Energy consumption data processing method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the technical field of data processing, and provides an energy consumption data processing method and device, electronic equipment and a readable storage medium. The method comprises the steps of obtaining original energy consumption data sent by a meter, and performing granularity conversion on the original energy consumption data according to data granularity corresponding to a target task to obtain target energy consumption data; and determining an energy consumption calculation strategy corresponding to the target task, and processing the target energy consumption data according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task. According to the method, the original energy data reported by the meter can be comprehensively analyzed to obtain the energy consumption of the meter under various energy consumption calculation strategies, so that the original energy data is converted into the service energy consumption data which can be directly utilized, and the energy management efficiency is further improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an energy consumption data processing method, device, electronic device and readable storage medium. Background Art

[0002] The energy management platform is a highly integrated information system, and its core purpose is to significantly improve the overall efficiency of energy use through all-round monitoring, precise control, intelligent optimization, and efficient management of energy consumption of various meters. However, given the large number of meters connected to the current platform and the extremely large amount of raw energy consumption data that needs to be processed, the current energy management platform has not yet established a complete data processing process to effectively process the energy consumption data reported by the meters, and thus cannot directly convert these energy data into directly usable business data, resulting in low energy management efficiency. Summary of the invention

[0003] In view of this, the embodiments of the present application provide an energy consumption data processing method, device, electronic device and readable storage medium to solve the problem in the prior art that the energy data reported by the meter cannot be directly converted into directly usable business data, resulting in low energy management efficiency.

[0004] In a first aspect of an embodiment of the present application, a method for processing energy consumption data is provided, the method comprising:

[0005] Obtain the original energy consumption data sent by the meter, and convert the original energy consumption data into a granularity according to the data granularity corresponding to the target task to obtain the target energy consumption data; determine the energy consumption calculation strategy corresponding to the target task, process the target energy consumption data according to the energy consumption calculation strategy, and obtain the energy consumption data corresponding to the target task.

[0006] A second aspect of an embodiment of the present application provides an energy consumption data processing device, the method comprising:

[0007] An acquisition module is configured to acquire original energy consumption data sent by the meter, and perform granularity conversion on the original energy consumption data according to the data granularity corresponding to the target task to obtain target energy consumption data;

[0008] The processing module is configured to determine an energy consumption calculation strategy corresponding to the target task, process the target energy consumption data according to the energy consumption calculation strategy, and obtain the energy consumption data corresponding to the target task.

[0009] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0010] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0011] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0012] The original energy consumption data sent by the meter is obtained, and the original energy consumption data is converted into granularity according to the data granularity corresponding to the target task to obtain the target energy consumption data; the energy consumption calculation strategy corresponding to the target task is determined, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain the energy consumption data corresponding to the target task. By converting the obtained original energy consumption data into target energy consumption data with the same data granularity as that corresponding to the target task, and applying the energy consumption calculation strategy corresponding to the target task to calculate the energy consumption data corresponding to the target task, the original energy data reported by the meter can be comprehensively analyzed to obtain the energy consumption of the meter under various energy consumption calculation strategies, so as to convert the original energy data into directly usable business energy consumption data, thereby improving energy management efficiency. At the same time, the energy consumption of the target task can be more accurately grasped, thereby conducting more effective energy management and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 It is a flowchart of an energy consumption data processing method provided in an embodiment of the present application;

[0015] Figure 2 It is a structural schematic diagram of a topological structure provided in an embodiment of the present application;

[0016] Figure 3 It is a structural schematic diagram of an energy consumption data processing device provided in an embodiment of the present application;

[0017] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0019] An energy consumption data processing method, device, electronic device and readable storage medium according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0020] Figure 1 is a flow chart of an energy consumption data processing method provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0021] S101, obtaining original energy consumption data sent by the meter, and performing granularity conversion on the original energy consumption data according to the data granularity corresponding to the target task to obtain target energy consumption data;

[0022] S102, determining an energy consumption calculation strategy corresponding to the target task, and processing the target energy consumption data according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task.

