Electrical variable correction method and system based on new energy measurement

Abstract

According to the new energy measurement-based electrical variable correction method and system provided by the invention, the data splicing processing is performed on the new energy electric energy conversion description data set corresponding to the new energy power generation equipment measurement electrical variable data through the first analysis instruction data to obtain the first data splicing result; and performing variable correction processing on the measurement electric variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result. All data can be processed as far as possible to guarantee the integrity of the data only by performing data splicing and data testing on the instruction description content in the new energy electric energy conversion description data set corresponding to the measurement electric variable data of the new energy power generation equipment, and disturbance interference and the like can also be avoided; therefore, the variable correction processing efficiency and accuracy are improved.

Description

Technical Field

[0001] This application relates to the technical field of new energy measurement and correction of electrical variables. Specifically, it relates to a new energy measurement electrical variable correction method and system. Background Art

[0002] New energy refers to renewable energy that is systematically developed and utilized based on new technologies, such as solar energy, wind energy, biomass energy, geothermal energy, ocean energy, hydrogen energy, nuclear energy, etc. New energy power generation is the process of using existing technologies to generate electricity through the above-mentioned new energy sources. However, new energy power generation has characteristics such as intermittency, randomness, and volatility. To achieve safe and stable power consumption, it is necessary to be equipped with a sufficient number and sufficient flexibility of regulating power sources.

[0003] When new energy is converted into electrical energy and stored in a battery, it is necessary to measure its electrical variables to determine the allowable charge of the battery. This requires the use of an electrical variable measurement device for measurement. The electrical variable measurement device includes a measuring instrument, measuring wires, and measuring clips. When in use, the measuring wires need to be connected to the measuring instrument, and then the measuring clip at the other end of the measuring wire is clamped on the battery to be measured, and then the staff can measure through the measuring instrument. However, during the measurement process, due to equipment failures or dust, etc., there may be problems with inaccurate new energy measurement (for example: equipment abnormalities include abnormalities of the electrical variable measurement device and the battery, so the specific amount of electricity in the battery cannot be detected, and thus accurate measurement cannot be performed; when dust adheres to the connection end, power loss and loss problems may occur during measurement, so the power of the battery cannot be accurately measured), which may lead to inaccurate determination of the conversion power. To improve the detection accuracy of the conversion power of new energy, a new energy measurement electrical variable correction method is urgently needed to overcome the above technical problems. Summary of the Invention

[0004] To improve the technical problems existing in the related art, this application provides a new energy measurement electrical variable correction method and system.

[0005] In a first aspect, a method for correcting electrical variables based on new energy measurement is provided. The method includes: obtaining electrical variable measurement data of a new energy power generation device to be processed, and inputting the electrical variable measurement data of the new energy power generation device into a new energy power conversion description data set corresponding to the electrical variable measurement data of the new energy power generation device according to the data category of the electrical variable measurement data of the new energy power generation device and the target information of the new energy power generation device object; obtaining prediction instruction information corresponding to the electrical variable measurement data of the new energy power generation device and first analysis instruction data corresponding to the prediction instruction information; performing data splicing processing on the new energy power conversion description data set corresponding to the electrical variable measurement data of the new energy power generation device through the first analysis instruction data to obtain a first data splicing result; and performing variable correction processing on the electrical variable measurement data of the new energy power generation device through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result.

[0006] The beneficial effect of this application is that after obtaining the electrical variable measurement data of the new energy power generation device to be processed, the electrical variable measurement data of the new energy power generation device is input into a new energy power conversion description data set corresponding to the data category and the target information of the new energy power generation device object according to the data category of the electrical variable measurement data of the new energy power generation device and the target information of the new energy power generation device object. Since the new energy power conversion description data set to be stored is determined based on the data category and the target information of the new energy power generation device object when storing, that is, the electrical variable measurement data of the new energy power generation device is stored in the database, and then the prediction instruction information corresponding to the electrical variable measurement data of the new energy power generation device and the first analysis instruction data corresponding to the prediction instruction information are obtained. Then, data splicing processing is performed on the new energy power conversion description data set corresponding to the electrical variable measurement data of the new energy power generation device through the first analysis instruction data to obtain a first data splicing result, and then variable correction processing is performed on the electrical variable measurement data of the new energy power generation device through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result. In the embodiment of this application, when performing variable correction processing, only the instruction description content in the new energy power conversion description data set corresponding to the electrical variable measurement data of the new energy power generation device needs to be subjected to data splicing and data testing, which can process all data as much as possible to ensure the integrity of the data, and can also avoid disturbance interference, etc., thereby improving the efficiency and accuracy of variable correction processing.

[0007] In this application, the data category of the electrical variable data measured by the new energy power generation equipment and the object target information of the new energy power generation equipment input the electrical variable data measured by the new energy power generation equipment into the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation equipment, including: obtaining the power generation architecture parameters of the electrical variable data measured by the new energy power generation equipment, and predicting the power generation architecture parameters to obtain a second variable correction processing result; when determining that the second variable correction processing result passes the prediction, generating a power generation architecture directory of the electrical variable data measured by the new energy power generation equipment; determining one or more new energy power conversion description data sets corresponding to the electrical variable data measured by the new energy power generation equipment through the data category of the electrical variable data measured by the new energy power generation equipment and the object target information of the new energy power generation equipment; respectively inputting the electrical variable data measured by the new energy power generation equipment and the power generation architecture directory into each new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation equipment, and the new energy power conversion description data set is stored in the cache space.

