnormal data is recorded and the number of times the abnormal data occurs within a first preset period is accumulated.

[0070] The DWS performs data logic verification at a data collection time interval, such as once every 5 minutes. After each verification determines that the power data is abnormal data, the abnormal data is recorded and the cumulative number of abnormal data is increased by 1.

[0071] Step 103: performing a variation analysis on each abnormal data recorded within the first preset period, and determining the type of abnormal data based on the analysis result.

[0072] The abnormal data types include: a first type for indicating that the abnormality is caused by human operation, and a second type for indicating that the abnormality is caused by non-human operation.

[0073] An optional method of performing a variation analysis on each abnormal data recorded within the first preset period and determining the type of abnormal data based on the analysis result may be as follows:

[0074] Determine whether the change amount of each abnormal data recorded within the first preset period is monotonically increasing; if it is monotonically increasing, determine that the abnormal data type is the first type; if the abnormal data does not change, determine that the abnormal data type is the second type.

[0075] When the abnormal data type is the first type, execute step 104; when the abnormal data type is the second type, determine that the abnormal data remains the data before the jump, label the abnormal data and output an alarm, and the staff will go to the site for inspection and adjustment.

[0076] Step 104: When the abnormal data type is the first type, the regression calculation start time is determined according to the current time and the number of accumulated abnormal data.

[0077] In an optional case where the abnormal data type is the first type, a method for determining the regression calculation start time according to the current time and the number of accumulated abnormal data may be as follows:

[0078] When the abnormal data type is the first type, determine the current time and the number of accumulated abnormal data; determine the regression duration based on the current time, the number of accumulated abnormal data and the data collection time interval; and calculate the regression duration backward from the current time to obtain the regression calculation start time.

[0079] For example: the current time is x o'clock, and the number of accumulated abnormal data is 12 times, then the regression time is 12 times × 5 minutes = 1 hour, and the starting time of the regression calculation is set to x-1 o'clock, and the power data is taken again at this starting time for logic verification.

[0080] Step 105: Determine the power data corresponding to the start time as the intermediate verification data for regression calculation to obtain a calculation result.

[0081] After determining the start time of the regression calculation and the intermediate verification data, return to step 101 to perform logic verification on the power data after the start time according to the data collection time interval and execute subsequent processes.

[0082] The calculation result is the value of the change in power within the second preset period.

[0083] The method for processing electric quantity data provided in the embodiment of the present application performs a logical check on the electric quantity data after cleaning to determine whether the electric quantity data is abnormal; in the case of abnormal electric quantity data, records the abnormal data and accumulates the number of abnormal data occurrences in the first preset period; performs a change analysis on each abnormal data recorded in the first preset period, and determines the abnormal data type based on the analysis result; in the case of the abnormal data type being the first type, determines the regression calculation start time according to the current time and the number of abnormal data accumulated; determines the electric quantity data corresponding to the start time as the intermediate verification data for regression calculation to obtain the calculation result. The method for processing electric quantity data confirms whether the abnormal data is truly abnormal data (i.e., the second type of abnormal data) or abnormal data caused by manual table switching (i.e., the first type of abnormal data) through the state and the abnormal change of measurement, and the abnormal data caused by manual table switching can be regressed according to the number of abnormal data occurrences, so as to achieve the purpose of reliable and efficient processing of historical data. On the one hand, the first type of abnormal data is automatically processed without manual verification and processing, which can save manpower and time costs; on the other hand, the first type of abnormal data can be regressed, and no manual rerun is required, which can further save manpower and time costs.

[0084] Refer to the following Figure 2 The power data processing method provided in the embodiment of the present application is described.

[0085] The method for processing power data provided in the embodiment of the present application comprises the following steps:

[0086] Step 201: ODS original data is saved.

[0087] The original data generated by the data source is saved in the ODS layer, and the original data is the power data mentioned above.

[0088] Step 202: DWD data cleaning.

[0089] The original data is cleaned at the DWD layer to obtain cleaned power data. The specific method of cleaning the data can be implemented by those skilled in the art using any appropriate existing method, and this is not specifically limited in the embodiments of the present application.

