A power grid load supervision data processing system based on cloud computing

Abstract

The present invention discloses a power grid load supervision data processing system based on cloud computing, which belongs to the technical field of power grid load supervision, and comprises a demand analysis module, a demand processing module, an acquisition module, a storage module and a data analysis module; the demand analysis module is used to perform load demand analysis and establish a demand statistics table; the demand processing module is used to perform demand processing analysis according to the demand statistics table, and determine the data analysis method and the adaptive acquisition method corresponding to the load analysis demand; the acquisition module collects load data in the power grid in real time; the storage module is used to store the load data, and classify the load data according to the load analysis demand to obtain demand monitoring data; the data analysis module is used to perform data analysis, and distribute the load analysis demand according to the analysis level; the demand monitoring data is analyzed according to the data analysis method to obtain the load monitoring result.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid load supervision, and in particular is a power grid load supervision data processing system based on cloud computing. Background Art

[0002] With the rapid development of power systems and the continuous advancement of smart grid construction, grid load data processing technology has become one of the key technologies for ensuring the safe, stable, and efficient operation of the power grid. However, existing grid load data processing systems still have certain shortcomings. For example, existing technologies have certain delays in data acquisition, transmission, and processing, resulting in insufficient real-time processing results. This can affect the real-time scheduling and operational decision-making of the power grid. Furthermore, due to the large amount and complexity of data, existing technologies may consume a large amount of computing resources and time during the data processing process. This reduces data processing efficiency and may limit the scope of application of grid load data processing technology.

[0003] Therefore, in order to efficiently process the power grid load supervision data, it has become an urgent problem to be solved. Based on this, the present invention provides a power grid load supervision data processing system based on cloud computing. Summary of the Invention

[0004] In order to solve the problems existing in the above solutions, the present invention provides a power grid load supervision data processing system based on cloud computing.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A cloud computing-based power grid load supervision data processing system includes a demand analysis module, a demand processing module, a collection module, a storage module, and a data analysis module;

[0007] The demand analysis module is used to perform load demand analysis, determine a number of load analysis requirements of the user for grid load supervision, and establish a demand statistics table based on the load analysis requirements;

[0008] The analysis level of the load analysis demand is determined in real time according to the demand statistics table, and the analysis level is marked accordingly in the demand statistics table.

[0009] Furthermore, the method for determining the load analysis requirements includes:

[0010] Establishing a demand reserve library, wherein the demand reserve library is used to store potential demands and demand background ranges corresponding to the potential demands;

[0011] A background judgment model is established, and the expression of the background judgment model is:

[0012]

[0013] Where: (BD, si) is the input data, BD is the power grid background, si is the demand background range of the corresponding potential demand in the demand reserve, i represents the corresponding potential demand in the demand reserve, i = 1, 2, ..., n, n is the number of potential demands in the demand reserve; BD → si indicates that the power grid background falls within the demand background range of the corresponding potential demand; the output data is the background matching value BQ(BD, si), which is 1 or 0;

[0014] Identify the user's power grid background, analyze the power grid background and demand reserve through the background judgment model, and obtain the background matching value of the corresponding potential demand;

[0015] The potential demand with a background matching value of 1 is marked as a recommended demand, and a recommendation information table is established according to the recommended demand; the recommendation information table is displayed to the user to determine the load analysis demand.

[0016] Furthermore, the method for determining the analysis level of the corresponding load analysis demand in real time according to the demand statistics table is as follows:

[0017] The platform presets each analysis level and the level value range corresponding to the analysis level; sets corresponding benchmark level values ​​for the load analysis requirements in the demand statistics table;

[0018] Acquire demand-related data of the load analysis demand in real time, analyze the demand-related data in real time using a preset level adjustment model, and obtain a level adjustment value of the load analysis demand;

[0019] The level positioning value of the corresponding load analysis requirement is calculated in real time according to the level calculation formula. The level calculation formula is:

[0020] DZ=DC+DLt;

[0021] Where: DZ is the level positioning value; DC is the reference level value; DLt is the level adjustment value at the corresponding time, and t is time;

[0022] The analysis level of the load analysis requirement is determined in real time according to the level positioning value and the level value range.

[0023] The demand processing module is used to perform demand processing analysis based on the demand statistics table, and determine the data analysis method and adaptive collection method corresponding to the load analysis demand.