[0008] The beneficial effect of this application is that before inputting the electrical variable data measured by the new energy power generation equipment into the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation equipment, first predict the power generation architecture parameters of the electrical variable data measured by the new energy power generation equipment. After determining that the power generation architecture parameters pass the prediction, then input the electrical variable data measured by the new energy power generation equipment into the corresponding new energy power conversion description data set through the data category of the electrical variable data measured by the new energy power generation equipment and the object target information of the new energy power generation equipment. And the new energy power conversion description data set is stored in the high-performance storage space, which can improve the reading and writing efficiency of the data, thereby improving the execution efficiency of the variable correction processing.

[0009] In this application, the data splicing process is performed on the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation equipment through the first analysis instruction data to obtain a first data splicing result, including: obtaining the correction kernel information corresponding to the first analysis instruction data, and determining the data correction result according to the correction kernel information; determining one or more instruction description contents through the first analysis instruction data; determining the characteristic coefficients corresponding to each instruction description content from the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation equipment according to the data correction result; using the characteristic coefficients corresponding to each instruction description content as the first data splicing result.

[0010] The beneficial effects of this application are as follows: When performing data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data, the problem of inaccurate data splicing is improved, so that the first data splicing result can be accurately obtained.

[0011] In this application, the variable correction processing of the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain the first variable correction processing result includes: determining the threshold corresponding to each instruction description content and the matching relationship between each instruction description content through the prediction instruction information; performing variable correction processing on the measured electrical variable data of the new energy power generation equipment through the threshold corresponding to each instruction description content, the characteristic coefficient, and the matching relationship to obtain the first variable correction processing result.

[0012] The beneficial effects of this application are as follows: When performing variable correction processing on the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information, the problem of inaccurate testing is improved, so that the first variable correction processing result can be accurately obtained.

[0013] In this application, the method further includes: obtaining the new energy power conversion element parameters of the measured electrical variable data of the new energy power generation equipment, and predicting the new energy power conversion element parameters to obtain the third variable correction processing result; determining whether the third variable correction processing result is predicted to pass, wherein when the third variable correction processing result is predicted to pass, performing data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data to obtain the first data splicing result; when the third variable correction processing result is predicted to fail, rejecting the processing of the measured electrical variable data of the new energy power generation equipment.

[0014] The beneficial effects of this application are as follows: By predicting multiple variable correction processing results, the reliability of determining whether the third variable correction processing result is predicted to pass can be improved.

[0015] In this application, the method further includes: when the first variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment meets the prediction instruction information, obtaining a pre-configured enhanced prediction algorithm, and sending enhanced prediction instruction data to the power correction data processing terminal corresponding to the measured electrical variable data of the new energy power generation equipment to perform enhanced prediction on the measured electrical variable data of the new energy power generation equipment; or, rejecting the processing of the measured electrical variable data of the new energy power generation equipment; or, annotating the measured electrical variable data of the new energy power generation equipment.

[0016] The beneficial effects of this application are as follows: strengthening the prediction of the processing result of the first variable can improve the accuracy of the prediction.

[0017] In this application, the method further includes: obtaining the number of annotations for the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment; when the number of annotations reaches the specified value configured in advance, determining the abnormal annotation data, and using the abnormal annotation data to debug the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment.

[0018] The beneficial effects of this application are as follows: debugging the object directory of the new energy power generation equipment through the accurate number of annotations can improve the accuracy of the debugging.

[0019] In this application, the method further includes: obtaining the re-prediction instruction information corresponding to the measured electrical variable data of the new energy power generation equipment and the second analysis instruction data corresponding to the re-prediction instruction information; performing data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the second analysis instruction data to obtain a second data splicing result; performing variable correction processing on the measured electrical variable data of the new energy power generation equipment through the second data splicing result and the re-prediction instruction information to obtain a fourth variable correction processing result; performing optimization or debugging processing on the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment through the fourth variable correction processing result.

[0020] The beneficial effects of this application are as follows: by accurately obtaining the second data splicing result, the accuracy and reliability of the optimization or debugging processing of the object directory of the new energy power generation equipment can be improved.

[0021] In this application, the optimization or debugging process of the measured electrical variable data of the new energy power generation equipment by using the fourth variable correction processing result includes: when the fourth variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment meets the re-prediction instruction information, determining the debugging method for the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment, and performing debugging processing on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment through the debugging method; when the fourth variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment does not meet the re-prediction instruction information, determining whether the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment is in a debugging state; if the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment is in a debugging state, determining the optimization method for the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment, and performing optimization processing on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment through the optimization method.

[0022] The beneficial effect of this application is that when it is determined that the re-prediction instruction information is met by using the fourth variable correction processing result, the debugging process can be performed on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment, and when it is determined that the re-prediction instruction information is not met, the debugging process can be performed on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment, so as to improve the flexibility and comprehensiveness of prediction detection based on re-prediction.

[0023] In this application, the method further includes: in response to the training step for the first profiling instruction data, obtaining each instruction description content in the first profiling instruction data; obtaining the specified step for the threshold of each instruction description content, and obtaining the threshold of each instruction description content; obtaining the matching relationship for different instruction description contents; and determining the prediction instruction information through each instruction description content, the threshold of each instruction description content, and the matching relationship.

[0024] The beneficial effect of this application is that the matching relationship for different instruction description contents can be accurately obtained, so as to improve the accuracy of the prediction instruction information.

[0025] In a second aspect, a new energy measurement electrical variable correction system is provided, including a processor and a memory that communicate with each other. The processor is configured to read and execute a computer program from the memory to implement the above method.

