Electric energy meter verification data uplink data planning method

Abstract

The invention relates to the technical field of block chain technology and data management, and discloses an electric energy meter verification data uplink data planning method, a multi-source data management system of the invention comprises a multi-source data storage system, a multi-source data calculation system and a multi-source data management system, the multi-source data storage system is further divided into a data cleaning module, a data dimension reduction module, a data fusion module, a data verification module and a block chain module, and the multi-source data calculation system calculates an effective cleaning index, an effective dimension reduction index, an effective verification index and information of an effective distributed region according to module data in the multi-source data storage system; the multi-source data management system performs classified management on each module according to a calculation result, in the multi-source data calculation system, through calculation of an effective verification index, final troubleshooting can be performed before entering the block chain module, data accuracy is ensured, the verification operation not only conforms to a detection specification of the block chain module, but also meets the detection requirement of the block chain module. And the use of the block chain module can be guided.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology and data management technology, and specifically to a method for planning data on-chain for electric energy meter verification data. Background Art

[0002] The technologies of the data planning method for uploading the verification data of electric energy meters mainly include blockchain technology, intelligent development of metering technology, and information technology and big data analysis technology of power grid systems. These technologies jointly promote the automation and standardization of the verification process of electric energy meters, and significantly improve the efficiency and security of data processing. Among them, the application of blockchain technology is particularly critical. It provides strong technical support for the safe management and reliable transmission of electric energy meter verification data through new application modes of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, etc. This technology can ensure that once the data is recorded, it cannot be tampered with or deleted, thereby ensuring the immutability and transparency of the data, which is crucial to the accuracy and credibility of the verification data of electric energy meters.

[0003] As the complexity of the power grid system business deepens, higher requirements are placed on the reliability of the operating data of the metering device. By implementing the automation and intelligence of metering verification, State Grid Corporation of China has greatly improved the efficiency and accuracy of metering verification, and achieved a revolutionary leap from manual verification to automated verification of measuring instruments. The development and application of these technologies have provided a solid foundation for the implementation of data planning methods for uploading electricity meter verification data. In terms of informatization and big data analysis technology of the power grid system, the safety and stability of the metering device are ensured through regular supervision and status inspection function construction, which is an important task for marketing and operation and maintenance. The application of these technologies not only improves the operating efficiency of the power grid system, but also provides reliable technical guarantees for the accurate collection and analysis of electricity meter verification data.

[0004] Although blockchain technology has significant advantages in improving data security and reliability, it still has some defects in monitoring. For example, traditional blockchain technology cannot identify program failure information before the block chain is recorded, which may lead to the direct recording of erroneous data after the final data processing and cannot be changed. Therefore, when applying blockchain technology to upload the verification data of electric energy meters to the chain, it is necessary to combine other technologies and measures to ensure the accuracy and integrity of the data. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a method for planning data for uploading electric energy meter calibration data to the blockchain, which has the advantage of quickly identifying program fault information before blockchain records, and solves the problem that traditional blockchain technology cannot identify program fault information before blockchain records.

[0007] (II) Technical solution

[0008] To achieve the above-mentioned object, the present invention provides the following technical solutions: a data planning method for uploading verification data of electric energy meters, the method comprising a multi-source data storage system, a multi-source data calculation system and a multi-source data management system;

[0009] The multi-source data storage system is divided into a data cleaning module, a data dimension reduction module, a data fusion module, a data verification module, and a blockchain module;

[0010] The data cleaning module is used to remove erroneous and incomplete data. The data cleaning module collects the data to be cleaned through the multi-source data storage system network client, numbers them, and connects them to the data fusion module through the network;

[0011] The data dimension reduction module performs linear discriminant dimension reduction on the cleaned data. The data dimension reduction module collects the cleaned data through the multi-source data storage system network client, numbers the data, and connects it to the data fusion module through the network;

[0012] The data fusion module fuses the reduced-dimensional data together. The data fusion module collects the reduced-dimensional data through the multi-source data storage system network client, and transmits the data to the data verification module through the network after numbering.

[0013] The data verification module verifies the fused data. The data verification module collects the fused data through the multi-source data storage system network client. After verification, the data is numbered and transmitted to the blockchain module through the network.

[0014] The blockchain module formally records the verified data on the blockchain and classifies it. The blockchain module collects the classified blockchain data through the network client of the data storage system, and transmits it to the multi-source data computing system through the network after numbering.