[0024] Furthermore, the method for performing demand processing analysis based on the demand statistics table includes:

[0025] The platform establishes an analysis algorithm library and a collection reserve library. The analysis algorithm library is used to store data analysis methods applicable to corresponding load analysis requirements; the collection reserve library is used to store adaptive collection methods applicable to corresponding data analysis methods.

[0026] Identify the load analysis requirements in the demand statistics table, perform simulation analysis on the load analysis requirements based on the analysis algorithm library and the collection reserve library, and determine the data analysis method and adaptive collection method for the load analysis requirements.

[0027] Furthermore, the method for simulating and analyzing the load analysis requirements based on the analysis algorithm library and the acquisition reserve library includes:

[0028] Identify available data analysis methods and adaptive collection method combinations based on the analysis algorithm library and collection reserve library, mark them as reserve combinations, and add corresponding load analysis requirement tags to the reserve combinations;

[0029] Establishing a condition reserve library, the condition reserve library is used to store analysis condition data, the analysis condition data including a benchmark accuracy and a benchmark estimation method;

[0030] Matching corresponding reserve combinations according to the load analysis requirements, and matching corresponding analysis condition data from the condition reserve library according to the reserve combinations and the load analysis requirements;

[0031] The reserve combination is screened according to the analysis condition data to determine a target analysis combination, and the data analysis method and adaptive collection method of the load analysis requirement are determined according to the target analysis combination.

[0032] Furthermore, the method for establishing the conditional reserve library includes:

[0033] Acquire historical load monitoring data of the user, classify the historical load monitoring data according to the load analysis requirements, and form load material data for the load analysis requirements;

[0034] Performing simulation analysis on the load material data according to the reserve combination to obtain simulation accuracy and simulated usage duration of the reserve combination for the load analysis requirement; and setting a duration estimation method according to the simulated usage duration;

[0035] The simulation accuracy is marked as the benchmark accuracy, the duration estimation method is marked as the benchmark estimation method, the benchmark accuracy and the benchmark estimation method are integrated into analysis condition data, and the analysis condition data is marked with corresponding reserve combination and load analysis requirement labels; and a condition reserve library is established based on the analysis condition data.

[0036] Furthermore, the method for screening the reserve portfolio based on the analysis condition data includes:

[0037] Identify the reserve portfolio and analysis condition data corresponding to the load analysis requirement, identify the benchmark accuracy and benchmark prediction method corresponding to the analysis condition data, and determine the corresponding predicted usage duration according to the benchmark prediction method;

[0038] Identify the analysis level of the load analysis requirement according to the requirement statistical table;

[0039] Perform comparative analysis on the corresponding reserve portfolio according to the screening comparison formula to obtain the corresponding comparison and screening value. The screening comparison formula is:

[0040]

[0041] In the formula: SE is the comparison and screening value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1. The proportionality coefficients are determined according to the analysis level; GD1 and GD2 are the benchmark accuracies of the two reserve portfolios being compared respectively; YT1 and YT2 are the predicted usage durations of the two reserve portfolios being compared respectively;

[0042] Screen the reserve portfolio corresponding to the load analysis requirement according to the comparison and screening value to determine the target analysis portfolio.

[0043] The acquisition module acquires load data, determines the adaptive acquisition method for the load analysis requirement, and acquires the load data in the power grid in real time according to the adaptive acquisition method;

[0044] The storage module is used to store the load data, classify the load data according to the load analysis requirement, and obtain the demand monitoring data;

[0045] The data analysis module is used to perform data analysis, identify the analysis level of the load analysis requirement, and perform distributed processing on the load analysis requirement according to the analysis level; analyze the demand monitoring data according to the data analysis method of the load analysis requirement to obtain the load monitoring result.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] By introducing cloud computing technology, the present invention can leverage the cloud's powerful computing capabilities and distributed processing mechanisms to effectively shorten the delay time for data collection, transmission, and processing. This ensures the immediate update and rapid response of power grid load data, providing strong support for the real-time scheduling and operational decision-making of the power grid, thereby significantly improving the safety and stability of power grid operation. By optimizing the data processing process and improving processing efficiency, the present invention provides strong support for the intelligent management of the power grid. Real-time and accurate data processing results can provide key information for the automated control, fault prediction, energy efficiency management, etc. of the power grid, promoting the development of the power grid in a more intelligent and efficient direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] like Figure 1 As shown, a cloud computing-based power grid load supervision data processing system includes a demand analysis module, a demand processing module, a collection module, a storage module and a data analysis module;

[0052] The demand analysis module is used to perform load demand analysis, determine various load analysis requirements of users for grid load supervision, such as the change pattern of grid load, load forecasting and other load analysis requirements, and establish a demand statistics table based on the determined load analysis requirements;

[0053] The analysis level of the corresponding load analysis demand is determined in real time according to the demand statistics table, and the obtained analysis level is marked accordingly in the demand statistics table.

