Electric power standard data extraction method and system based on big data model

Abstract

The embodiment of the invention provides a power standard data extraction method and system based on a big data model, and belongs to the technical field of Internet and cloud computing. The extraction method comprises the following steps: acquiring a current to-be-extracted power data file; performing statistical comparison on each parameter in the power data file to obtain a first constraint space of each parameter; performing statistical comparison on a plurality of parameters in the power data file to obtain a second constraint space of each parameter; correcting the first constraint space by adopting the second constraint space to obtain a comprehensive constraint space of each parameter value; constructing a parameter architecture of the power standard data; filling the comprehensive constraint space into the parameter architecture; searching blank items and filled items of the matching parameter architecture based on the big data model; judging the effectiveness of the search matching result according to the search matching result and the actual value of the filled item; and judging whether the validity meets requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet and cloud computing, and particularly to a method and system for extracting power standard data based on a big data model. Background Art

[0002] Power standard data is an important basis for the implementation of power engineering projects. However, the current power standard data is not perfect in many aspects and cannot achieve comprehensive coverage. For the project fields that are not covered, the current power standard data mainly relies on manual collation and summary, and this method has significant deficiencies. The possibility of introducing errors by human factors in the data collation process is relatively high, resulting in difficulties in ensuring data accuracy and consistency. The efficiency of manual data processing is low and it is difficult to meet the timely processing requirements of a large amount of data in the modern power industry. Summary of the Invention

[0003] An object of an embodiment of the present invention is to provide a method and system for extracting power standard data based on a big data model, and the extraction method and system can achieve efficient extraction of power standard data.

[0004] To achieve the above object, an embodiment of the present invention provides a method for extracting power standard data based on a big data model, including:

[0005] Obtain the current power data file to be extracted;

[0006] Perform statistical comparison on each parameter in the power data file to obtain a first constraint space for each parameter;

[0007] Perform statistical comparison on multiple parameters in the power data file to obtain a second constraint space for each parameter;

[0008] Use the second constraint space to correct the first constraint space to obtain a comprehensive constraint space for each parameter value;

[0009] Construct a parameter architecture for power standard data;

[0010] Fill the comprehensive constraint space into the parameter architecture;

[0011] Search and match the blank items and filled items of the parameter architecture based on the big data model;

[0012] Judge the validity of the search and match result according to the search and match result and the actual value of the filled item;

[0013] Judge whether the validity meets the requirements;

[0014] When it is determined that the validity meets the requirements, fill the search matching results corresponding to the blank items into the parameter architecture to obtain the extracted power standard data.

[0015] Optionally, perform statistical comparison for each parameter in the power data file to obtain the first constraint space for each parameter, including:

[0016] Perform inductive matching on each parameter item based on the keyword matching method to obtain the corresponding parameter for each item;

[0017] Statistically calculate the maximum and minimum values of each parameter to determine the upper limit value and lower limit value of the first constraint space.

[0018] Optionally, perform statistical comparison for multiple parameters in the power data file to obtain the second constraint space for each parameter, including:

[0019] Taking the number of the power data file as the sequence coordinate and the value of each parameter as the ordinate, construct a parameter sequence diagram;

[0020] Calculate the similarity of the parameter sequence diagram to obtain the correlation degree between every two parameters;

[0021] Based on the correlation degree, associate the corresponding two parameters to obtain the association relationship between the two parameters.

[0022] Optionally, calculate the similarity of the parameter sequence diagram to obtain the correlation degree between every two parameters, including:

[0023] Calculate the jump value of the parameter sequence diagram according to formula (1):

[0024]

[0025] where, Δf i is the i-th jump value, x i+1 , x i are the (i + 1)-th and i-th parameter values respectively;

[0026] Taking the jump value as the ordinate and the number of the power data as the sequence coordinate, construct a jump sequence diagram;

[0027] Traverse every two of the jump sequence diagrams and calculate the corresponding deviation values respectively;

[0028] Judge whether the deviation value is less than or equal to a preset deviation value threshold;

[0029] When it is determined that the deviation value is less than or equal to the deviation value threshold, determine that the correlation degree of the corresponding two parameters is relevant;

[0030] When it is determined that the deviation value is greater than the deviation value threshold, it is determined that the correlation degree of the corresponding two parameters is non - correlated.