[0015] The multi-source data computing system serves as the core computing platform of the multi-source data storage system. The multi-source data computing system calculates the effective cleaning index Ri according to the data cleaned according to the needs and the cleaned data. The multi-source data computing system calculates the effective dimension reduction index Tj according to the cleaned data and the dimension-reduced data. The multi-source data computing system calculates the effective verification index Fl according to the dimension-reduced data and the fused data. The multi-source data computing system calculates the effective distributed area Qijl according to the classified blockchain data. The multi-source data computing system and the multi-source data management system are connected through a network.

[0016] The multi-source data management system implements classified management of the data cleaning module, data dimensionality reduction module, data fusion module, data verification module and blockchain module according to the information of the effective cleaning index Ri, the effective dimensionality reduction index Tj, the effective verification index Fl and the effective distributed area Qijl.

[0017] Preferably, the data cleaning module numbers the original values, missing values ​​and abnormal values ​​according to the data characteristics to be cleaned, and the original values, missing values ​​and abnormal values ​​are numbered I1, I2, and I3 respectively. The data dimension reduction module numbers the cleaned data according to the characteristics of the cleaned data, and the cleaned data are numbered E1, E2, E3, ... E n .

[0018] Preferably, the multi-source data calculation system calculates the effective cleaning index Ri according to the data to be cleaned and the cleaned data, and the calculation formula is:

[0019]

[0020] In the formula, Ri represents the effective cleaning index, represents the sum of the cleaned data, n represents the type of cleaned data, and I1+I2-I3 represents the sum of the cleaned data.

[0021] Preferably, the data fusion module numbers the data after dimensionality reduction according to its features, and the data after dimensionality reduction are numbered as J1, J2, J3, ... J m .

[0022] Preferably, the multi-source data calculation system calculates the effective dimension reduction index Tj based on the cleaned data and the dimension reduced data, and the calculation formula is:

[0023]

[0024] In the formula, Tj represents the effective dimension reduction index, represents the data after dimensionality reduction, m represents the type of dimensionality reduction data, represents the sum of the cleaned data, and n represents the type of cleaned data.

[0025] Preferably, the data verification module numbers the fused data according to its features, and the fused data are numbered L1, L2, L3, ... L v .

[0026] Preferably, the multi-source data calculation system calculates the effective verification index Fl according to the dimension-reduced data and the fused data, and the calculation formula is:

[0027]

[0028] In the formula, Fl represents the effective verification index, represents the data after dimensionality reduction, m represents the type of dimensionality reduction data, represents the fused data and, v represents the fused data type.

[0029] Preferably, the blockchain module numbers the actual recorded block header area data, the block header area standard data, the actual recorded block body area data and the block body area standard data according to the classified blockchain data characteristics, and the actual recorded block header area data are numbered A1, A2, A3, ... A v , the total number of the data is Qa, and the standard data numbers of the block header area are Ao1, Ao2, Ao3, ... Ao v The data is generally numbered Qao, and the actual recording block area data is numbered B1, B2, B3, ... B v , the total number of the data is Qb, and the block area standard data numbers are Bp1, Bp2, Bp3, ... Bp v , the total number of this data is Qbp.

[0030] Preferably, the multi-source data calculation system calculates the effective distributed area Qijl according to the classified blockchain data, and the calculation formula is:

[0031] Qa=A1、A2、A3、…A v

[0032] Qao=Ao1、Ao2、Ao3、…Ao v

[0033] Qb=B1、B2、B3、…B v

[0034] Qbp=Bp1、Bp2、Bp3、…Bp v

[0035] Qijl=(Qa∩Qao)∪(Qb∩Qbp)

[0036] In the formula, Qijl represents the effective distribution area, Qa=A1、A2、A3、…A v Indicates the actual record block header area data, Qao = Ao1, Ao2, Ao3, ... Ao v Indicates the standard data of the block header area, Qb = B1, B2, B3, ... B v It means Qb = B1, B2, B3, ... B v , Qbp=Bp1, Bp2, Bp3,...Bp vrepresents the standard data of the block area, (Qa∩Qao) represents the valid data of the block header area, (Qb∩Qbp) represents the valid data of the block area, and v represents the number of data statistics.

[0037] Preferably, the classification management process of the multi-source data management system:

[0038] The multi-source data management system performs troubleshooting on the outlier detection program and duplicate data detection program of the data cleaning module according to the numerical range of the effective cleaning index Ri;

[0039] The multi-source data management system performs troubleshooting on the linear discriminant analysis program of the data dimension reduction module according to the numerical range of the effective dimension reduction index Tj;

[0040] The multi-source data management system performs troubleshooting on the fusion algorithm program of the data fusion module according to the numerical range of the effective verification index Fl;

[0041] The multi-source data management system performs troubleshooting on the effective resource usage space of the blockchain module according to the numerical range of the effective distributed area Qijl.

