Processing method and device of power data and power data system

Abstract

The application relates to a power data processing method and device and a power data system. The processing method comprises the following steps: acquiring power data; obtaining a characteristic parameter according to the power data; judging whether the power data is stored in a data warehouse according to the characteristic parameter; in the case that the power data is not stored in the data warehouse, storing the power data in the data warehouse, and performing ETL processing according to the data stored in the data warehouse. The method ensures that the computing power is used in the business that truly needs calculation, and greatly improves the data processing efficiency of the power data system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system big data, in particular to a power data processing method and device, a power data system and a computer readable storage medium. BACKGROUND

[0002] By monitoring the actual operation state of each line and equipment of the power system and controlling the related equipment, the stable operation of the power system can be ensured. In the case of a large scale of the power system, a large amount of power data with high density will be generated. The power data system in the traditional technology has the problem of low processing efficiency of power data, which causes waste of computing power. SUMMARY

[0003] Therefore, it is necessary to provide a power data processing method, device, power data system and computer readable storage medium to solve the problems of low efficiency and waste of computing power caused by repeated collection and repeated calculation of power data.

[0004] In one aspect, the present application provides a power data processing method, comprising: acquiring power data; obtaining a feature parameter according to the power data, and determining whether the power data is stored in a data warehouse according to the feature parameter; in the case that the power data is not stored in the data warehouse, storing the power data in the data warehouse, and performing ETL processing according to the data stored in the data warehouse.

[0005] In one embodiment, the power data includes a device identifier, a data collection time and power operation data, and the feature parameter includes a first feature parameter and a second feature parameter; the step of obtaining a feature parameter according to the power data and determining whether the power data is stored in a data warehouse according to the feature parameter includes: obtaining a first feature parameter according to the device identifier and the data collection time, and obtaining a second feature parameter according to the device identifier, the data collection time and the power operation data; in the case that there is no data matching the first feature parameter in the data warehouse, it is determined that the power data is not stored in the data warehouse; in the case that there is data matching the first feature parameter in the data warehouse, it is determined whether there is data matching the second feature parameter in the data matching the first feature parameter, and if not, it is determined that the power data is not stored in the data warehouse.

[0006] In one of the embodiments, the step of obtaining the second feature parameter according to the device identifier, the data collection time and the power operation data comprises: composing a string with the device identifier, the data collection time and the power operation data; and calculating a Hash feature value of the string to obtain the second feature parameter.

[0007] In one of the embodiments, in the case that the data matching the first feature parameter exists in the data warehouse and the data matching the first feature parameter does not match the second feature parameter to determine that the power data is not stored in the data warehouse, the step of storing the power data in the data warehouse and performing ETL processing according to the data stored in the data warehouse comprises: replacing the data matching the first feature parameter in the data warehouse with the power data; determining a target time interval; the target time interval is a time interval in which the data collection time of the power data is located; and performing ETL processing on the data stored in the data warehouse in the target time interval again.

[0008] In one of the embodiments, the step of performing ETL processing on the data stored in the data warehouse in the target time interval comprises: calculating power statistical data of the target time interval according to the data stored in the data warehouse in the target time interval; and the power statistical data comprises maximum voltage, minimum voltage, average voltage, current load, minimum load and maximum load.

[0009] In one of the embodiments, the step of obtaining the power data comprises: reading the power data from a distributed message queue server.

[0010] In one of the embodiments, the Hash feature value comprises MD5 feature value, SHA256 feature value and SHA1 feature value.

[0011] In another aspect, the embodiments of the present application provide a power data system, comprising: a data warehouse server configured with a data warehouse; a business server configured to output power data; a distributed message queue server connected with the business server and configured to store the power data output by the business server in a distributed message queue; an access server connected with the distributed message queue server and the data warehouse server, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the power data processing method described above when executing the computer program; and a scheduling server configured to schedule the data warehouse server to perform ETL processing according to the data stored in the data warehouse under the instruction of the access server.