[0023] Specifically, raw energy consumption data is data obtained from meters (such as electricity meters, water meters, gas meters, etc.), which usually represents the energy consumed by the equipment within a certain period of time. Data granularity refers to the level of detail or accuracy of raw energy consumption data. For example, in raw energy consumption data, the data granularity can be time units (such as seconds, minutes, hours, days, etc.) or spatial units (such as areas, buildings, floors, etc.). According to specific needs or tasks, the data is converted from one granularity to another. The raw energy consumption data after granularity conversion is the target energy consumption data, and its granularity matches the needs of the target task. Through granularity conversion, it can be ensured that the granularity of the data matches the subsequent analysis or processing tasks.

[0024] It can be understood that the energy consumption calculation strategy is a series of rules and methods for processing the target energy consumption data to generate energy consumption data related to the target task. When obtaining the original energy consumption data sent by the meter, first ensure that the communication between the energy management platform and the meter is stable and reliable. Once the original energy consumption data sent by the meter is obtained, the required data granularity is determined according to the specific requirements of the target task. For example, if the target task is to analyze the daily energy consumption, then the original energy consumption data needs to be converted from a finer time granularity (such as every hour) to daily data. Aggregation, interpolation or other mathematical operations can be used specifically, which are not specifically limited here.

[0025] It should be noted that when obtaining the original energy consumption data sent by the meter, the energy management platform can receive the data packaged on the meter in the form of a message queue to ensure the order, persistence and reliability of the data. Furthermore, the data received from the message queue is stored in the Doris database for granular conversion processing.

[0026] According to the technical solution provided in the embodiment of the present application, the original energy consumption data sent by the meter is obtained, and the original energy consumption data is converted into a granularity according to the data granularity corresponding to the target task to obtain the target energy consumption data; the energy consumption calculation strategy corresponding to the target task is determined, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain the energy consumption data corresponding to the target task. By converting the obtained original energy consumption data into target energy consumption data with the same data granularity as that corresponding to the target task, and applying the energy consumption calculation strategy corresponding to the target task to calculate the energy consumption data corresponding to the target task, the energy data reported by the meter can be converted into directly usable business data, thereby improving the efficiency of energy management. At the same time, the energy consumption situation of the target task can be more accurately grasped, thereby performing more effective energy management and optimization.

[0027] In some embodiments, the original energy consumption data is converted into target energy consumption data according to the data granularity corresponding to the target task, including: downsampling the original energy consumption data to convert the original energy consumption data of a first data granularity into target energy consumption data with a display granularity of a second data granularity, wherein the data granularity corresponding to the target task is the same as the second data granularity.

[0028] Specifically, downsampling can reduce the granularity of data by reducing the number of data points while retaining the main features of the data, especially when the frequency of data collection is much higher than the frequency required for data processing or display. In energy consumption data processing, downsampling can reduce the original energy consumption data from a higher time resolution (such as every minute, every hour) to a lower time resolution (such as every day, every week) to meet the target task's requirements for energy consumption data granularity. Specifically select an appropriate aggregation function to aggregate the original data, such as sum, average, maximum, minimum, etc. The specific method is not specifically limited here.

[0029] As an example, the reporting cycle of the meter is once every 5 minutes (first data granularity), which means that multiple data points are generated every hour. However, at this time, according to the target task, the minimum data granularity that the energy management platform needs to display is determined to be 15 minutes (second data granularity), which means that the platform does not need such a high frequency of data points for display or analysis. At this time, data downsampling processing is required. At this time, the average downsampling method can be used to group the original data according to a 15-minute cycle. Since the reporting cycle of the original data is 5 minutes, there will be 3 data points in each 15-minute cycle. Next, calculate the average of the 3 data points in each 15-minute cycle. Finally, use this average value to represent the data of the entire 15-minute cycle, and store or display this average value as a downsampled data point. Through this method, the reporting frequency of the data is successfully converted from once every 5 minutes (first data granularity) to once every 15 minutes (second data granularity), while retaining the main features of the data.

[0030] It should be noted that the first data granularity can also be an interval range rather than a fixed time interval. For example, in some cases, the reporting period of the meter may fluctuate according to actual needs or equipment status, and may be between 1 minute and 2 minutes. At this time, a 2-minute sliding window can be determined as the reference period for downsampling. For example, the data that should have been uploaded on 12.05 was actually uploaded on 12.04 or 12.06 due to meter errors. At this time, the data reported on 12.04 or 12.06 can be taken as the data uploaded on 12.05 according to the value range of the sliding window.