[0031] Optionally, traverse every two of the jump sequence diagrams, and calculate the corresponding deviation values respectively, including:

[0032] Calculate the deviation value according to formula (2):

[0033]

[0034] where, Δt is the deviation value, Δf i ′ and Δf i are respectively two calculated jump values, and n is the number of jump values in one jump sequence diagram.

[0035] Optionally, perform statistical comparison on multiple parameters in the power data file to obtain the second constraint space for each parameter, including:

[0036] According to two associated parameters, perform a function fitting operation on the parameter sequence diagram by using the function fitting method to obtain the conversion relationship between the two parameters.

[0037] Optionally, use the second constraint space to correct the first constraint space to obtain the comprehensive constraint space for each parameter value, including:

[0038] For every two associated parameters in the second constraint space, use the upper limit value and the lower limit value of one of the parameters as the input, and the corresponding conversion relationship as the output, and calculate the upper limit value and the lower limit value corresponding to the other parameter;

[0039] Use the calculated upper limit value and lower limit value to correct the actual upper limit value and lower limit value of the other parameter to obtain the comprehensive constraint space.

[0040] Optionally, judge the validity of the search - matching result according to the search - matching result and the actual value of the filled item, including:

[0041] Calculate the difference between the search - matching result and the actual value;

[0042] Judge whether the difference is less than or equal to a preset difference threshold;

[0043] When it is determined that the difference is less than or equal to the difference threshold, it is determined that the corresponding point - value matching is successful;

[0044] Calculate the ratio of the number of successfully - matched point - values to the total number;

[0045] Judge whether the ratio is greater than or equal to a preset ratio threshold;

[0046] When it is determined that the ratio is greater than or equal to the ratio threshold, it is determined that the result of the search match is valid;

[0047] When it is determined that the ratio is less than or equal to the ratio threshold, it is determined that the result of the search match is invalid.

[0048] On the other hand, the present invention also provides an extraction system for power standard data based on a big data model, where the extraction system includes a processor configured to execute the extraction method as described in any one of the above.

[0049] Through the above technical solutions, the embodiments of the present invention provide an extraction method and system for power standard data based on a big data model. By combining the characteristics of statistical comparison of single parameters and correlation constraints of multiple parameters, the accurate extraction of standard data for each power parameter is realized. Finally, through the verification and complementation of the big data model, the comprehensive extraction of the standard data of power parameters is achieved. Compared with the manual collation of the prior art, the extraction method and system provided by the present invention improve the extraction efficiency and accuracy of power standard data.

[0050] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0052] Figure 1 is a flowchart of an extraction method for power standard data based on a big data model according to an embodiment of the present invention;

[0053] Figure 2 is a flowchart of a statistical comparison method according to an embodiment of the present invention;

[0054] Figure 3 is an example diagram of a parameter sequence diagram according to an embodiment of the present invention;

[0055] Figure 4 is a flowchart of a method for determining correlation according to an embodiment of the present invention;

[0056] Figure 5 is an example diagram of a jump sequence diagram according to an embodiment of the present invention;

[0057] Figure 6It is a flowchart for judging the validity of the search matching result according to the search matching result and the actual value of the filled item according to an embodiment of the present invention. Detailed Embodiment

[0058] The following will describe in detail the specific embodiments of the embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0059] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. In the embodiments of this application, some industry-existing solutions such as software, components, models, etc. may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0060] As Figure 1 shown is a flowchart of a method for extracting power standard data based on a big data model according to an embodiment of the present invention. In this Figure 1 the extraction method may include the following steps:

[0061] In step S10, obtain the current power data file to be extracted;

[0062] In step S11, perform statistical comparison on each parameter in the power data file respectively to obtain the first constraint space of each parameter;