[0012] In yet another aspect, an embodiment of the present application provides a device for processing power data, comprising: a data acquisition module configured to acquire power data; a judgment module configured to obtain a feature parameter according to the power data, and judge whether the power data is stored in a data warehouse according to the feature parameter; and a processing module configured to store the power data in the data warehouse in the case that the power data is not stored in the data warehouse, and perform ETL processing according to the data stored in the data warehouse.

[0013] In yet another aspect, an embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the above-mentioned method for processing power data.

[0014] Based on any of the above-mentioned embodiments, the mapping relationship between the feature parameter and the power data is utilized, and the efficient comparison between the feature parameters constituted by short strings can determine whether the power data to be stored in the warehouse will be duplicated with the data in the data warehouse. Moreover, only the data in the data warehouse that is not duplicated is subjected to downstream calculation, which ensures that the computing power is used in the business that truly needs calculation, and greatly improves the data processing efficiency of the power data system. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 FIG. 1 is a flowchart of a method for processing power data in an embodiment;

[0016] Figure 2 FIG. 2 is a flowchart of judging whether power data is stored in a data warehouse in an embodiment;

[0017] Figure 3 FIG. 3 is a flowchart of performing ETL processing in an embodiment;

[0018] Figure 4 FIG. 4 is a flowchart of calculating a second feature parameter in an embodiment;

[0019] Figure 5 FIG. 5 is a structural diagram of a power data system in an embodiment;

[0020] Figure 6 FIG. 6 is a structural block diagram of a device for processing power data in an embodiment. DETAILED DESCRIPTION

[0021] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms, and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0023] It is to be understood that the terms "first", "second", and the like, used herein do not connote any hierarchy or order, but are used to distinguish one element from another.

[0024] Spatially relative terms, such as "under", "below", "lower", "over", "upper" and the like, can be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientations depicted in the figures. For example, if a device described is turned over, elements described as "under" or "below" other elements or features would then be oriented "over" the other elements or features. Thus, the exemplary term "under" can encompass both an orientation of over and under. The device can also be oriented in the other direction, and the spatially relative terms used herein are intended to encompass such additional orientations. It is to be understood that the spatially relative terms used herein, including up, down, front, back, right, left, and the like, are intended to be interpreted as described above.

[0025] It will be understood that when an element is referred to as being "connected" to or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements can be present. In addition, the word "connected" or "coupled" as used herein can include electrically connected, communicatively connected, or the like.

[0026] As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising", or "includes" and / or "including" when used herein, specify the presence of stated features, integers, steps, operations, elements, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, parts, or combinations thereof.

[0027] As described in the background, the power data system in the prior art has the problem of low processing efficiency of power data, resulting in waste of computing power. The inventor found that the reason for this problem is that the existing power data system processes power data as soon as it is collected, regardless of whether there is duplicate data. However, power data may be collected repeatedly, resulting in a high probability of duplicate processing and waste of computing power.

[0028] Based on the above reasons, the present application provides a power data processing method, please refer to Figure 1 , the power data processing method comprises steps S102 to S106.

[0029] S102, obtaining power data.

[0030] It can be understood that the power data refers to the data obtained by data collection of each component in the power system, such as power equipment, power lines, etc. The power data includes device identification, data collection time and power operation data. The device identification is an identification code used to distinguish each component in the power system. The data collection time refers to the time when the power data is obtained, which can include the year of data collection to the second of data collection, for example, recording the data collection time in the form of year-month-day-hour-minute-second. The power operation data refers to various electrical signals when each component in the power system is running, such as phase current, phase voltage, etc.

[0031] S104, obtaining a feature parameter according to the power data, and determining whether the power data is stored in the data warehouse according to the feature parameter.