[0054] In one embodiment, the user may determine various load analysis requirements and upload the determined load analysis requirements.

[0055] In one embodiment, in order to facilitate users to fully and quickly determine various load analysis requirements, the following analysis method is used, including:

[0056] The platform conducts real-time demand statistics in the power grid management field, identifies various analytical requirements within the power grid management field that fall within the scope of power grid load monitoring, marks them as potential requirements, and sets a demand background range based on the power grid background of the corresponding potential requirements. The demand background range is used to indicate under what circumstances the power grid monitoring background will have this potential requirement, and statistical analysis can be performed based on the corresponding power grid background with this potential requirement.

[0057] Organize various potential demands and demand background ranges to establish a demand reserve library; and update the demand reserve library in real time according to data updates to ensure the comprehensiveness of the demands.

[0058] A background judgment model is established. The background judgment model is used to judge whether the current power grid background belongs to the corresponding demand background range. The expression of the background judgment model is:

[0059]

[0060] Where: (BD, si) is the input data, BD is the power grid background, si is the demand background range of the corresponding potential demand in the demand reserve, i represents the corresponding potential demand in the demand reserve, i = 1, 2, ..., n, n is the number of potential demands in the demand reserve; BD → si indicates that the power grid background falls within the demand background range of the corresponding potential demand; the output data is the background matching value BQ(BD, si), which is 1 or 0;

[0061] Identify the user's power grid background, analyze the power grid background and demand reserve through the background judgment model, and obtain the background matching value of the corresponding potential demand;

[0062] The potential demand with a background matching value of 1 is marked as a recommended demand, and a recommendation information table is established based on the recommended demand. The introduction information of the corresponding recommended demand can be supplemented in the recommended information table; the recommended information table is displayed to the user, and the user selects the corresponding recommended demand as the load analysis demand.

[0063] In one embodiment, a method for determining the analysis level of the corresponding load analysis demand in real time based on the demand statistics table is: using the existing method to determine the analysis level, the analysis level is determined based on the timeliness requirement of the corresponding load analysis demand, and the platform party presets several analysis levels and corresponding level introduction information based on the platform resources. Different analysis levels subsequently correspond to different service resources and timeliness. Service resources include computing resources (such as CPU, memory), storage resources, network resources, etc.; subsequent users can set the corresponding analysis level to meet the analysis requirements according to actual needs, and manually adjust the analysis level when it is necessary to adjust the analysis level.

[0064] In one embodiment, the method for determining the analysis level of the corresponding load analysis demand in real time according to the demand statistics table is:

[0065] The platform presets each analysis level and the corresponding level value range, such as 0-20 for the first level and 21-40 for the second level. The platform will set the specific range based on actual conditions.

[0066] The user sets the corresponding benchmark level value for each load analysis requirement. For example, if the user sets a load analysis requirement to the second level, the corresponding benchmark level value can be set in the range of 21-40 based on the degree of difference. The platform can also assist the user in setting the value.

[0067] Obtain demand-related data for each load analysis demand in real time. Demand-related data refers to data related to whether the load analysis demand has time-sensitive changes. For example, if a power grid abnormality requires timely analysis of a load analysis demand, the corresponding demand-related data is collected to adjust the benchmark level value.

[0068] Perform real-time analysis on demand-related data through a preset level adjustment model to obtain the level adjustment value of the corresponding load analysis demand;

[0069] The level positioning value of the corresponding load analysis requirement is calculated in real time according to the level calculation formula. The level calculation formula is:

[0070] DZ=DC+DLt;

[0071] Where: DZ is the level positioning value; DC is the reference level value; DLt is the level adjustment value at the corresponding time, and t is time;

[0072] The analysis level of the corresponding load analysis requirement is determined based on the level positioning value and the level value range of each analysis level.