[0063] In step S12, perform statistical comparison on multiple parameters in the power data file to obtain the second constraint space of each parameter;

[0064] In step S13, use the second constraint space to correct the first constraint space to obtain the comprehensive constraint space of each parameter value;

[0065] In step S14, construct the parameter architecture of the power standard data;

[0066] In step S15, fill the comprehensive constraint space into the parameter architecture;

[0067] In step S16, search and match the blank items and filled items of the parameter architecture based on the big data model;

[0068] In step S17, judge the validity of the search matching result according to the search matching result and the actual value of the filled item;

[0069] In step S18, judge whether the validity meets the requirements;

[0070] In step S19, when it is determined that the validity meets the requirements, the search matching results corresponding to the blank items are filled into the parameter architecture to obtain the extracted power standard data. Conversely, when it is determined that the validity does not meet the requirements, return to execute the step of judging the validity of the search matching results based on the search matching results and the actual values of the filled items, that is, return to execute step S17.

[0071] In the extraction method as Figure 1 shown, step S10 can be used to obtain the current power data file to be extracted. This power data file can be a historical file directly accessed from the power information system.

[0072] Step S11 can be used to perform statistical comparison for each parameter in the power data file to obtain the first constraint space for each parameter. Specifically, in this embodiment, the method for obtaining this first constraint space can be to first perform inductive matching on each parameter item based on the keyword matching method to obtain the corresponding parameter for each item, and then count the maximum and minimum values of each parameter to determine the upper limit value and lower limit value of the first constraint space.

[0073] Step S12 can be used to perform statistical comparison for multiple parameters in the power data file to obtain the second constraint space for each parameter. Among them, for the specific method of this statistical comparison, it can be various forms known to those skilled in the art. In an example of the present invention, the method of this statistical comparison can include as Figure 2 shown in the steps. In this Figure 2 the statistical comparison method can include the following steps:

[0074] In step S20, with the number of the power data file as the sequence coordinate and the value of each parameter as the ordinate, a parameter sequence diagram is constructed. An example diagram of this parameter sequence diagram can be as Figure 3 shown.

[0075] In step S21, calculate the similarity of the parameter sequence diagram to obtain the correlation degree between every two parameters. Specifically, the method for determining this correlation degree can include as Figure 4 shown in the steps. In this Figure 4 the method for determining this correlation degree can include the following steps:

[0076] In step S30, calculate the jump value of the parameter sequence diagram according to formula (1):

[0077]

[0078] where Δf i is the i-th jump value, x i+1 、x iThey are the (i + 1)-th and i-th parameter values respectively;

[0079] In step S31, taking the jump value as the ordinate and the serial number of the power data as the sequence coordinate, a jump sequence diagram is constructed. An example diagram of this jump sequence diagram can be as shown in Figure 5 shown.

[0080] In step S32, for every two jump sequence diagrams traversed, the corresponding deviation values are calculated respectively. Among them, for the specific calculation method of this deviation value, there can be various forms known to those skilled in the art. In an example of the present invention, the deviation value can be calculated using the following formula (2):

[0081]

[0082] where Δt is the deviation value, Δf i ′ and Δf i are two calculated jump values respectively, and n is the number of jump values in a jump sequence diagram.

[0083] In step S33, it is judged whether the deviation value is less than or equal to a preset deviation value threshold;

[0084] In step S34, when it is judged that the deviation value is less than or equal to the deviation value threshold, it is determined that the correlation degree of the corresponding two parameters is relevant;

[0085] In step S35, when it is judged that the deviation value is greater than the deviation value threshold, it is determined that the correlation degree of the corresponding two parameters is non - relevant.

[0086] In step S22, based on the correlation degree, the corresponding two parameters are associated to obtain the association relationship between the two parameters.

[0087] After the correlation degree is determined, in this step S12, further according to the two associated parameters, the function fitting method can be used to perform a function fitting operation on the parameter sequence diagram to obtain the conversion relationship between the two parameters.