[0032] It can be understood that the data warehouse is a database used to store power data and the results of related downstream calculations of power data. Downstream calculations read power data from the data warehouse, so if the same data is stored in the warehouse, it will cause repeated calculations of duplicate data, resulting in waste of computing power. Therefore, it is necessary to determine whether the same power data has been stored in the data warehouse before storing the power data. Since each power data contains a lot of information, it is difficult and inefficient to compare the power data directly, and the data security of the power system has high requirements, therefore, various encryption algorithms can be used to process the power data into feature parameters, so that the feature parameters can map the power data containing more information in a shorter string. The data stored in the data warehouse also has corresponding feature parameters. Comparing the feature parameters of the power data to be stored with the feature parameters of the data stored in the data warehouse can determine whether the power data to be stored has been stored in the data warehouse.

[0033] S106, in the case where the power data is not stored in the data warehouse, storing the power data in the data warehouse, and performing ETL processing according to the data stored in the data warehouse.

[0034] It can be understood that the newly read power data can be stored in the data warehouse only when the power data is not stored in the data warehouse, so as to avoid repeated power data in the data warehouse. The power data system performs downstream calculation on the power data through ETL (Extract-Transform-Load) processing. The results obtained through ETL processing of a large amount of power data can facilitate the power system managers to evaluate the stability and reliability of the power system, and provide theoretical support for the power system managers to make decisions. Since the embodiment only processes the data in the data warehouse when the data is not repeated, the computing power for related downstream calculation is not wasted in the repeated collection or repeated transmission of data, thereby ensuring the reasonable use of computing power.

[0035] Based on the power data processing method in the embodiment, the mapping relationship between the feature parameters and the power data is used, and the efficient comparison between the feature parameters composed of short strings can determine whether the power data to be stored in the warehouse will be repeated with the data in the data warehouse. Moreover, only the non-repeated data in the data warehouse is calculated downstream, which ensures that the computing power is used in the business that really needs calculation, and greatly improves the data processing efficiency of the power data system.

[0036] In one embodiment, the power data includes device identification, data collection time and power operation data, and the feature parameters include first feature parameters and second feature parameters. Please refer to Figure 2 According to the power data, the feature parameters are obtained, and the step of judging whether the power data is stored in the data warehouse according to the feature parameters includes steps S202 to S206.

[0037] S202, the first feature parameter is obtained according to the device identification and the data collection time, and the second feature parameter is obtained according to the device identification, the data collection time and the power operation data.

[0038] It can be understood that the first feature parameter can distinguish which device the corresponding power data is collected from and when. The second feature parameter completely maps all the information of the power data.

[0039] S204, if there is no data matching the first feature parameter in the data warehouse, it is determined that the power data is not stored in the data warehouse.

[0040] Specifically, taking the first characteristic parameter reflecting the power data as the data of device A collected at T time as an example. The data warehouse does not have data matching the first characteristic parameter, which means that the data warehouse can have data of device A at T time other than the data, and the power data is not repeated with the data in the data warehouse. It can also mean that the data warehouse can have data of devices other than device A at T time, and the power data is not repeated with the data in the data warehouse. It can also mean that the data warehouse does not have data related to device A or T time, and the power data is not repeated with the data in the data warehouse. Therefore, in the above cases, it can be determined that the power data is not stored in the data warehouse.

[0041] S206, in the case that the data warehouse has data matching the first characteristic parameter, it is determined whether the data matching the first characteristic parameter has data matching the second characteristic parameter, and if not, it is determined that the power data is not stored in the data warehouse.

[0042] Specifically, taking the first characteristic parameter reflecting the power data as the data of device A collected at T time as an example. In the case that the data warehouse has data matching the first characteristic parameter, if the data also matches the second characteristic parameter, it means that the data is exactly the same as the power data to be stored in the warehouse, which is repeated power data. Since the collection of power data can be wrong, it is necessary to correct the data already stored in the warehouse. In the case that the data warehouse has data matching the first characteristic parameter, if the data does not match the second characteristic parameter, it means that the power data to be stored in the warehouse is re-collected and used to correct the data, which is still power data not stored in the data warehouse.