[0026] The new - energy - based measurement electrical variable correction method and system provided by the embodiments of the present application, after obtaining the measurement electrical variable data of the new - energy power generation equipment to be processed, input the measurement electrical variable data of the new - energy power generation equipment into the new - energy power conversion description data set corresponding to the data category and the target information of the new - energy power generation equipment object according to the data category of the measurement electrical variable data of the new - energy power generation equipment and the target information of the new - energy power generation equipment object. Since the new - energy power conversion description data set to be stored is determined based on the data category and the target information of the new - energy power generation equipment object when storing, that is, the measurement electrical variable data of the new - energy power generation equipment is stored in the database. Then, obtain the prediction instruction information corresponding to the measurement electrical variable data of the new - energy power generation equipment and the first analysis instruction data corresponding to the prediction instruction information. Then, perform data splicing processing on the new - energy power conversion description data set corresponding to the measurement electrical variable data of the new - energy power generation equipment through the first analysis instruction data to obtain the first data splicing result. Then, perform variable correction processing on the measurement electrical variable data of the new - energy power generation equipment through the first data splicing result and the prediction instruction information to obtain the first variable correction processing result. In the embodiments of the present application, when performing variable correction processing, only need to perform data splicing and data testing on the instruction description content in the new - energy power conversion description data set corresponding to the measurement electrical variable data of the new - energy power generation equipment, which can process all data as much as possible to ensure the integrity of the data, and can also avoid disturbance interference, etc., thereby improving the efficiency and accuracy of variable correction processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a flowchart of a new - energy - based measurement electrical variable correction method provided by the embodiments of the present application.

[0029] Figure 2 It is an architecture diagram of a new - energy - based measurement electrical variable correction system provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To better understand the above technical solution, the technical solution of this application will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations on the technical solution of this application. Without conflict, the technical features in the embodiments of this application and the embodiments can be combined with each other.

[0031] Please refer to Figure 1 , which shows a new energy measurement electrical variable correction method. This method may include the technical solutions described in the following steps S101 - S104.

[0032] Step S101: Obtain the measured electrical variable data of the new energy power generation equipment to be processed, and input the measured electrical variable data of the new energy power generation equipment into the corresponding new energy power conversion description data set according to the data category of the measured electrical variable data of the new energy power generation equipment and the target information of the new energy power generation equipment object.

[0033] Furthermore, the data category can be specifically understood as the type of data that affects the new energy conversion power.

[0034] After the power correction data processing terminal triggers the measured electrical variable data of the new energy power generation equipment based on the obtained steps, after obtaining the measured electrical variable data of the new energy power generation equipment, send the measured electrical variable data of the new energy power generation equipment to the data processing server to perform variable correction processing on the measured electrical variable data of the new energy power generation equipment. After obtaining the measured electrical variable data of the new energy power generation equipment, obtain the data category of the measured electrical variable data of the new energy power generation equipment and the target information of the new energy power generation equipment object. The target information of the new energy power generation equipment object includes: solar photovoltaic panels, wind turbines, etc.

[0035] After obtaining the data category of the measured electrical variable data of the new energy power generation equipment and the target information of the new energy power generation equipment object, then obtain the real - time data category and the target information of the new energy power generation equipment object in the database for storing the measured electrical variable data of the new energy power generation equipment, and then store the measured electrical variable data of the new energy power generation equipment into the corresponding new energy power conversion description data set based on the data category of the measured electrical variable data of the new energy power generation equipment, the target information of the new energy power generation equipment object, and each real - time data dimension.

[0036] Step S102: Obtain the prediction instruction information corresponding to the measured electrical variable data of the new energy power generation equipment and the first analysis instruction data corresponding to the prediction instruction information.

[0037] Among them, the parsing requirement can be understood as the prediction requirement, specifically, it can be understood as the prediction requirement for the measured electrical variable data of the new energy power generation equipment.

[0038] Before this step, one or more prediction methods are preset for different data categories. When implementing this step, one or more prediction instruction information corresponding to the electrical variable data measured by the new energy power generation device can be determined based on the data category of the electrical variable data measured by the new energy power generation device. Each prediction instruction information includes one or more prediction requirements, and each prediction requirement corresponds to analysis instruction data. When the prediction instruction information corresponding to the electrical variable data measured by the new energy power generation device is determined, the first analysis instruction data corresponding to the prediction instruction information can be obtained.

[0039] Step S103: Perform data splicing processing on the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation device through the first analysis instruction data to obtain a first data splicing result.

[0040] Among them, data splicing processing can be understood as data fusion processing, integration processing and other methods.

[0041] When implementing this step, first obtain the correction kernel information corresponding to the data category, determine the data correction result according to the correction kernel information, and then determine the characteristic coefficients corresponding to each instruction description content in the first analysis instruction data from the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation device, so as to obtain the first data splicing result.

[0042] Step S104: Perform variable correction processing on the electrical variable data measured by the new energy power generation device through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result.

[0043] When implementing this step, it can be determined whether the characteristic coefficients of each instruction description content in the first data splicing result meet the prediction requirements in the prediction instruction information. If the prediction requirements are met, a first variable correction processing result that meets the prediction instruction information is obtained. If the prediction requirements are not met, a first variable correction processing result that does not meet the prediction instruction information is obtained.