[0090] Step 203: DWS data logic check; when the check result is abnormal, execute step 204; when the check result is correct, directly execute step 208.

[0091] If the verification result is abnormal, it means that the data verified this time is abnormal data, otherwise, if the verification result is correct, it means that the data verified this time is correct data. The specific verification method adopted by the DWS data logic verification is similar to that in the above embodiment, and will not be repeated here.

[0092] Step 204: When the verification result is abnormal, record the abnormal data, the number of abnormal data occurrences, and the amount of abnormal data change.

[0093] Step 205: Determine the change in the abnormal value; if the determination result is a positive change, execute step 206; if the determination result is no change, execute step 207.

[0094] In the embodiment of the present application, the data status is added to monitor whether the data is consistent with the data at the last moment of logic verification. If an abnormal jump occurs, the data at the last moment is maintained first, and then the data status of this time is added, and the number of abnormal data occurrences is recorded. When the data maintains a value for a long time (the default is one day, and the normal stability is that the device will be charged and discharged twice a day, and the power data will have an incremental change), it is considered that the data is abnormal. The alarm is notified to remind the operation and maintenance personnel of the data abnormality and notify the after-sales personnel. This type of data does not need to be re-run. This type of abnormal data is the second type of abnormal data mentioned above.

[0095] The second is to record the number of abnormal data and its change value. If abnormal jump data appears and there is a positive change (i.e. monotonically increasing) after the jump, it is considered that the abnormal data is caused by artificial replacement of the meter, resulting in the execution of step 206. This type of abnormal data is the first type of abnormal data mentioned above.

[0096] Step 206: Modify the intermediate verification data and return to execute step 203.

[0097] This step is the data replacement for the abnormal data calculation process. The regression calculation start time is determined according to the current time and the number of accumulated abnormal data. The power data corresponding to the start time is determined as the intermediate verification data, and the intermediate verification data is modified in the intermediate table. Then the re-run data mechanism is called to re-run the data within the count range to complete the data, and the data calculation is performed at the ADS layer to obtain the calculation result.

[0098] Step 207: The data before the abnormal data is considered to be kept.

[0099] When the abnormal data type is the second type, it is determined that the abnormal data remains the data before the jump, and a label is added to the abnormal data to output an alarm, and the staff goes to the site for inspection and adjustment.

[0100] Step 208: ADS data calculation to obtain calculation results.

[0101] The electricity data processing method provided in the embodiment of the present application confirms whether it is abnormal data or manual meter replacement through the state and abnormal metering change, and performs regression calculation according to the number of occurrences of the abnormal data to achieve the purpose of historical data processing. The method can judge the type of abnormal data and perform automatic regression calculation on the first type of abnormal data in a targeted manner. There is no need for each type of abnormal data to be manually processed by staff, which can save manpower and time costs.

[0102] Figure 3 A structural block diagram of an electric quantity data processing device for implementing an embodiment of the present application.

[0103] The power data processing device provided in the embodiment of the present application includes the following functional modules:

[0104] The determination module 301 is used to perform a logic check on the power data after cleaning to determine whether the power data is abnormal;

[0105] The recording module 302 is used to record the abnormal data and accumulate the number of times the abnormal data occurs within a first preset period when the power data is abnormal;

[0106] A type determination module 303 is used to perform a change analysis on each abnormal data recorded within the first preset period, and determine the abnormal data type based on the analysis result, wherein the abnormal data type includes: a first type for indicating that the abnormality is caused by human operation, and a second type for indicating that the abnormality is caused by non-human operation;

[0107] A time determination module 304, configured to determine a regression calculation start time according to the current time and the number of accumulated abnormal data when the abnormal data type is the first type;

[0108] The regression calculation module 305 is used to determine the power data corresponding to the start time as the intermediate verification data to perform regression calculation to obtain a calculation result, wherein the calculation result is a power change value within a second preset period.