[0043] In one embodiment, please refer to Figure 3 In the case that the data warehouse has data matching the first characteristic parameter and the data matching the first characteristic parameter does not match the second characteristic parameter to determine that the power data is not stored in the data warehouse, the steps of storing the power data in the data warehouse and performing ETL processing according to the data stored in the data warehouse include steps S302 to S306.

[0044] S302, replace the data matching the first characteristic parameter in the data warehouse with the power data.

[0045] It can be understood that when the first characteristic parameter of the power data does not match the data in the data warehouse, the power data can be directly stored in the data warehouse. However, for the case that the data warehouse has data matching the first characteristic parameter and the data matching the first characteristic parameter does not match the second characteristic parameter, at this time, it is a correction to the data already stored in the warehouse, and the power data should be replaced with the power data re-collected.

[0046] S304, determining the target time interval.

[0047] The target time interval is a time interval in which the data collection time of the power data is located. It can be understood that some ETL processing is the statistical result obtained by processing the power data collected in a time interval, and therefore it is necessary to determine which time interval the replaced data belongs to.

[0048] S306, re-ETL processing of the data stored in the target time interval in the data warehouse.

[0049] It can be understood that after the target time interval is determined, since the data source for ETL processing has changed, it is necessary to re-ETL process the data in the changed target time interval to realize automatic updating of the data in the data warehouse.

[0050] In one embodiment, please refer to Figure 4 The step of obtaining the second feature parameter according to the device identifier, the data collection time and the power operation data includes steps S402 and S404.

[0051] S402, the device identifier, the data collection time and the power operation data are combined into a string.

[0052] It can be understood that the order of the string combined in step S402 is not limited in this embodiment, and can be the device identifier, the data collection time and the power operation data in turn, or other orders.

[0053] S404, calculating the Hash feature value of the string to obtain the second feature parameter.

[0054] It can be understood that Hash algorithm is one of the most widely used encryption algorithms at present, which can convert a string of arbitrary length into a fixed length string. In this embodiment, the feature value of the string obtained by the Hash algorithm is used as the second feature parameter. In one specific embodiment, the Hash feature value includes MD5 (Message-Digest Algorithm, Message Digest Algorithm) feature value, SHA256 (Secure Hash Algorithm, Secure Hash Algorithm) feature value, SHA1 feature value. It can be understood that the calculation speed of MD5 feature value is faster, but the encryption security is poorer. The calculation speed of SHA256 feature value is slower, but the encryption security is the best. The calculation speed and encryption security of SHA1 feature value are balanced. In use, the feature value can be selected according to the characteristics of the feature value.

[0055] In one embodiment, the step of re-ETL processing the data stored in the data warehouse in the target time interval comprises: calculating power statistics of the target time interval according to the data stored in the data warehouse in the target time interval. The power statistics include the highest voltage, the lowest voltage, the average voltage, the current load, the lowest load, and the highest load.

[0056] In one embodiment, the step of obtaining the power data comprises: reading the power data from the distributed message queue server. It can be understood that the distributed message queue server is configured with a distributed message queue. With the development of big data technology, in order to solve the problems brought by asynchronous processing, application decoupling, traffic shaping, and message communication, a large amount of data is often classified and stored through a distributed message queue to speed up processing.

[0057] It should be understood that, although Figures 1-4 The steps in the flowchart of the application are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figures 1-4 At least part of the steps in the flowchart of the application can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or steps or stages in other steps.

[0058] Referring to Figure 5 The embodiment of the application provides a power data system, which comprises a data warehouse server 10, a business server 30, a distributed message queue server 50, an access server 70, and a scheduling server 90. The above servers can be a single physical server, a physical server cluster, or a cloud server. The data warehouse server 10 is configured with a data warehouse. The business server 30 is used to output power data. The business server 30 is directly or indirectly connected with sensors arranged at different positions, collects the power data collected by the sensors, and outputs the power data. The distributed message queue server 50 is connected with the business server 30, and is used to store the power data output by the business server 30 in a distributed message queue. With the development of big data technology, in order to solve the problems brought by asynchronous processing, application decoupling, traffic shaping, and message communication, a large amount of data is often classified and stored through a distributed message queue to speed up processing. The scheduling server 90 is used to schedule the data warehouse server 10 to perform ETL processing according to the data stored in the data warehouse under the instruction of the access server 70.