[0044] In the new energy measurement electrical variable correction method provided by the embodiments of the present application, after obtaining the new energy power generation equipment measurement electrical variable data to be processed, the new energy power generation equipment measurement electrical variable data is input into the new energy power conversion description data set corresponding to the data category and the new energy power generation equipment object target information. Since the new energy power conversion description data set to be stored is determined based on the data category and the new energy power generation equipment object target information during storage, that is, the new energy power generation equipment measurement electrical variable data is stored in the database. Then, the prediction instruction information corresponding to the new energy power generation equipment measurement electrical variable data and the first analysis instruction data corresponding to the prediction instruction information are obtained. Then, the data splicing process is performed on the new energy power conversion description data set corresponding to the new energy power generation equipment measurement electrical variable data through the first analysis instruction data to obtain the first data splicing result. Then, the variable correction process is performed on the new energy power generation equipment measurement electrical variable data through the first data splicing result and the prediction instruction information to obtain the first variable correction process result. In the embodiments of the present application, when performing the variable correction process, only the instruction description content in the new energy power conversion description data set corresponding to the new energy power generation equipment measurement electrical variable data needs to be subjected to data splicing and data testing, which can process all data as much as possible to ensure the integrity of the data and avoid disturbance interference, etc., thereby improving the efficiency and accuracy of the variable correction process.

[0045] In some embodiments, the above-mentioned "inputting the new energy power generation equipment measurement electrical variable data into the corresponding new energy power conversion description data set through the data category of the new energy power generation equipment measurement electrical variable data and the new energy power generation equipment object target information" in step S101 can be as follows.

[0046] Step S1011: Obtain the power generation architecture parameters of the new energy power generation equipment measurement electrical variable data, and predict the power generation architecture parameters to obtain the second variable correction process result.

[0047] Step S1012: When it is determined that the second variable correction process result passes the prediction, generate the power generation architecture directory of the new energy power generation equipment measurement electrical variable data.

[0048] Step S1013: Determine one or more new energy power conversion description data sets corresponding to the new energy power generation equipment measurement electrical variable data through the data category of the new energy power generation equipment measurement electrical variable data and the new energy power generation equipment object target information.

[0049] Generally, a new energy power generation device measures electrical variable data with one data category, but there will be one or more new energy power generation device object target information. When implementing this step, it is possible to first determine the target data dimension in the real-time data dimension that includes the data category of the electrical variable data measured by the new energy power generation device, and then based on the new energy power generation device object target information included in the target data dimension, determine the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation device.

[0050] Step S1014, input the electrical variable data measured by the new energy power generation device and the power generation architecture directory into each new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation device.

[0051] When implementing this step, store the electrical variable data measured by the new energy power generation device and the power generation architecture directory in the new energy power conversion description data set corresponding to the account identifier and loan of the electrical variable data measured by the new energy power generation device, and store the electrical variable data measured by the new energy power generation device and the power generation architecture directory in the new energy power conversion description data set corresponding to the device identifier and loan of the electrical variable data measured by the new energy power generation device.

[0052] Through the above steps S1011 to S1014, before inputting the electrical variable data measured by the new energy power generation device into the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation device, first predict the power generation architecture parameters of the electrical variable data measured by the new energy power generation device. After determining that the power generation architecture parameters pass the prediction, then input the electrical variable data measured by the new energy power generation device into the corresponding new energy power conversion description data set through the data category of the electrical variable data measured by the new energy power generation device and the new energy power generation device object target information. And the new energy power conversion description data set is stored in a high-performance storage space, which can improve the reading and writing efficiency of the data, thereby improving the execution efficiency of the variable correction process.

[0053] In some embodiments, the above step S103 "perform data splicing processing on the new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation device through the first analysis instruction data to obtain a first data splicing result" may include the following steps.

[0054] Step S1031, obtain the correction kernel information corresponding to the first analysis instruction data, and determine the data correction result according to the correction kernel information.

[0055] Step S1032, determine one or more instruction description contents through the first analysis instruction data.

[0056] Step S1033: Determine the characteristic coefficients corresponding to each instruction description content from the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment according to the data correction result.

[0057] Step S1034: Use the characteristic coefficients corresponding to each instruction description content as the first data splicing result.

[0058] In the embodiment where the above steps S1031 to S1034 are located, when performing data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data, the problem of inaccurate data splicing is improved, so that the first data splicing result can be accurately obtained.

[0059] Based on the implementation process of the above step S103, the above step S104 "Perform variable correction processing on the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain the first variable correction processing result" may include the following steps.

[0060] Step S1041: Determine the threshold corresponding to each instruction description content and the matching relationship between each instruction description content through the prediction instruction information.

[0061] Thresholds corresponding to each instruction description content are set in the prediction instruction information, and the matching relationship between each instruction description content may include logical AND or logical OR.

[0062] Step S1042: Perform variable correction processing on the measured electrical variable data of the new energy power generation equipment through the threshold corresponding to each instruction description content, the characteristic coefficient, and the matching relationship to obtain the first variable correction processing result.

[0063] When implementing this step, when the matching relationship is logical AND, it is necessary to determine whether each characteristic coefficient matches the threshold. If each characteristic coefficient matches the threshold, the first variable correction processing result that meets the prediction instruction information is obtained; if there is one or more characteristic coefficients that do not match the threshold, the first variable correction processing result that does not meet the prediction instruction information is obtained. When the matching relationship is logical OR, it is necessary to determine whether there is one or more characteristic coefficients that match the threshold. If there is one or more characteristic coefficients that match the threshold, the first variable correction processing result that meets the prediction instruction information is obtained. If all characteristic coefficients do not match the threshold, the first variable correction processing result that does not meet the prediction instruction information is obtained.

[0064] In some embodiments, the prediction instruction information can be configured through steps S201 to S204, including the following described content.