[0109] Optionally, the determining module includes:

[0110] The first submodule is used to compare the power data after cleaning with the historical data verified within the first preset period to determine whether the power data after cleaning is monotonically increasing;

[0111] The second submodule is used to determine whether the increment value does not exceed a preset threshold if the increment value is monotonically increasing;

[0112] The third submodule is used to determine that the power data after cleaning is correct data if it does not exceed a preset threshold;

[0113] The fourth submodule is used to determine that the power data after cleaning is abnormal data if the power data after cleaning does not increase monotonically or the increment value exceeds a preset threshold.

[0114] Optionally, the type determination module includes:

[0115] A fifth submodule is used to determine whether the change amount of each abnormal data recorded within the first preset period is monotonically increasing;

[0116] The sixth submodule is used to determine that the abnormal data type is the first type if it is monotonically increasing; and to determine that the abnormal data type is the second type if there is no change in each of the abnormal data.

[0117] Optionally, the time determination module includes:

[0118] A seventh submodule, for determining the current time and the number of accumulated abnormal data when the abnormal data type is the first type;

[0119] The eighth submodule is used to determine the regression time according to the current time, the number of accumulated abnormal data and the data collection time interval;

[0120] The ninth submodule is used to reverse the regression time from the current time to obtain the regression calculation start time.

[0121] Optionally, the device further comprises:

[0122] A saving module, used for saving the electric quantity data generated by the data source through the data storage system before the determination module performs a logic check on the cleaned electric quantity data to determine whether the electric quantity data is abnormal;

[0123] The cleaning module is used to clean the power data at the data warehouse detail layer to obtain the cleaned power data.

[0124] The power data processing device provided in the embodiment of the present application performs a logical check on the cleaned power data to determine whether the power data is abnormal; in the case of abnormal power data, the abnormal data is recorded and the number of abnormal data occurrences in the first preset period is accumulated; the change amount of each abnormal data recorded in the first preset period is analyzed, and the abnormal data type is determined based on the analysis result; in the case of the abnormal data type being the first type, the regression calculation start time is determined according to the current time and the number of abnormal data accumulated; the power data corresponding to the start time is determined as the intermediate verification data for regression calculation to obtain the calculation result. The power data processing device confirms whether the abnormal data is truly abnormal data (i.e., the second type of abnormal data) or abnormal data caused by manual table switching (i.e., the first type of abnormal data) through the state and the measurement abnormal change amount, and the abnormal data caused by manual table switching can be regressed according to the number of abnormal data occurrences, so as to achieve the purpose of reliable and efficient processing of historical data. On the one hand, the first type of abnormal data is automatically processed without manual verification and processing, which can save manpower and time costs; on the other hand, the first type of abnormal data can be regressed, and no manual rerun is required, which can further save manpower and time costs.

[0125] An embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0126] Memory, used to store computer programs;

[0127] The processor is used to implement each step of the power data processing method executed by the host computer in the above method embodiment when executing the program stored in the memory.

[0128] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0129] The communication interface is used for communication between the above terminal and other devices.

[0130] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0131] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0132] In another embodiment provided by the present invention, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is executed on a computer, the computer implements the steps of the method for processing power data described in any of the above embodiments.

[0133] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0134] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0135] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0136] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing electric quantity data, characterized in that: The method comprises: Performing logic verification on the power data after cleaning to determine whether the power data is abnormal; In the case where the power data is abnormal, the abnormal data is recorded and the number of times the abnormal data occurs within a first preset period is accumulated; Performing a variation analysis on each abnormal data recorded within the first preset period, and determining the abnormal data type based on the analysis result, wherein the abnormal data type includes: a first type for indicating that the abnormality is caused by human operation, and a second type for indicating that the abnormality is caused by non-human operation; In the case where the abnormal data type is the first type, determining the regression calculation start time according to the current time and the number of accumulated abnormal data; The power data corresponding to the start time is determined as the intermediate verification data for regression calculation to obtain a calculation result, wherein the calculation result is a power change value within a second preset period.