[0073] In one embodiment, the level adjustment model is established based on existing intelligent technology, such as based on a neural network such as a CNN network or a DNN network. The platform sets a corresponding training set for training. The training set includes input data and output data. The input data is the demand-related data of the corresponding load analysis requirements; the output data is the level adjustment value, which is analyzed by the level adjustment model after successful training.

[0074] The demand processing module is used to perform demand processing analysis based on the demand statistics table, and determine the data analysis method and adaptive collection method corresponding to the corresponding load analysis demand.

[0075] The data analysis method is a specific analysis method that meets the load analysis requirements, such as traditional statistical methods, machine learning algorithms, deep learning models and other related data analysis methods.

[0076] The adaptive collection method is the data collection, processing, transmission and other methods for the data analysis method, which is used to minimize the collection time and improve timeliness.

[0077] In one embodiment, the demand processing analysis is performed according to the demand statistics table, and the analysis can be performed based on an existing method.

[0078] In one embodiment, a method for performing demand processing analysis based on a demand statistics table includes:

[0079] The platform will establish an analysis algorithm library based on the development of existing technologies. The analysis algorithm library is used to store data analysis methods for corresponding load analysis requirements. The analysis algorithm library will be regularly updated and maintained, and new algorithms and technologies will be introduced to ensure the advancement and applicability of the analysis algorithm library.

[0080] A collection reserve library is established based on the analysis algorithm library. The collection reserve library is used to store adaptive collection methods that adapt to the corresponding data analysis methods. That is, the load data can be directly analyzed by the data analysis method after collection and processing. The corresponding adaptive collection method is mainly set according to the collection equipment available on site, applicable data processing methods, etc.; data processing refers to the cleaning, integration and formatting of the collected data to meet the analysis requirements of subsequent data analysis methods.

[0081] The analysis algorithm library and collection reserve library are both established by the platform;

[0082] Identify the load analysis requirements in the demand statistics table, perform simulation analysis on the load analysis requirements based on the analysis algorithm library and collection reserve library, and determine the data analysis method and adaptive collection method corresponding to the corresponding load analysis requirements.

[0083] In one embodiment, a method for simulating and analyzing load analysis requirements based on an analysis algorithm library and a collection reserve library includes:

[0084] Determine various data analysis methods and adaptive collection method combinations based on the analysis algorithm library and collection reserve library, mark the corresponding combinations as reserve combinations, and label the reserve combinations with corresponding applicable load analysis requirement tags;

[0085] Obtain the user's historical load monitoring data, classify the historical load monitoring data according to the load analysis requirements, form historical load classification data corresponding to the corresponding load analysis requirements, and mark it as load material data, that is, classify it according to what data is needed to analyze the load analysis requirements;

[0086] According to the reserve combination, the corresponding load material data is simulated and analyzed to obtain the corresponding simulation accuracy and simulation usage time; that is, the simulation is performed according to the data analysis method and the adaptive collection method corresponding to the corresponding reserve combination, and the corresponding usage time from collection to analysis, as well as the accuracy of the corresponding simulation results, and a representative simulation accuracy and simulation usage time are determined by simulating multiple groups of data, such as using the average, mode, etc. for statistical analysis; the duration estimation method is set according to the simulation usage time, that is, the time required for collection and analysis using the reserve combination is subsequently estimated according to the duration evaluation method; a simulation adjustment model is established according to the above process, and the simulation adjustment model is used to subsequently adjust the corresponding simulation accuracy and duration estimation method according to the received analysis process data to make it more accurate. It is established using existing technology, and for example, it is established based on a neural network such as a CNN network or a DNN network. The platform establishes a corresponding training set for training, and the training set includes input data and output data. The input data is the analysis process data corresponding to the received corresponding reserve combination and load analysis requirements, and the output data is the adjusted simulation accuracy and duration estimation method;

[0087] Mark the simulation accuracy as the baseline accuracy, mark the duration estimation method as the baseline estimation method, integrate the baseline accuracy and baseline estimation method into analysis condition data, and label the analysis condition data with corresponding reserve combination and load analysis requirement tags; the model can be dynamically updated based on the simulation adjustment model later;

[0088] Establish a condition reserve based on the analysis of condition data;

[0089] Match the corresponding reserve combination according to the load analysis requirements, match the corresponding analysis condition data from the condition reserve library according to the reserve combination and load analysis requirements, screen the reserve combination according to the analysis condition data, determine the target analysis combination, and output the data analysis method and adaptation collection method corresponding to the target analysis combination as the data analysis method and adaptation collection method for the load analysis requirements.