[0065] Step S201, in response to the training step for the first profiling instruction data, obtain each instruction description content in the first profiling instruction data.

[0066] Step S202, obtain the specified step for the threshold of each instruction description content, and obtain the threshold of each instruction description content.

[0067] After determining each instruction description content in the first metric information, it is necessary to set a threshold for each instruction description content. Only when the threshold is set can it be determined whether the feature coefficient matches the threshold during the variable correction process.

[0068] Step S203, obtain the matching relationship for different instruction description contents.

[0069] After setting the instruction description content and the threshold of the instruction description content, when there are two or more instruction description contents, it is necessary to configure the matching relationship of different instruction description contents.

[0070] Step S204, determine the predicted instruction information through each instruction description content in the first profiling instruction data, the threshold of each instruction description content, and the matching relationship.

[0071] After setting each instruction description content in the first profiling instruction data, as well as the threshold of each instruction description content and the matching relationship of different instruction description contents, that is, the configuration of the predicted instruction information is completed, and at this time, the predicted instruction information can be obtained.

[0072] In some embodiments, after step S104, it is also necessary to determine whether the first variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment satisfies the predicted instruction information. Among them, when the first variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment satisfies the predicted instruction information, the measured electrical variable data of the new energy power generation equipment is processed by one of the following three methods.

[0073] The first method: obtain the pre-configured enhanced prediction algorithm, and send the enhanced prediction instruction data to the power correction data processing terminal corresponding to the measured electrical variable data of the new energy power generation equipment to perform enhanced prediction on the measured electrical variable data of the new energy power generation equipment.

[0074] The second method: refuse to process the measured electrical variable data of the new energy power generation equipment.

[0075] The third method: annotate the measured electrical variable data of the new energy power generation equipment.

[0076] When processing in this way, it is possible to add annotations to the electrical variable data measured by the new energy power generation equipment to determine whether to debug the new energy power generation equipment object corresponding to the electrical variable data measured by the new energy power generation equipment in the subsequent implementation process. In some embodiments, when implementing, the number of annotations added to the electrical variable data measured by the new energy power generation equipment can be obtained first; when the number of annotations reaches the specified value configured in advance, abnormal annotation data is determined, and the new energy power generation equipment object directory corresponding to the electrical variable data measured by the new energy power generation equipment is debugged using the abnormal annotation data.

[0077] Based on the above embodiments, the embodiments of the present application further provide a method for correcting electrical variables based on new energy measurement, and the method for correcting electrical variables based on new energy measurement will be described.

[0078] Step S301, the power correction data processing terminal responds to the operation instruction to measure the electrical variable data of the new energy power generation equipment, and sends the electrical variable data measured by the new energy power generation equipment.

[0079] Step S302, send the electrical variable data measured by the new energy power generation equipment to the data processing server.

[0080] Step S303, obtain the power generation architecture parameters of the electrical variable data measured by the new energy power generation equipment, and predict the power generation architecture parameters to obtain the second variable correction processing result.

[0081] Step S304, when it is determined that the second variable correction processing result passes the prediction, generate a power generation architecture directory for the electrical variable data measured by the new energy power generation equipment.

[0082] Step S305, determine one or more new energy power conversion description data sets corresponding to the electrical variable data measured by the new energy power generation equipment through the data category of the electrical variable data measured by the new energy power generation equipment and the target information of the new energy power generation equipment object.

[0083] Step S306, input the electrical variable data measured by the new energy power generation equipment and the power generation architecture directory into each new energy power conversion description data set corresponding to the electrical variable data measured by the new energy power generation equipment.

[0084] Among them, the new energy power conversion description data set is stored in the high-performance cache space. The implementation processes of the above steps S302 to S306 refer to the implementation processes of the above steps S1011 to S1014.

[0085] Step S307: Obtain the new - energy power conversion element parameters for the measured electrical variable data of the new - energy power generation equipment, and predict the new - energy power conversion element parameters to obtain the third - variable correction processing result.

[0086] In the embodiment of the present application, the new - energy power conversion element parameters are different from the power generation architecture parameters. Through the power generation architecture parameters, it can be determined whether the measured electrical variable data message of the new - energy power generation equipment is correct, while the new - energy power conversion element parameters can ensure that the measured electrical variable data of the new - energy power generation equipment can be correctly executed. When implementing this step, it is to determine whether the new - energy power conversion element parameters necessary for the correct execution of the measured electrical variable data of the new - energy power generation equipment exist, and whether the parameters on which the prediction method depends exist. If both exist, the third - variable correction processing result of passing the prediction is obtained; if not, the third - variable correction processing result of failing the prediction is obtained.

[0087] Step S308: Determine whether the third - variable correction processing result is a prediction pass.

[0088] Among them, when the third - variable correction processing result is a prediction failure, step S309 is entered; when the third - variable correction processing result is a prediction pass, step S310 is entered.

[0089] Step S309: The data - processing server refuses to process the measured electrical variable data of the new - energy power generation equipment.

[0090] Step S310: Based on the first analysis instruction data, perform data splicing processing on the new - energy electrical energy conversion description data set corresponding to the measured electrical variable data of the new - energy power generation equipment to obtain the first data - splicing result.

[0091] Step S311: Perform variable correction processing on the measured electrical variable data of the new - energy power generation equipment through the first data - splicing result and the prediction instruction information to obtain the first variable correction processing result.

[0092] The implementation processes of the above steps S310 to S311 are similar to the implementation processes of steps S103 to S104. In actual implementation, the implementation processes of steps S103 to S104 can be referred to.