[0031] According to the technical solution provided in the embodiment of the present application, the original energy consumption data is downsampled to convert the original energy consumption data of the first data granularity into target energy consumption data of the second data granularity, wherein the data granularity corresponding to the target task is the same as the second data granularity. This can ensure that the data granularity matches the subsequent analysis or processing tasks, while improving the accuracy and reliability of the data.

[0032] In addition, in some embodiments, before downsampling the original energy consumption data, it also includes: if it is determined that there are missing values ​​in the original energy consumption data sent by the meter based on the data granularity of the original energy consumption data and the original energy consumption data, then it is determined whether there is a numerical value in the previous data granularity of the data granularity corresponding to the missing value; if so, the numerical value is filled in the missing value, and the target operation is performed on the numerical value after the missing value is filled in, if not, the data of the missing value is set to zero.

[0033] Specifically, when it is detected that there are missing values ​​in the original energy consumption data, in order to ensure the integrity and accuracy of the data, this embodiment adopts a missing value filling strategy based on the value of the previous data granularity. For example, when it is detected that there are missing values ​​in the original energy consumption data of a certain data granularity (for example, 5 minutes), first check whether there is a value in the previous data granularity (for example, the previous 5-minute cycle) of the data granularity corresponding to the missing value. If there is a value, the value can better represent the data state of the missing value, so it is directly filled in the position of the missing value. Then, the target operation is performed on the data after the missing value is filled, such as marking the value to determine the source of the data, so that the original data and the filled data can be distinguished in subsequent data analysis or processing.

[0034] In addition to marking the value, the filled-in value can also be smoothed to reduce mutations or noise that may be introduced due to data completion. It should be noted that if the filled-in value is significantly different from the surrounding data points, the interpolation method can be used to further adjust the value. The interpolation method can calculate the possible value of the missing value based on the values ​​of the adjacent data points. Furthermore, if there are missing values ​​in the previous data granularity, that is, there is no value to refer to, the data of the missing value should be set to zero. To prevent the introduction of additional noise or misleading information.

[0035] According to the technical solution provided in the embodiment of the present application, if it is determined that there are missing values ​​in the original energy consumption data sent by the meter based on the data granularity of the original energy consumption data and the original energy consumption data, it is determined whether there is a numerical value in the previous data granularity of the data granularity corresponding to the missing value; if so, the numerical value is filled in the missing value, and the target operation is performed on the numerical value after the missing value is filled in; if not, the data of the missing value is set to zero. The missing value filling strategy based on the numerical value of the previous data granularity can effectively handle the missing value problem in the original energy consumption data while maintaining data integrity and accuracy, thereby improving the accuracy of data analysis or processing.

[0036] In addition, in some embodiments, the energy consumption calculation strategy includes a time calculation strategy, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task, including: determining the standard time and non-standard time of the meter operation according to a preset business operation schedule corresponding to the meter; determining the standard energy consumption of the meter based on the target energy consumption data and the standard time, and determining the non-standard energy consumption of the meter based on the target energy consumption data and the non-standard time.

[0037] Specifically, the energy consumption calculation strategy includes a time calculation strategy. The time strategy generally refers to a strategy formulated according to the characteristics and needs of different time periods in business operations. In this embodiment, the time strategy is defined based on different time periods of the day, which are divided into standard time and non-standard time. Specifically, the standard time and non-standard time of the meter operation can be distinguished according to the preset business operation schedule. Standard time generally refers to the normal business operation time period, such as daytime working hours; and non-standard time may include night, holidays or other non-working time periods. After determining the standard time and non-standard time, the standard energy consumption and non-standard energy consumption of the meter can be calculated based on the target energy consumption data and these time periods. The standard energy consumption reflects the energy consumption of the meter during the normal business operation period, and the energy consumption level of the equipment under normal working conditions can be understood. Non-standard energy consumption reveals the energy consumption status of the meter during the non-working time period, including energy consumption information of the equipment in standby, maintenance or other abnormal working modes.