[0059] The access server 70 is connected with the distributed message queue server 50 and the data warehouse server 10, and includes a memory and a processor. The memory stores a computer program, and the processor implements the following steps when executing the computer program: obtaining power data; obtaining a feature parameter according to the power data, and determining whether the power data is stored in the data warehouse according to the feature parameter; in the case that the power data is not stored in the data warehouse, storing the power data in the data warehouse, and performing ETL processing according to the data stored in the data warehouse. It can be understood that the access server 70 obtains the power data by reading the power data in the distributed message queue of the distributed message queue server 50, and then determines whether the power data needs to be stored according to the feature parameter. Finally, when ETL processing is needed, a control instruction is sent to the scheduling server 90 to instruct the scheduling server 90 to schedule the data warehouse server 10 to perform ETL processing.

[0060] Based on the power data system in the embodiment, by using the mapping relationship between the feature parameter and the power data, the efficient comparison between the feature parameters formed by the short strings can determine whether the power data to be stored in the warehouse will be repeated with the data in the data warehouse. Moreover, only the non-repeated data in the data warehouse is calculated downstream, which ensures that the computing power is used in the business that really needs to be calculated, and greatly improves the data processing efficiency of the power data system.

[0061] In some embodiments, the processor executes the computer program to implement the steps of the power data processing method in any of the above embodiments.

[0062] Please refer to Figure 6 The embodiment of the present application also provides a power data processing device, which includes a data acquisition module 110, a judgment module 130 and a processing module 150. The data acquisition module 110 is used to obtain power data. The judgment module 130 is used to obtain a feature parameter according to the power data, and determine whether the power data is stored in the data warehouse according to the feature parameter. The processing module 150 is used to store the power data in the data warehouse in the case that the power data is not stored in the data warehouse, and perform ETL processing according to the data stored in the data warehouse.

[0063] In one embodiment, the power data comprises a device identifier, a data collection time and power operation data, the feature parameters comprise a first feature parameter and a second feature parameter. The judging module 130 comprises a parameter calculation unit, a first judging unit and a second judging unit. The parameter calculation unit is configured to obtain the first feature parameter according to the device identifier and the data collection time, and obtain the second feature parameter according to the device identifier, the data collection time and the power operation data. The first judging unit is configured to determine that the power data is not stored in the data warehouse in a case that there is no data matching the first feature parameter in the data warehouse. The second judging unit is configured to determine whether there is data matching the second feature parameter in the data matching the first feature parameter in a case that there is data matching the first feature parameter in the data warehouse, and determine that the power data is not stored in the data warehouse if not.

[0064] In one embodiment, in a case that there is data matching the first feature parameter in the data warehouse and the data matching the first feature parameter does not match the second feature parameter to determine that the power data is not stored in the data warehouse, the processing module 150 is configured to replace the data matching the first feature parameter in the data warehouse with the power data, determine a target time interval, the target time interval is a time interval in which the data collection time of the power data is located, and perform ETL processing on data stored in the data warehouse in the target time interval again.

[0065] In one embodiment, the parameter calculation unit is configured to form a string by using the device identifier, the data collection time and the power operation data, and calculate a Hash feature value of the string to obtain the second feature parameter.

[0066] In one embodiment, the processing module 150 is configured to calculate power statistical data of the target time interval according to the data stored in the data warehouse in the target time interval. The power statistical data comprises a maximum voltage, a minimum voltage, an average voltage, a current load, a minimum load and a maximum load.