[0093] Step S312: Determine whether the first variable correction processing result is a prediction pass.

[0094] When processing the measured electrical variable data of the new - energy power generation equipment, the measured electrical variable data of the new - energy power generation equipment can be processed based on the processing method corresponding to the prediction instruction information, where the processing method may include, but is not limited to: strengthening prediction, refusing to process, adding annotations, etc.

[0095] Step S313: Obtain the re - prediction instruction information corresponding to the measured electrical variable data of the new - energy power generation equipment and the second analysis instruction data corresponding to the re - prediction instruction information.

[0096] In the embodiment of the present application, the re - prediction instruction information corresponding to the measured electrical variable data of the new - energy power generation equipment can be determined based on the data category of the measured electrical variable data of the new - energy power generation equipment. The re - prediction instruction information can be in the same way as the prediction instruction information or in a different way. Each re - prediction instruction information includes one or more prediction requirements, and each prediction requirement corresponds to analysis instruction data. When the re - prediction instruction information corresponding to the measured electrical variable data of the new - energy power generation equipment is determined, the second analysis instruction data corresponding to the re - prediction instruction information can be obtained.

[0097] Step S314: The data - processing server performs data splicing processing on the new - energy power conversion description data set corresponding to the measured electrical variable data of the new - energy power generation equipment based on the second analysis instruction data to obtain a second data - splicing result.

[0098] When implementing this step, first obtain the correction - kernel information corresponding to the second analysis instruction data, determine the data - correction result according to the correction - kernel information, then determine one or more prediction - instruction description contents based on the second analysis instruction data, and determine the prediction - feature coefficients corresponding to each prediction - instruction description content from the new - energy power conversion description data set corresponding to the measured electrical variable data of the new - energy power generation equipment according to the data - correction result, and use the prediction - feature coefficients corresponding to each prediction - instruction description content as the second data - splicing result.

[0099] Step S315: Perform variable - correction processing on the measured electrical variable data of the new - energy power generation equipment based on the second data - splicing result and the re - prediction instruction information to obtain a fourth variable - correction processing result.

[0100] The implementation process of this step is similar to that of step S104. First, determine the threshold values of each prediction - instruction description content and the matching relationship of each prediction - instruction description content based on the re - prediction instruction information, and perform variable - correction processing on the measured electrical variable data of the new - energy power generation equipment based on the threshold values, prediction - feature coefficients, and matching relationship corresponding to each prediction - instruction description content to obtain a fourth variable - correction processing result.

[0101] Step S316: Optimize or debug the new - energy power generation equipment object directory corresponding to the measured electrical variable data of the new - energy power generation equipment based on the fourth variable - correction processing result.

[0102] Here, when the processing result of the fourth variable correction indicates that the measured electrical variable data of the new energy power generation equipment satisfies the re-prediction instruction information, the debugging process is performed on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment. When the processing result of the fourth variable correction indicates that the measured electrical variable data of the new energy power generation equipment does not satisfy the re-prediction instruction information, if the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment is in the debugging state, the new energy power generation equipment object directory can be optimized.

[0103] After the data processing server obtains the measured electrical variable data of the new energy power generation equipment to be processed, the measured electrical variable data of the new energy power generation equipment is input into the new energy power conversion description data set corresponding to the data category and the new energy power generation equipment object target information through the data category of the measured electrical variable data of the new energy power generation equipment and the new energy power generation equipment object target information. Since the new energy power conversion description data set to be stored is determined based on the data category and the new energy power generation equipment object target information during storage, that is, the measured electrical variable data of the new energy power generation equipment is stored in the database. Then, the prediction instruction information corresponding to the measured electrical variable data of the new energy power generation equipment and the first analysis instruction data corresponding to the prediction instruction information are obtained. Then, the data splicing process is performed on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data to obtain the first data splicing result. Then, the variable correction process is performed on the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain the first variable correction processing result. When the first variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment satisfies the prediction instruction information, the measured electrical variable data of the new energy power generation equipment is processed. In the embodiment of the present application, when performing the variable correction process, it is only necessary to count the values of the instruction description contents in the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment, and the characteristic coefficients of each instruction description content can be determined from a single new energy power conversion description data set, without performing complex relational operations, thereby improving the statistical efficiency, and then being able to efficiently support relatively complex business methods, thereby improving the efficiency and accuracy of the processing. After the processing is completed, the re-prediction instruction information can also be obtained, and the measured electrical variable data of the new energy power generation equipment is re-predicted by using the re-prediction instruction information, and the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment is optimized or debugged based on the processing result of the fourth variable correction of the re-prediction, which can improve the flexibility of data processing.

[0104] In some embodiments, the above step S316, "optimizing or debugging the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment through the fourth variable correction processing result", may include the following content.

[0105] Step S3161, when the fourth variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment satisfies the re-prediction instruction information, determine the debugging method for the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment.

[0106] When the fourth variable correction processing result is a prediction pass, it indicates that the measured electrical variable data of the new energy power generation equipment satisfies the re-prediction instruction information. At this time, it is necessary to debug the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment, and it is necessary to determine the debugging method. This debugging method can be preset when setting the re-prediction instruction information. When it is determined which re-prediction method is satisfied, the debugging method can also be determined.

[0107] Step S3162, debug the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment through the debugging method.

[0108] Step S3163, when the fourth variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment does not satisfy the re-prediction instruction information, determine whether the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment is in a debugging state.

[0109] Step S3164, if the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment is in a debugging state, determine the optimization method for the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment.