[0038] As an example, for example, the standard time is determined to be from 9 am to 6 pm according to the preset business operation schedule. During this time period, business activities may be more frequent, service demand may be higher, or the utilization rate of personnel and equipment may be higher. Non-standard time is relative to standard time. Non-standard time refers to a time period with fewer business activities, lower service demand, or lower utilization rate of personnel and equipment, such as from 6 pm to 9 am the next day. During the standard time (9 am to 6 pm), all equipment corresponding to the meter (such as air conditioners and lights) are in normal operation to meet business needs. The target energy consumption data generated during this time period is the standard energy consumption. During the non-standard time (6 pm to 9 am the next day), all equipment corresponding to the meter is in standby mode. However, due to the need for security monitoring, some lights and monitoring equipment may be kept running. The power consumption generated during this time period is the non-standard energy consumption, which is usually less than the standard energy consumption.

[0039] In addition, it should be noted that standard time and non-standard time can be adjusted based on seasonal changes and holiday changes to adapt to business needs and operating characteristics in different periods. For example, in winter, due to the low temperature, it may be necessary to start the heating equipment in advance, so the standard time may need to be advanced; and in summer, due to the high temperature, it may be necessary to extend the use time of air conditioners and other equipment, so the standard time may need to be postponed. Similarly, during holidays, due to the reduction of business activities, the standard time may also need to be adjusted accordingly. The specific division of standard time and non-standard time will not be elaborated here.

[0040] According to the technical solution provided by this embodiment, by distinguishing between standard energy consumption and non-standard energy consumption, the energy consumption performance of the meter in different time periods can be more accurately evaluated, so as to formulate a more effective energy management strategy. At the same time, it can be determined whether the device corresponding to the meter is abnormal, so as to optimize the operation time of the device. For example, if it is found that the energy consumption of a certain device in a non-standard time period is too high, it may be that the operation time of the device is too long, and it can be considered to adjust the device to enter standby mode or perform maintenance to reduce unnecessary energy consumption.

[0041] In some embodiments, the energy consumption calculation strategy includes a difference calculation strategy, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task, including: determining the topological relationship between meters, the topological relationship includes multiple levels of sub-items, each level of sub-items includes multiple total meters, and each total meter is connected to multiple sub-meters in the next level of sub-items; based on the target energy consumption data, multiple total meters in each level of sub-items in the multiple levels of sub-items, and multiple sub-meters, determine the difference energy consumption between meter levels.

[0042] Specifically, the difference calculation strategy can be used to evaluate the energy consumption differences between meters at different levels. In a complex energy management platform, there is usually a multi-level sub-item meter structure, each sub-item includes multiple master meters, and each master meter is connected to multiple sub-meters in the next level of sub-items. The structure makes the energy consumption data have differences between different levels, that is, the energy consumption data of the upper-level meter will include the energy consumption data of the lower-level meter, and will also include some additional energy consumption, such as transmission loss, management loss, etc.

[0043] It can be understood that this embodiment determines the topological relationship between the meters, that is, clarifies the connection relationship between the meters at each level. Then, based on the target energy consumption data and the multiple total meters and multiple sub-meters of each level of the multi-level sub-items, the differential energy consumption between the meter levels is calculated. The differential energy consumption reflects the difference in energy consumption of the total meter of a certain level of sub-items relative to the sub-meters in the next level of sub-items within a specific time period. The difference can determine whether the topological relationship of the meter is correctly connected and whether the meter is online. If the topological relationship of the device is not correctly connected or a meter is offline, the calculated differential energy consumption will be a large value or a negative value.