[0042] Compared with the prior art, the present invention provides a data planning method for uploading electric energy meter verification data, which has the following beneficial effects:

[0043] 1. The present invention uses a data cleaning module to number and process original values, missing values, and abnormal values, which can effectively identify and correct errors and incomplete information in the data, which helps to improve the accuracy and integrity of the data, thereby enhancing the credibility of the cleaned data; the data dimension reduction module uses numbering and classification processing to effectively reduce the complexity of the data and intuitively monitor the scope of the reduced dimension data. Data dimension reduction not only improves the efficiency of data processing, but also reduces storage requirements and provides a computational basis for subsequent calculations.

[0044] 2. The present invention calculates the effective verification index Fl through a multi-source data computing system, and can perform a final check before entering the subsequent blockchain module steps to ensure the accuracy of the data. This verification operation not only complies with the detection specifications of the blockchain module, but also can guide the use of the blockchain module.

[0045] 3. The present invention can effectively screen and reduce data before implementing data immutability by performing effective regional data management on the actual recorded data of the block header and block body in the blockchain module, thereby expanding the storage space. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the structure of the present invention; DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 , a data planning method for uploading electric energy meter verification data, the method includes a multi-source data storage system, a multi-source data calculation system and a multi-source data management system;

[0049] The multi-source data storage system is divided into data cleaning module, data dimension reduction module, data fusion module, data verification module, and blockchain module;

[0050] The data cleaning module is used to remove erroneous and incomplete data. The data cleaning module collects the data to be cleaned through the multi-source data storage system network client, numbers it, and connects it to the data fusion module through the network;

[0051] The data dimension reduction module performs linear discriminant dimension reduction on the cleaned data. The data dimension reduction module collects the cleaned data through the multi-source data storage system network client, numbers the data, and connects it to the data fusion module through the network;

[0052] The data fusion module fuses the reduced-dimensional data together. The data fusion module collects the reduced-dimensional data through the multi-source data storage system network client, and transmits the data to the data verification module through the network after numbering;

[0053] The data verification module verifies the fused data. The data verification module collects the fused data through the multi-source data storage system network client. After verification, the data is numbered and transmitted to the blockchain module through the network.

[0054] The blockchain module will formally record the verified data on the blockchain and classify it. The blockchain module collects the classified blockchain data through the network client of the data storage system, and transmits it to the multi-source data computing system through the network after numbering;

[0055] The multi-source data computing system is the core computing platform of the multi-source data storage system. The multi-source data computing system calculates the effective cleaning index Ri according to the data that needs to be cleaned and the cleaned data. The multi-source data computing system calculates the effective dimension reduction index Tj according to the cleaned data and the dimension-reduced data. The multi-source data computing system calculates the effective verification index Fl according to the dimension-reduced data and the fused data. The multi-source data computing system calculates the effective distributed area Qijl according to the classified blockchain data. The multi-source data computing system and the multi-source data management system are connected through a network.

[0056] The multi-source data management system implements classified management of data cleaning module, data dimensionality reduction module, data fusion module, data verification module and blockchain module according to the information of effective cleaning index Ri, effective dimensionality reduction index Tj, effective verification index Fl and effective distributed area Qijl.

[0057] The data cleaning module numbers the original values, missing values ​​and abnormal values ​​according to the data characteristics that need to be cleaned. The original values, missing values ​​and abnormal values ​​are numbered I1, I2, and I3 respectively. The data dimension reduction module numbers the cleaned data according to the characteristics of the cleaned data. The cleaned data are numbered E1, E2, E3, ... E n .

[0058] The advantages are: by numbering and processing the original values, missing values ​​and abnormal values ​​through the data cleaning module, errors and incomplete information in the data can be effectively identified and corrected, which helps to improve the accuracy and completeness of the data, thereby enhancing the credibility of the cleaned data; by numbering and classifying the data through the data dimensionality reduction module, the complexity of the data can be effectively reduced, and the scope of the reduced dimensionality data can be intuitively monitored. Data dimensionality reduction not only improves the efficiency of data processing, but also reduces storage requirements and provides a computational basis for subsequent calculations.

[0059] The multi-source data calculation system calculates the effective cleaning index Ri based on the data to be cleaned and the data after cleaning. The calculation formula is:

[0060]

[0061] In the formula, Ri represents the effective cleaning index, represents the sum of the cleaned data, n represents the type of cleaned data, and I1+I2-I3 represents the sum of the cleaned data.