[0110] Step S3165, optimize the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment through the optimization method.

[0111] Through the above steps S3161 to S3165, it is possible to debug the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment through the fourth variable correction processing result when it is determined that the re-prediction instruction information is satisfied, and to debug the object directory of the new energy power generation equipment corresponding to the measured electrical variable data of the new energy power generation equipment when it is determined that the re-prediction instruction information is not satisfied. In this way, the flexibility and comprehensiveness of the prediction detection are improved based on the re-prediction.

[0112] On the above basis, a new energy-based measurement electrical variable correction device is provided. The device includes: A data acquisition module for acquiring new energy power generation equipment measurement electrical variable data to be processed, and inputting the new energy power generation equipment measurement electrical variable data into the new energy power conversion description data set corresponding to the new energy power generation equipment measurement electrical variable data according to the data category of the new energy power generation equipment measurement electrical variable data and the new energy power generation equipment object target information; An instruction acquisition module for acquiring prediction instruction information corresponding to the new energy power generation equipment measurement electrical variable data and first analysis instruction data corresponding to the prediction instruction information; A result splicing module for performing data splicing processing on the geotechnical body description content set corresponding to the new energy power generation equipment measurement electrical variable data through the first analysis instruction data to obtain a first data splicing result; A parameter testing module for performing variable correction processing on the new energy power generation equipment measurement electrical variable data through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result.

[0113] On the above basis, please refer to Figure 2 , which shows a new energy-based measurement electrical variable correction system 300, including a processor 310 and a memory 320 that communicate with each other. The processor 310 is configured to read and execute a computer program from the memory 320 to implement the above method.

[0114] On the above basis, a computer-readable storage medium is further provided, on which a computer program is stored and implemented the above method when running.

[0115] In summary, based on the above solution, after obtaining the measured electrical variable data of the new energy power generation equipment to be processed, the measured electrical variable data of the new energy power generation equipment is input into the new energy power conversion description data set corresponding to the data category and the target information of the new energy power generation equipment object. Since the new energy power conversion description data set to be stored is determined based on the data category and the target information of the new energy power generation equipment object when storing, that is, the measured electrical variable data of the new energy power generation equipment is stored in the database. Then, the prediction instruction information corresponding to the measured electrical variable data of the new energy power generation equipment and the first analysis instruction data corresponding to the prediction instruction information are obtained. Then, the data splicing process is performed on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data to obtain the first data splicing result. Then, the variable correction process is performed on the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain the first variable correction process result. In the embodiment of the present application, when performing the variable correction process, only the instruction description content in the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment needs to be subjected to data splicing and data testing, which can process all data as much as possible to ensure the integrity of the data and avoid disturbances and interferences, thereby improving the efficiency and accuracy of the variable correction process.

[0116] It should be understood that the above-described systems and their modules can be implemented in various ways. For example, in some embodiments, the systems and their modules can be implemented through hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or included in processor control code, such as provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and their modules of the present application can be implemented not only by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable logic devices such as field programmable gate arrays, but also by software executed by various types of processors, or by a combination of the above hardware circuits and software (e.g., firmware).

[0117] It should be noted that the beneficial effects that may be produced by different embodiments are different. In different embodiments, the beneficial effects that may be produced may be any one or a combination of several of the above, or any other beneficial effects that may be obtained.

[0118] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this application. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are proposed in this application, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0119] At the same time, this application uses specific terms to describe the embodiments of this application. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to one or more embodiments of this application. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0120] In addition, those skilled in the art can understand that various aspects of this application can be described and illustrated by several patentable categories or situations, including any new and useful process, machine, product, or composition of matter, or any new and useful improvement thereof. Accordingly, various aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components", or "systems". In addition, various aspects of this application may be embodied as a computer product located in one or more computer-readable media, which includes computer-readable program codes.

[0121] A computer storage medium may contain a propagated data signal containing computer program codes, such as on a baseband or as part of a carrier wave. This propagated signal may have various forms of representation, including electromagnetic form, optical form, etc., or a suitable combination of forms. A computer storage medium can be any computer-readable medium other than a computer-readable storage medium, and this medium can be connected to an instruction execution system, device, or equipment to implement communication, propagation, or transmission for use of the program. The program codes located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0122] The computer program codes required for the operations of various parts of this application can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. This program code can run entirely on the user's computer, or run as an independent software package on the user's computer, or partially run on the user's computer and partially run on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (for example, through the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0123] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numbers and letters, or the use of other names in this application are not used to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on an existing server or mobile device.

[0124] Similarly, it should be noted that, in order to simplify the expression of the disclosure of this application and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this application, sometimes multiple features are merged into one embodiment, drawing or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the individual embodiments disclosed above.

[0125] In some embodiments, numbers are used to describe components and the number of attributes. It should be understood that such numbers used in the description of embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the said numbers allow for adaptive variations. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining general digits. Although the numerical ranges and parameters used to confirm the breadth of the scope in some embodiments of the present application are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0126] For each patent, patent application, patent application publication, and other materials cited in the present application, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into the present application by reference. Except for the application history files that are inconsistent with or conflict with the content of the present application, and except for the files that limit the broadest scope of the claims of the present application (currently or subsequently attached to the present application). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the attached materials of the present application and the content described in the present application, the descriptions, definitions, and / or uses of terms in the present application shall prevail.