[0067] The specific limitations of the power data processing apparatus can refer to the limitations of the power data processing method described above, which will not be repeated here. Each module in the power data processing apparatus described above can be realized by software, hardware and combinations thereof in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0068] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0069] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0070] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for processing power data, characterized in that, include: Acquire power data; the power data includes device identification, data acquisition time, and power operation data; A first feature parameter is obtained based on the device identifier and the data acquisition time, and the device identifier, the data acquisition time, and the power operation data are combined into a string; Calculate the hash feature value of the string to obtain the second feature parameter; If no data matching the first feature parameter exists in the data warehouse, it is determined that the power data is not stored in the data warehouse; If data matching the first feature parameter exists in the data warehouse, it is determined whether data matching the second feature parameter exists among the data matching the first feature parameter. If not, it is determined that the power data is not stored in the data warehouse. If the power data is not stored in the data warehouse, the power data is stored in the data warehouse, and ETL processing is performed based on the data stored in the data warehouse; If data matching the first feature parameter exists in the data warehouse, but the data matching the first feature parameter does not match the second feature parameter, and it is determined that the power data is not stored in the data warehouse, the step of storing the power data in the data warehouse and performing ETL processing based on the data stored in the data warehouse includes: Replace the data in the data warehouse that matches the first feature parameter with the power data; Determine the target time interval; the target time interval is the time interval in which the power data was acquired. The data stored in the data warehouse for the target time interval is reprocessed using ETL.

2. The method for processing power data according to claim 1, characterized in that, The device identifier is an identification code used to distinguish the various components of the power system.

3. The method for processing power data according to claim 2, characterized in that, The power operation data refers to the electrical signals generated during the operation of each component of the power system.

4. The method for processing power data according to claim 3, characterized in that, The electrical signals include phase current and phase voltage.

5. The method for processing power data according to claim 4, characterized in that, The step of performing ETL processing on the data stored in the data warehouse at the target time interval includes: The power statistics for the target time interval are calculated based on the data stored in the data warehouse for the target time interval; the power statistics include the highest voltage, lowest voltage, average voltage, current load, lowest load, and highest load.

6. The method for processing power data according to claim 1, characterized in that, The steps for acquiring power data include: The power data is read from the distributed message queue server.

7. The method for processing power data according to claim 3, characterized in that, The hash features include MD5 features, SHA256 features, and SHA1 features.

8. A power data system, characterized in that, include: Data warehouse server, configured with a data warehouse; The business server is used to output power data; A distributed message queue server, connected to the business server, is used to store the power data output by the business server in the distributed message queue; An access server, connected to the distributed message queue server and the data warehouse server, includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the power data processing method according to any one of claims 1 to 7. A scheduling server is used to schedule the data warehouse server to perform ETL processing based on the data stored in the data warehouse, under the instruction of the access server.

9. A power data processing device, characterized in that, include: The data acquisition module is used to acquire power data; The power data includes equipment identification, data acquisition time, and power operation data; The judgment module is used to obtain a first feature parameter based on the device identifier and the data acquisition time, combine the device identifier, the data acquisition time, and the power operation data into a string, and calculate the hash feature value of the string to obtain a second feature parameter; If no data matching the first feature parameter exists in the data warehouse, it is determined that the power data is not stored in the data warehouse; if data matching the first feature parameter exists in the data warehouse, it is determined whether data matching the first feature parameter exists among the data matching the first feature parameter. If not, it is determined that the power data is not stored in the data warehouse. A processing module is configured to, when the power data is not stored in the data warehouse, store the power data in the data warehouse and perform ETL processing based on the data stored in the data warehouse; when data matching the first feature parameter exists in the data warehouse and the data matching the first feature parameter does not match the second feature parameter, determining that the power data is not stored in the data warehouse, the step of storing the power data in the data warehouse and performing ETL processing based on the data stored in the data warehouse includes: replacing the data matching the first feature parameter in the data warehouse with the power data; determining a target time interval; the target time interval being the time interval in which the power data was acquired; and re-performing ETL processing on the data stored in the data warehouse for the target time interval.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the power data processing method according to any one of claims 1 to 7.

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