[0088] Step S13 can be used to modify the first constraint space by using the second constraint space to obtain the comprehensive constraint space of each parameter value. Specifically, in this step S13, first, for every two associated parameters in the second constraint space, using the upper limit value and the lower limit value of one of the parameters as the input and the corresponding conversion relationship as the output, calculate the upper limit value and the lower limit value of the other parameter, and then use the calculated upper limit value and lower limit value to modify the actual upper limit value and lower limit value of the other parameter to obtain the comprehensive constraint space.

[0089] Step S17 can be used to determine the validity of the search matching result based on the search matching result and the actual value of the filled item. Specifically, this step S17 may include the method as shown in Figure 6 as follows.

[0090] In this Figure 6 , this step S17 may include the following method:

[0091] In step S40, calculate the difference between the search matching result and the actual value;

[0092] In step S41, determine whether the difference is less than or equal to a preset difference threshold;

[0093] In step S42, when it is determined that the difference is less than or equal to the difference threshold, determine that the corresponding point value matching is successful;

[0094] In step S43, calculate the ratio of the number of successfully matched point values to the total number;

[0095] In step S44, determine whether the ratio is greater than or equal to a preset ratio threshold;

[0096] In step S45, when it is determined that the ratio is greater than or equal to the ratio threshold, determine that the search matching result is valid;

[0097] In step S46, when it is determined that the ratio is less than or equal to the ratio threshold, determine that the search matching result is invalid.

[0098] On the other hand, the present invention also provides an extraction system for power standard data based on a big data model. The extraction system includes a processor, and the processor is configured to execute the extraction method as described in any one of the above. Specifically, the extraction method may include the following steps:

[0099] In step S10, obtain the current power data file to be extracted;

[0100] In step S11, perform statistical comparison on each parameter in the power data file respectively to obtain the first constraint space of each parameter;

[0101] In step S12, perform statistical comparison on multiple parameters in the power data file to obtain the second constraint space of each parameter;

[0102] In step S13, use the second constraint space to correct the first constraint space to obtain the comprehensive constraint space of each parameter value;

[0103] In step S14, construct the parameter architecture of the power standard data;

[0104] In step S15, the comprehensive constraint space is filled into the parameter architecture;

[0105] In step S16, based on the big data model, blank items and filled items in the parameter architecture are searched and matched;

[0106] In step S17, according to the search and match results and the actual values of the filled items, the validity of the search and match results is judged;

[0107] In step S18, it is judged whether the validity meets the requirements;

[0108] In step S19, when it is judged that the validity meets the requirements, the search and match results corresponding to the blank items are filled into the parameter architecture to obtain the extracted power standard data. Conversely, when it is judged that the validity does not meet the requirements, the step of judging the validity of the search and match results according to the search and match results and the actual values of the filled items is returned, that is, step S17 is returned for execution.

[0109] In this Figure 1 shown extraction method, step S10 can be used to obtain the current power data file to be extracted. This power data file can be a historical file directly accessed from the power information system.

[0110] Step S11 can be used to perform statistical comparison on each parameter in the power data file respectively to obtain the first constraint space for each parameter. Specifically, in this embodiment, the method for obtaining the first constraint space can be to first perform inductive matching on each parameter item based on the keyword matching method to obtain the corresponding parameter for each item, and then count the maximum value and minimum value of each parameter to determine the upper limit value and lower limit value of the first constraint space.

[0111] Step S12 can be used to perform statistical comparison on multiple parameters in the power data file to obtain the second constraint space for each parameter. Among them, for the specific method of this statistical comparison, there can be various forms known to those skilled in the art. In an example of the present invention, the method of this statistical comparison can include as Figure 2 shown in the steps. In this Figure 2 the statistical comparison method can include the following steps:

[0112] In step S20, with the number of the power data file as the sequence coordinate and the value of each parameter as the ordinate, a parameter sequence diagram is constructed. An example diagram of this parameter sequence diagram can be as Figure 3 shown.