[0044] Combine the following Figure 2 This embodiment is exemplarily described as follows. Figure 2 As shown:

[0045] The figure is divided into three levels of sub-items, which are a hierarchical relationship, that is, some meters may be the total meters of other meters (project total table 1), and the meters in the next level of sub-items are their sub-meters (project total table 1 in the first level of sub-items corresponds to sales office 1, sales office 2, model room and landscape in the second level of sub-items). If the topological relationship is connected correctly, the energy consumption reading of the total meter should be equal to or slightly greater than the cumulative sum of the energy consumption readings of its sub-meters (taking into account transmission losses and measurement errors). If the difference is small, the topological relationship can be considered correct. If a meter is offline or fails, its energy consumption data may not be uploaded, resulting in a significant difference between the energy consumption reading of its total meter and the cumulative sum of the energy consumption readings of its sub-nodes. This difference will be a large value or a negative value. For example, in a certain period of time:

[0046] The target energy consumption data of Sales Office 1 is 1000kWh; the target energy consumption data of air conditioning in the corresponding three-level sub-items is 300kWh; the target energy consumption data of lighting is 600kWh; and the target energy consumption data of elevators is 50kW. Next, calculate the energy consumption difference of Sales Office 1: the difference between the energy consumption of Sales Office 1 (1000kWh) and the cumulative energy consumption of its corresponding air conditioning, lighting, and elevator (300kWh+600kWh+50kWh=950kWh) is 50kWh. This difference is very small, and it can be considered that the topological connection of A is correct and the sub-meters are all online. However, if the energy consumption of lighting is abnormally low (for example, 100kWh), then the energy consumption difference of A will be a large value (500kWh), and it can be judged that there may be problems with the lighting, such as offline or failure.

[0047] According to the technical solution provided in the embodiment of the present application, the application of the difference calculation strategy can provide a deeper understanding of the energy consumption of the energy management platform, discover possible energy consumption problems, and take corresponding measures to optimize them. For example, if it is found that the difference energy consumption of a certain level is abnormally high, it may be due to problems such as missed reading, wrong reading or transmission loss in the meter at that level, which requires further inspection and repair. At the same time, by comparing the difference energy consumption data of different levels, it is also possible to discover the energy consumption differences between different businesses or equipment, providing a basis for formulating more refined energy management strategies.

[0048] In addition, in some embodiments, the energy consumption calculation strategy includes an abnormal calculation strategy, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task, including: performing time series analysis on the target energy consumption data to determine the abnormal points of the target energy consumption data; based on the abnormal points of the target energy consumption data, determining the sending time interval of the target energy consumption data adjacent to the abnormal points; if the sending time interval is greater than a preset time threshold, determining that the target energy consumption data corresponding to the latter time is the abnormal energy consumption of the meter.

[0049] Specifically, the anomaly calculation strategy first performs a time series analysis on the target energy consumption data to find the anomaly points. After finding the anomaly point, the anomaly calculation strategy will further analyze the sending time interval of the energy consumption data adjacent to the point. Under normal circumstances, the meter will send the original energy consumption data according to the first data granularity. However, when the meter device fails or there is a problem with the network connection, the transmission of the data may be affected, resulting in a longer sending time interval. If the sending time interval of the energy consumption data adjacent to a certain anomaly point is greater than the preset time threshold (for example, 24 hours), that is, when the meter starts reporting data again after more than 24 hours without reporting data, the target energy consumption data corresponding to the first reported data will be marked as abnormal energy consumption.

[0050] It is understandable that after determining abnormal energy consumption, the energy management platform can automatically take appropriate measures to deal with it. For example, it can issue an alarm to notify the administrator to conduct manual inspection, or automatically start backup equipment to ensure the stability of energy supply. At the same time, the system can also adjust and optimize the energy management strategy based on historical data and current conditions to reduce the occurrence of similar problems.

[0051] According to the technical solution provided in the embodiment of the present application, a time series analysis is performed on the target energy consumption data to determine the abnormal points of the target energy consumption data; based on the abnormal points of the target energy consumption data, the sending time interval of the target energy consumption data adjacent to the abnormal points is determined; if the sending time interval is greater than the preset time threshold, the target energy consumption data corresponding to the latter time is determined to be the abnormal energy consumption of the meter. By calculating the abnormal energy consumption of the meter using the abnormal calculation strategy, the abnormal data existing in the energy management platform can be discovered and resolved in a timely manner, thereby improving the stability and reliability of the system.