[0127] Finally, it should be understood that the embodiments described in the present application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0128] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for correcting electrical variables based on new energy measurement, characterized in that, The method includes: Obtaining the measured electrical variable data of the new energy power generation equipment to be processed, and inputting the measured electrical variable data of the new energy power generation equipment into the corresponding new energy power conversion description data set of the measured electrical variable data of the new energy power generation equipment according to the data category of the measured electrical variable data of the new energy power generation equipment and the target information of the new energy power generation equipment object; Obtaining the prediction instruction information corresponding to the measured electrical variable data of the new energy power generation equipment and the first analysis instruction data corresponding to the prediction instruction information; Performing data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data to obtain a first data splicing result; Performing variable correction processing on the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result.

2. The method according to claim 1, wherein The step of inputting the measured electrical variable data of the new energy power generation equipment into the corresponding new energy power conversion description data set of the measured electrical variable data of the new energy power generation equipment according to the data category of the measured electrical variable data of the new energy power generation equipment and the target information of the new energy power generation equipment object includes: Obtaining the power generation architecture parameters of the measured electrical variable data of the new energy power generation equipment, and predicting the power generation architecture parameters to obtain a second variable correction processing result; When it is determined that the second variable correction processing result passes the prediction, generating a power generation architecture directory of the measured electrical variable data of the new energy power generation equipment; Determining one or more new energy power conversion description data sets corresponding to the measured electrical variable data of the new energy power generation equipment according to the data category of the measured electrical variable data of the new energy power generation equipment and the target information of the new energy power generation equipment object; Respectively inputting the measured electrical variable data of the new energy power generation equipment and the power generation architecture directory into each new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment, and the new energy power conversion description data set is stored in the cache space.

3. The method according to claim 1, wherein The step of performing data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data to obtain a first data splicing result includes: Obtaining the correction kernel information corresponding to the first analysis instruction data, and determining the data correction result according to the correction kernel information; Determining one or more instruction description contents through the first analysis instruction data; Determining the characteristic coefficients corresponding to each instruction description content from the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment according to the data correction result; Taking the characteristic coefficients corresponding to each instruction description content as the first data splicing result.

4. The method according to claim 3, wherein The step of performing variable correction processing on the measured electrical variable data of the new energy power generation equipment through the first data splicing result and the prediction instruction information to obtain a first variable correction processing result includes: Determining the thresholds corresponding to each instruction description content and the matching relationship between each instruction description content through the prediction instruction information; Perform variable correction processing on the measured electrical variable data of the new energy power generation equipment through the thresholds corresponding to the description contents of the respective instructions, the characteristic coefficients, and the matching relationship to obtain a first variable correction processing result.

5. The method according to claim 1, characterized in that The method further includes: Obtain the new energy power conversion element parameters of the measured electrical variable data of the new energy power generation equipment, and perform prediction on the new energy power conversion element parameters to obtain a third variable correction processing result; Determine whether the third variable correction processing result is a prediction pass. Among them, when the third variable correction processing result is a prediction pass, perform data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the first analysis instruction data to obtain a first data splicing result; When the third variable correction processing result is a prediction failure, reject the processing of the measured electrical variable data of the new energy power generation equipment.

6. The method according to claim 1, wherein The method further includes: When the first variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment meets the prediction instruction information, obtain a pre-configured enhanced prediction algorithm, and send enhanced prediction instruction data to the power correction data processing terminal corresponding to the measured electrical variable data of the new energy power generation equipment to perform enhanced prediction on the measured electrical variable data of the new energy power generation equipment; Alternatively, reject the processing of the measured electrical variable data of the new energy power generation equipment; or, annotate the measured electrical variable data of the new energy power generation equipment.

7. The method according to claim 6, characterized in that The method further includes: Obtain the number of annotations of the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment; When the number of annotations reaches a pre-configured number specified value, determine abnormal annotation data, and use the abnormal annotation data to debug the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment.

8. The method according to claim 5, characterized in that, The method further includes: Obtain the re-prediction instruction information corresponding to the measured electrical variable data of the new energy power generation equipment and the second analysis instruction data corresponding to the re-prediction instruction information; Perform data splicing processing on the new energy power conversion description data set corresponding to the measured electrical variable data of the new energy power generation equipment through the second analysis instruction data to obtain a second data splicing result; Perform variable correction processing on the measured electrical variable data of the new energy power generation equipment through the second data splicing result and the re-prediction instruction information to obtain a fourth variable correction processing result; Perform optimization or debugging processing on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment through the fourth variable correction processing result; Among them, the performing optimization or debugging processing on the measured electrical variable data of the new energy power generation equipment through the fourth variable correction processing result includes: When the fourth variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment satisfies the re-prediction instruction information, determine the debugging method for the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment, and perform debugging processing on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment through the debugging method; When the fourth variable correction processing result indicates that the measured electrical variable data of the new energy power generation equipment does not satisfy the re-prediction instruction information, determine whether the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment is in a debugging state; If the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment is in a debugging state, determine the optimization method for the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment, and perform optimization processing on the new energy power generation equipment object directory corresponding to the measured electrical variable data of the new energy power generation equipment through the optimization method.

9. The method according to claim 1, wherein The method further includes: In response to the training step for the first profiling instruction data, obtain each instruction description content in the first profiling instruction data; Obtain the specified step of the threshold for each instruction description content, and obtain the threshold for each instruction description content; Obtain the matching relationship for different instruction description contents; Determine the prediction instruction information through each instruction description content, the threshold of each instruction description content, and the matching relationship.

10. A new energy-based electrical variable measurement correction system, characterized in that, It includes a processor and a memory that communicate with each other. The processor is configured to read and execute a computer program from the memory to implement the method according to any one of claims 1-9.

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