[0090] In one embodiment, the method for screening a reserve portfolio according to analysis condition data includes:

[0091] Identify the reserve portfolio corresponding to the load analysis requirement and the analysis condition data, identify the benchmark accuracy and the benchmark prediction method corresponding to the analysis condition data, and predict the predicted usage duration corresponding to the current load analysis requirement according to the benchmark prediction method, that is, predict the duration required for collection and analysis according to this reserve portfolio in combination with the current load condition;

[0092] Identify the analysis level of the load analysis requirement according to the demand statistics table, match the corresponding proportionality coefficients b1 and b2 according to the analysis level, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1. Different analysis levels have different requirements for timeliness, so the proportionality coefficients are different, which are specifically preset by the platform side according to the actual needs of users;

[0093] Perform a comparative analysis on the corresponding reserve portfolio according to the screening comparison formula to obtain the corresponding comparison and screening value. The screening comparison formula is:

[0094]

[0095] In the formula: SE is the comparison and screening value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; GD1 and GD2 are the benchmark accuracies of the two reserve portfolios to be compared respectively; YT1 and YT2 are the predicted usage durations of the two reserve portfolios to be compared respectively;

[0096] Perform screening according to the comparison and screening values with each other to determine the target analysis portfolio.

[0097] The acquisition module acquires load data, determines the appropriate acquisition method for the corresponding load analysis requirement, and acquires the load data in the power grid in real time according to the corresponding appropriate acquisition method, including key parameters such as voltage, current, and power.

[0098] The storage module is used to store the load data acquired by the acquisition module, and classify the load data according to the corresponding load analysis requirements to obtain demand monitoring data;

[0099] The storage module uses cloud storage technology to achieve the storage and management of massive data. Cloud storage has the advantages of elastic scalability, high availability, and low cost, and can meet the growing demand for power grid load data.

[0100] The data analysis module is used to perform data analysis, identify the analysis level corresponding to the load analysis requirements, and distribute the corresponding load analysis requirements according to the analysis level, that is, using distributed processing technology and cloud computing resources to disperse data processing tasks to multiple servers or nodes for parallel execution; dynamically adjust the allocation of cloud computing resources according to data processing requirements to ensure processing speed and efficiency, and use existing technologies to achieve distributed processing; identify the data analysis method for load analysis requirements, analyze the corresponding demand monitoring data according to the data analysis method, and obtain corresponding load monitoring results.

[0101] The above formulas are all calculated by removing dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0102] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A cloud computing-based power grid load monitoring data processing system, characterized in that: It includes demand analysis module, demand processing module, acquisition module, storage module and data analysis module; The demand analysis module is used to perform load demand analysis, determine a number of load analysis requirements of the user for grid load supervision, and establish a demand statistics table based on the load analysis requirements; Determining the analysis level of the load analysis demand in real time according to the demand statistics table, and marking the analysis level accordingly in the demand statistics table; The demand processing module is used to perform demand processing analysis based on the demand statistics table and determine the data analysis method and adaptive collection method corresponding to the load analysis demand; The acquisition module is used to collect load data, determine the adaptive acquisition method required for the load analysis, and collect load data in the power grid in real time according to the adaptive acquisition method; The storage module is used to store the load data and classify the load data according to the load analysis requirements to obtain demand monitoring data; The data analysis module is used to perform data analysis, identify the analysis level of the load analysis requirement, and perform distributed processing on the load analysis requirement according to the analysis level; Analyze the demand monitoring data according to the data analysis method of the load analysis demand to obtain a load monitoring result; Methods for determining load analysis requirements include: Establishing a demand reserve library, wherein the demand reserve library is used to store potential demands and demand background ranges corresponding to the potential demands; A background judgment model is established, and the expression of the background judgment model is: ; Where: (BD, si) is the input data, BD is the grid background, si is the demand background range of the corresponding potential demand in the demand reserve, i represents the corresponding potential demand in the demand reserve, i = 1, 2, ..., n, n is the number of potential demands in the demand reserve; BD → si indicates that the grid background belongs to the demand background range of the corresponding potential demand; the output data is the background matching value BQ(BD, si), which is 1 or 0; Identify the user's power grid background, analyze the power grid background and demand reserve through the background judgment model, and obtain the background matching value of the corresponding potential demand; The potential demand with a background matching value of 1 is marked as a recommended demand, and a recommendation information table is established according to the recommended demand; the recommendation information table is displayed to the user to determine the load analysis demand.