[0052] In addition, in some embodiments, after obtaining the energy consumption data corresponding to the target task, it also includes: building an energy consumption knowledge base, storing the energy consumption data corresponding to the target task in the energy consumption knowledge base, and displaying the energy consumption data through a visual interface; receiving energy consumption feedback information input by the user based on the visual interface, and optimizing the meter according to the energy consumption feedback information.

[0053] Specifically, the energy consumption knowledge base is constructed to manage the collected energy consumption data in a systematic and structured manner, and to present it to users intuitively through a visual interface. In the energy consumption knowledge base, data can be classified and stored according to different target tasks, equipment types, time ranges and other dimensions, which is convenient for users to quickly query and analyze. At the same time, through the visual interface, users can intuitively see charts and curves of various energy consumption data, so as to better understand the energy consumption of the energy management platform.

[0054] In addition, users can input energy consumption feedback information based on the visual interface. Based on this feedback information, the energy management platform can optimize and adjust the meter in a targeted manner, such as adjusting equipment parameters, updating management strategies, improving data collection and transmission methods, etc., so as to improve energy efficiency and reduce energy consumption losses. When users find that the energy consumption data of a meter is abnormal, they can provide feedback through the visual interface. After receiving the energy consumption feedback information input by the user based on the visual interface, the platform will optimize the meter based on this information. For example, if the energy consumption of a meter is found to be abnormally high, the platform can automatically adjust the operating parameters of the corresponding equipment of the meter, or prompt the user to perform manual inspection and maintenance. In this way, the platform can continuously improve the efficiency and quality of energy management.

[0055] According to the technical solution provided in the embodiment of the present application, the energy management platform can realize comprehensive monitoring and management of energy usage by building an energy consumption knowledge base, displaying energy consumption data, and receiving user feedback. Not only can the problems and abnormal data in the energy management platform be discovered and solved in a timely manner, but also targeted optimization and improvement can be carried out according to user feedback and suggestions, thereby improving energy efficiency, reducing energy consumption costs, and improving energy consumption management efficiency.

[0056] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the process of the embodiments of the present application.

[0057] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described in detail here.

[0058] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0059] Figure 3 Schematic diagram of the structure of an energy consumption data processing device provided in an embodiment of the present application. Figure 3 As shown, the device comprises:

[0060] The acquisition module 301 is configured to acquire the original energy consumption data sent by the meter, and perform granularity conversion on the original energy consumption data according to the data granularity corresponding to the target task to obtain the target energy consumption data;

[0061] The processing module 302 is configured to determine an energy consumption calculation strategy corresponding to the target task, and process the target energy consumption data according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task.

[0062] In some embodiments, the acquisition module 301 is also used to downsample the original energy consumption data to convert the original energy consumption data of the first data granularity into target energy consumption data with a display granularity of the second data granularity, wherein the data granularity corresponding to the target task is the same as the second data granularity.

[0063] In some embodiments, the acquisition module 301 is also used to determine whether there is a missing value in the original energy consumption data sent by the meter based on the data granularity of the original energy consumption data and the original energy consumption data. If so, the missing value is filled with the value, and the target operation is performed on the value after the missing value is filled. If not, the data of the missing value is set to zero.

[0064] In some embodiments, the processing module 302 is also used to determine the standard time and non-standard time of the meter operation according to a preset business operation schedule corresponding to the meter; determine the standard energy consumption of the meter based on the target energy consumption data and the standard time, and determine the non-standard energy consumption of the meter based on the target energy consumption data and the non-standard time.

[0065] In some embodiments, the processing module 302 is also used to determine the topological relationship between the meters, where the topological relationship includes multiple levels of sub-items, each level of sub-items includes multiple total meters, and each total meter is connected to multiple sub-meters in the next level of sub-items; based on the target energy consumption data, the multiple total meters and the multiple sub-meters in each level of the multi-level sub-items, the differential energy consumption between the meter levels is determined.

[0066] In some embodiments, the processing module 302 is also used to perform time series analysis on the target energy consumption data to determine the abnormal points of the target energy consumption data; based on the abnormal points of the target energy consumption data, determine the sending time interval of the target energy consumption data adjacent to the abnormal points; if the sending time interval is greater than a preset time threshold, determine that the target energy consumption data corresponding to the next time is the abnormal energy consumption of the meter.