[0113] In step S21, the similarity of the parameter sequence diagram is calculated to obtain the correlation degree between every two parameters. Specifically, the method for determining this correlation degree can include as Figure 4The steps shown in. In this Figure 4 , the method for determining the relevance may include the following steps:

[0114] In step S30, calculate the jump value of the parameter sequence diagram according to formula (1):

[0115]

[0116] where, Δf i is the i-th jump value, and x i+1 , x i are the (i + 1)-th and i-th parameter values respectively;

[0117] In step S31, construct a jump sequence diagram with the jump value as the ordinate and the number of the power data as the sequence coordinate. An example diagram of this jump sequence diagram can be as Figure 5 shown.

[0118] In step S32, traverse every two jump sequence diagrams and calculate the corresponding deviation values respectively. Among them, for the specific calculation method of this deviation value, there can be various forms known to those skilled in the art. In an example of the present invention, the deviation value can be calculated by using the following formula (2):

[0119]

[0120] where, Δt is the deviation value, and Δf i ′ , Δf i are the two calculated jump values respectively, and n is the number of jump values in a jump sequence diagram.

[0121] In step S33, determine whether the deviation value is less than or equal to a preset deviation value threshold;

[0122] In step S34, when it is determined that the deviation value is less than or equal to the deviation value threshold, determine that the relevance of the corresponding two parameters is relevant;

[0123] In step S35, when it is determined that the deviation value is greater than the deviation value threshold, determine that the relevance of the corresponding two parameters is non-relevant.

[0124] In step S22, based on the relevance, associate the corresponding two parameters to obtain the association relationship between the two parameters.

[0125] After determining the relevance, this step S12 can further perform a function fitting operation on the parameter sequence diagram according to the two associated parameters by using the function fitting method to obtain the conversion relationship between the two parameters.

[0126] Step S13 can be used to correct the first constraint space by using the second constraint space to obtain a comprehensive constraint space for each parameter value. Specifically, this step S13 can first calculate the upper and lower limit values corresponding to another parameter for every two associated parameters in the second constraint space by using the upper and lower limit values of one of the parameters as inputs and the corresponding conversion relationship as the output, and then correct the actual upper and lower limit values of another parameter by using the calculated upper and lower limit values to obtain the comprehensive constraint space.

[0127] Step S17 can be used to determine the validity of the search matching result according to the search matching result and the actual value of the filled item. Specifically, this step S17 can include the method as Figure 6 shown.

[0128] In this Figure 6 the step S17 can include the following method:

[0129] In step S40, calculate the difference between the search matching result and the actual value;

[0130] In step S41, determine whether the difference is less than or equal to a preset difference threshold;

[0131] In step S42, when it is determined that the difference is less than or equal to the difference threshold, determine that the corresponding point value matching is successful;

[0132] In step S43, calculate the ratio of the number of successfully matched point values to the total number;

[0133] In step S44, determine whether the ratio is greater than or equal to a preset ratio threshold;

[0134] In step S45, when it is determined that the ratio is greater than or equal to the ratio threshold, determine that the search matching result is valid;

[0135] In step S46, when it is determined that the ratio is less than or equal to the ratio threshold, determine that the search matching result is invalid.

[0136] Through the above technical solutions, the embodiments of the present invention provide a method and system for extracting power standard data based on a big data model. The extraction method and system realize the accurate extraction of the standard data of each power parameter by combining the characteristics of statistical comparison of single parameters and correlation constraints of multiple parameters, and finally realize the comprehensive extraction of the standard data of power parameters by combining the verification and complement of the big data model. Compared with the manual collation of the prior art, the extraction method and system provided by the present invention improve the extraction efficiency and accuracy of power standard data.

[0137] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0139] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0141] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0142] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0143] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0145] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for extracting power standard data based on a big data model, characterized in that: The extraction method comprises: Get the current power data file to be extracted; Performing statistical comparison on each parameter in the power data file to obtain a first constraint space for each parameter; Performing statistical comparison on multiple parameters in the power data file to obtain a second constraint space of each parameter; Using the second constraint space to modify the first constraint space to obtain a comprehensive constraint space for each parameter value; Construct parameter architecture for power standard data; Filling the comprehensive constraint space into the parameter framework; Searching for blank items and filled items matching the parameter architecture based on the big data model; Judging the validity of the search and matching result according to the search and matching result and the actual value of the filled item; Determine whether the validity meets the requirements; When it is determined that the validity meets the requirement, the search and matching result corresponding to the blank item is filled into the parameter structure to obtain the extracted power standard data.