2. A cloud computing-based power grid load monitoring data processing system according to claim 1, characterized in that: The method for determining the analysis level of the corresponding load analysis demand in real time based on the demand statistics table is: The platform presets each analysis level and the level value range corresponding to the analysis level; sets corresponding benchmark level values ​​for the load analysis requirements in the demand statistics table; Acquire demand-related data of the load analysis demand in real time, analyze the demand-related data in real time using a preset level adjustment model, and obtain a level adjustment value of the load analysis demand; The level positioning value of the corresponding load analysis requirement is calculated in real time according to the level calculation formula. The level calculation formula is: ; Where: DZ is the level positioning value; DC is the reference level value; DLt is the level adjustment value at the corresponding time, and t is time; The analysis level of the load analysis requirement is determined in real time according to the level positioning value and the level value range.

3. The cloud computing-based power grid load monitoring data processing system according to claim 1, characterized in that: Methods for demand processing analysis based on demand statistics include: The platform party establishes an analysis algorithm library and a collection reserve library. The analysis algorithm library is used to store data analysis methods applicable to corresponding load analysis requirements; the collection reserve library is used to store adaptation collection methods applicable to corresponding data analysis methods. Identify the load analysis requirements in the requirement statistics table, and perform simulation analysis on the load analysis requirements based on the analysis algorithm library and the collection reserve library to determine the data analysis method and adaptation collection method for the load analysis requirements.

4. A cloud computing-based power grid load monitoring data processing system according to claim 3, characterized in that: The method for performing simulation analysis on load analysis requirements based on the analysis algorithm library and the collection reserve library includes: Identify the combinations of data analysis methods and adaptation collection methods available in the analysis algorithm library and the collection reserve library, mark them as reserve combinations, and assign corresponding load analysis requirement labels to the reserve combinations. Establish a condition reserve library, which is used to store analysis condition data. The analysis condition data includes a benchmark accuracy and a benchmark estimation method. Match the corresponding reserve combination according to the load analysis requirements, and match the corresponding analysis condition data from the condition reserve library according to the reserve combination and the load analysis requirements. Filter the reserve combination according to the analysis condition data to determine the target analysis combination, and determine the data analysis method and adaptation collection method for the load analysis requirements according to the target analysis combination.

5. The cloud computing-based power grid load monitoring data processing system according to claim 4, characterized in that: The method for establishing the condition reserve library includes: Obtain the historical load monitoring data of the user, classify the historical load monitoring data according to the load analysis requirements to form load material data for the load analysis requirements. Perform simulation analysis on the load material data according to the reserve combination to obtain the simulation accuracy and simulation usage duration of the reserve combination for the load analysis requirements; set the duration estimation method according to the simulation usage duration. Mark the simulation accuracy as the benchmark accuracy, mark the duration estimation method as the benchmark estimation method, integrate the benchmark accuracy and the benchmark estimation method into analysis condition data, assign corresponding reserve combination and load analysis requirement labels to the analysis condition data; establish a condition reserve library according to the analysis condition data.

6. The cloud computing-based power grid load monitoring data processing system according to claim 4, characterized in that: The method for filtering the reserve combination according to the analysis condition data includes: Identify the reserve combination and analysis condition data corresponding to the load analysis requirements, identify the benchmark accuracy and benchmark estimation method corresponding to the analysis condition data, and determine the corresponding estimated usage duration according to the benchmark estimation method. Identify the analysis level of the load analysis requirements according to the requirement statistics table. Perform comparative analysis on the corresponding reserve combination according to the screening comparison formula to obtain the corresponding comparison screening value. Filter the reserve combination corresponding to the load analysis requirements according to the comparison screening value to determine the target analysis combination.

7. The cloud computing-based power grid load monitoring data processing system according to claim 6, characterized in that: The screening comparison formula is: ; In the formula: SE is the comparison screening value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1. The proportionality coefficients are determined according to the analysis level; GD1 and GD2 are the benchmark accuracies of the two reserve combinations being compared; YT1 and YT2 are the estimated usage durations of the two reserve combinations being compared respectively.

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