[0067] In some embodiments, the processing module 302 is also used to build an energy consumption knowledge base, store the energy consumption data corresponding to the target task in the energy consumption knowledge base, and display the energy consumption data through a visual interface; receive energy consumption feedback information input by the user based on the visual interface, and optimize the meter according to the energy consumption feedback information.

[0068] Figure 4 Schematic diagram of an electronic device 4 provided in an embodiment of the present application. Figure 4As shown, the electronic device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0069] The electronic device 4 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4 The electronic device 4 is merely an example and does not limit the electronic device 4 , and may include more or less components than those shown in the figure, or different components.

[0070] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0071] The memory 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. The memory 402 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0072] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0073] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The readable storage medium may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for processing energy consumption data, characterized in that: include: Obtaining the original energy consumption data sent by the meter, and performing granularity conversion on the original energy consumption data according to the data granularity corresponding to the target task to obtain the target energy consumption data; An energy consumption calculation strategy corresponding to the target task is determined, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task.

2. The method according to claim 1, characterized in that: The step of converting the original energy consumption data into a granularity according to the data granularity corresponding to the target task to obtain the target energy consumption data includes: The original energy consumption data is downsampled to convert the original energy consumption data of the first data granularity into the target energy consumption data with a display granularity of the second data granularity, wherein the data granularity corresponding to the target task is the same as the second data granularity.

3. The method according to claim 2, characterized in that Before downsampling the original energy consumption data, the method further includes: If it is determined that the original energy consumption data sent by the meter has missing values ​​according to the data granularity of the original energy consumption data and the original energy consumption data, then it is determined whether there is a value in the previous data granularity of the data granularity corresponding to the missing value; If so, the missing value is filled in with the numerical value, and the target operation is performed on the numerical value after the missing value is filled in. If not, the data of the missing value is set to zero.

4. The method according to claim 1, characterized in that The energy consumption calculation strategy includes a time calculation strategy, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain the energy consumption data corresponding to the target task, including: Determining the standard time and non-standard time for the operation of the meter according to a preset service operation schedule corresponding to the meter; The standard energy consumption of the meter is determined based on the target energy consumption data and the standard time, and the non-standard energy consumption of the meter is determined based on the target energy consumption data and the non-standard time.

5. The method according to claim 1, characterized in that The energy consumption calculation strategy includes a difference calculation strategy, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain the energy consumption data corresponding to the target task, including: Determine a topological relationship between the meters, wherein the topological relationship includes multiple levels of sub-items, each level of sub-items includes multiple master meters, and each master meter is connected to multiple sub-meters in the next level of sub-items; Based on the target energy consumption data, the plurality of total meters of each level of the multi-level sub-items, and the plurality of sub-meters, the differential energy consumption between the meter levels is determined.

6. The method according to claim 1, characterized in that The energy consumption calculation strategy includes an abnormal calculation strategy, and the target energy consumption data is processed according to the energy consumption calculation strategy to obtain the energy consumption data corresponding to the target task, including: Performing time series analysis on the target energy consumption data to determine abnormal points of the target energy consumption data; Based on the abnormal point of the target energy consumption data, determining the time interval for sending the target energy consumption data adjacent to the abnormal point; If the sending time interval is greater than a preset time threshold, it is determined that the target energy consumption data corresponding to the next time is the abnormal energy consumption of the meter.

7. The method according to any one of claims 1 to 6, characterized in that: After obtaining the energy consumption data corresponding to the target task, the method further includes: Building an energy consumption knowledge base, storing the energy consumption data corresponding to the target task in the energy consumption knowledge base, and displaying the energy consumption data through a visual interface; Receive energy consumption feedback information input by a user based on the visual interface, and optimize the meter according to the energy consumption feedback information.

8. An energy consumption data processing device, characterized in that: include: An acquisition module is configured to acquire original energy consumption data sent by the meter, and perform granularity conversion on the original energy consumption data according to the data granularity corresponding to the target task to obtain target energy consumption data; The processing module is configured to determine an energy consumption calculation strategy corresponding to the target task, and process the target energy consumption data according to the energy consumption calculation strategy to obtain energy consumption data corresponding to the target task.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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