2. The method according to claim 1, characterized in that Statistical comparison is performed on each parameter in the power data file to obtain a first constraint space for each parameter, including: Based on the keyword matching method, each parameter item is summarized and matched to obtain the corresponding parameters; The maximum value and the minimum value of each parameter are counted to determine the upper limit and the lower limit of the first constraint space.

3. The method according to claim 1, characterized in that: Statistical comparison is performed on multiple parameters in the power data file to obtain a second constraint space for each parameter, including: The number of the power data file is used as the sequence coordinate, and the value of each parameter is used as the vertical coordinate to construct a parameter sequence diagram; Calculating the similarity of the parameter sequence graph to obtain the correlation between every two parameters; The two corresponding parameters are associated based on the correlation to obtain an associated relationship between the two parameters.

4. The method according to claim 3, characterized in that Calculate the similarity of the parameter sequence diagram to obtain the correlation between every two parameters, including: The jump value of the parameter sequence diagram is calculated according to formula (1): Where Δf i is the i-th jump value, x i+1 、x i are the i+1th and ith parameter values ​​respectively; With the jump value as the vertical coordinate and the number of the power data as the sequence coordinate, a jump sequence diagram is constructed; Traversing every two of the hopping sequence diagrams, and calculating the corresponding deviation values ​​respectively; Determining whether the deviation value is less than or equal to a preset deviation value threshold; In the case where it is determined that the deviation value is less than or equal to the deviation value threshold, determining that the correlation between the two corresponding parameters is correlated; When it is determined that the deviation value is greater than the deviation value threshold, the correlation between the two corresponding parameters is determined to be non-correlated.

5. The method according to claim 4, characterized in that Traversing every two of the jump sequence diagrams, and calculating the corresponding deviation values ​​respectively, including: The deviation value is calculated according to formula (2): Wherein, Δt is the deviation value, Δf i ′、Δf i are two of the jump values ​​calculated respectively, and n is the number of jump values ​​in one of the jump sequence diagrams.

6. The method according to claim 3, characterized in that: Statistical comparison is performed on multiple parameters in the power data file to obtain a second constraint space for each parameter, including: According to the two associated parameters, a function fitting operation is performed on the parameter sequence diagram using a function fitting method to obtain a conversion relationship between the two parameters.

7. The method according to claim 1, characterized in that The first constraint space is modified by using the second constraint space to obtain a comprehensive constraint space for each parameter value, including: For each two parameters associated in the second constraint space, the upper limit value and the lower limit value of one of the parameters are used as input, and the corresponding conversion relationship is used as output to calculate the upper limit value and the lower limit value corresponding to the other parameter; The calculated upper limit value and lower limit value are used to correct the actual upper limit value and lower limit value of another parameter to obtain the comprehensive constraint space.

8. The method according to claim 1, characterized in that Judging the validity of the search matching result according to the search matching result and the actual value of the filled item includes: Calculate the difference between the search result and the actual value; Determining whether the difference is less than or equal to a preset difference threshold; In the case where it is determined that the difference is less than or equal to the difference threshold, determining that the corresponding point value matches successfully; Calculate the ratio of the number of points matched successfully to the total number; Determining whether the ratio is greater than or equal to a preset ratio threshold; In the case where it is determined that the ratio is greater than or equal to the ratio threshold, determining that the search and matching result is valid; When it is determined that the ratio is less than or equal to the ratio threshold, it is determined that the result of the search match is invalid.

9. A system for extracting power standard data based on a big data model, characterized in that: The extraction system comprises a processor, and the processor is configured to execute the extraction method according to any one of claims 1 to 8.

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