2. The method according to claim 1, characterized in that: The step of performing a logic check on the cleaned power data to determine whether the power data is abnormal includes: Comparing the power data after cleaning with the historical data verified within the first preset period to determine whether the power data after cleaning is monotonically increasing; If it is monotonically increasing, determine whether the increment value does not exceed the preset threshold; If it does not exceed the preset threshold, determining that the power data after cleaning is correct data; If the power data after cleaning does not increase monotonically or the increment value exceeds a preset threshold, it is determined that the power data after cleaning is abnormal data.

3. The method according to claim 1, characterized in that The step of performing a variation analysis on each abnormal data recorded within the first preset period, and determining the type of abnormal data based on the analysis result, comprises: Determine whether the change amount of each abnormal data recorded within the first preset period is monotonically increasing; If it is monotonically increasing, determining that the abnormal data type is the first type; If the abnormal data does not change, it is determined that the type of the abnormal data is the second type.

4. The method according to claim 1, characterized in that In the case where the abnormal data type is the first type, the step of determining the regression calculation start time according to the current time and the number of accumulated abnormal data includes: In the case where the abnormal data type is the first type, determining the current time and the number of accumulated abnormal data; Determine the regression duration based on the current time, the number of accumulated abnormal data, and the data collection time interval; The regression calculation start time is obtained by regressing the regression time length from the current time.

5. The method according to claim 1, characterized in that Before the step of performing a logic check on the cleaned power data to determine whether the power data is abnormal, the method further includes: The power data generated by the data source is stored in the data storage system; The electricity quantity data is cleaned at the detail layer of the data warehouse to obtain cleaned electricity quantity data.

6. An electric quantity data processing device, characterized in that: The device comprises: A determination module, used for performing a logic check on the power data after cleaning to determine whether the power data is abnormal; A recording module, used for recording the abnormal data and accumulating the number of times the abnormal data occurs within a first preset period when the power data is abnormal; A type determination module, configured to perform a change analysis on each abnormal data recorded within the first preset period, and determine the type of abnormal data based on the analysis result, wherein the type of abnormal data includes: a first type for indicating that the abnormality is caused by human operation, and a second type for indicating that the abnormality is caused by non-human operation; A time determination module, used for determining the regression calculation start time according to the current time and the number of accumulated abnormal data when the abnormal data type is the first type; The regression calculation module is used to determine the power data corresponding to the start time as the intermediate verification data to perform regression calculation to obtain a calculation result, wherein the calculation result is a power change value within a second preset period.

7. The device according to claim 6, characterized in that The determination module comprises: The first submodule is used to compare the power data after cleaning with the historical data verified within the first preset period to determine whether the power data after cleaning is monotonically increasing; The second submodule is used to determine whether the increment value does not exceed a preset threshold if the increment value is monotonically increasing; The third submodule is used to determine that the power data after cleaning is correct data if it does not exceed a preset threshold; The fourth submodule is used to determine that the power data after cleaning is abnormal data if the power data after cleaning does not increase monotonically or the increment value exceeds a preset threshold.

8. The device according to claim 6, characterized in that The type determination module comprises: A fifth submodule is used to determine whether the change amount of each abnormal data recorded within the first preset period is monotonically increasing; The sixth submodule is used to determine that the abnormal data type is the first type if it is monotonically increasing; and to determine that the abnormal data type is the second type if there is no change in each of the abnormal data.

9. The device according to claim 6, characterized in that The time determination module comprises: A seventh submodule, for determining the current time and the number of accumulated abnormal data when the abnormal data type is the first type; The eighth submodule is used to determine the regression time according to the current time, the number of accumulated abnormal data and the data collection time interval; The ninth submodule is used to reverse the regression time from the current time to obtain the regression calculation start time.

10. The device according to claim 6, characterized in that The device also includes: A saving module, used for saving the electric quantity data generated by the data source through a data storage system before the determining module performs a logic check on the cleaned electric quantity data to determine whether the electric quantity data is abnormal; The cleaning module is used to clean the power data at the data warehouse detail layer to obtain the cleaned power data.

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