[0062] The advantages are: through the calculation of the effective cleaning index Ri, the multi-source data calculation system provides an evaluation index for the data management and monitoring process, which helps to continuously monitor the effect of data cleaning and ensure the rationality of data cleaning operations. At the same time, it also provides a judgment basis for troubleshooting of outlier detection programs and duplicate data detection programs.

[0063] The data fusion module numbers the data after dimensionality reduction according to its characteristics. The data after dimensionality reduction are numbered as J1, J2, J3, ... J m .

[0064] The advantages are: by numbering the reduced-dimensional data through the data fusion module, the data can be managed more effectively. The dimensionality reduction operation itself simplifies the data structure by reducing the dimension of the data, thereby improving the efficiency of subsequent data analysis, reducing the consumption of computing resources and speeding up processing.

[0065] The multi-source data calculation system calculates the effective dimension reduction index Tj based on the cleaned data and the dimension reduced data. The calculation formula is:

[0066]

[0067] In the formula, Tj represents the effective dimension reduction index, represents the data after dimensionality reduction, m represents the type of dimensionality reduction data, represents the sum of the cleaned data, and n represents the type of cleaned data.

[0068] The advantages are: the multi-source data computing system provides an evaluation index for data management and supervision by calculating the effective dimensionality reduction index Tj, which helps to continuously monitor the effect of data processing and ensure the effectiveness of data processing operations. At the same time, it also provides a basis for troubleshooting the linear discriminant analysis program.

[0069] The data verification module numbers the fused data according to its features, and the fused data are numbered as L1, L2, L3, ... L v .

[0070] The multi-source data calculation system calculates the effective verification index Fl based on the reduced-dimensional data and the fused data. The calculation formula is:

[0071]

[0072] In the formula, Fl represents the effective verification index, represents the data after dimensionality reduction, m represents the type of dimensionality reduction data, represents the fused data and, v represents the fused data type.

[0073] The advantage is that the multi-source data computing system can perform a final check before entering the subsequent blockchain module steps by calculating the effective verification index Fl to ensure the accuracy of the data. This verification operation not only complies with the detection specifications of the blockchain module, but also guides the use of the blockchain module.

[0074] The blockchain module numbers the actual recorded block header area data, block header area standard data, actual recorded block body area data and block body area standard data according to the classified blockchain data characteristics. The actual recorded block header area data is numbered as A1, A2, A3, ...A v The total number of this data is Qa, and the standard data numbers in the block header area are Ao1, Ao2, Ao3, ...Ao v The total number of the data is Qao, and the actual recording block area data numbers are B1, B2, B3, ... B vThe total number of this data is Qb, and the block area standard data numbers are Bp1, Bp2, Bp3, ... Bp v , the total number of this data is Qbp.

[0075] The multi-source data computing system calculates the effective distributed area Qijl based on the classified blockchain data, and its calculation formula is:

[0076] Qa=A1、A2、A3、…A v

[0077] Qao=Ao1、Ao2、Ao3、…Ao v

[0078] Qb=B1、B2、B3、…B v

[0079] Qbp=Bp1、Bp2、Bp3、…Bp v

[0080] Qijl=(Qa∩Qao)∪(Qb∩Qbp)

[0081] In the formula, Qijl represents the effective distribution area, Qa=A1、A2、A3、…A v Indicates the actual record block header area data, Qao = Ao1, Ao2, Ao3, ... Ao v Indicates the standard data of the block header area, Qb = B1, B2, B3, ... B v It means Qb = B1, B2, B3, ... B v , Qbp=Bp1, Bp2, Bp3,...Bp v represents the standard data of the block area, (Qa∩Qao) represents the valid data of the block header area, (Qb∩Qbp) represents the valid data of the block area, and v represents the number of data statistics.

[0082] The advantage is that by effectively managing the actual recorded data of the block header and block body in the blockchain module, data can be effectively screened before the data immutability is implemented, thereby expanding the storage space.

[0083] Classification management process of multi-source data management system:

[0084] The multi-source data management system performs troubleshooting on the outlier detection program and duplicate data detection program of the data cleaning module according to the value range of the effective cleaning index Ri;

[0085] The multi-source data management system performs troubleshooting on the linear discriminant analysis program of the data dimension reduction module according to the numerical range of the effective dimension reduction index Tj;

[0086] The multi-source data management system performs troubleshooting on the fusion algorithm program of the data fusion module according to the numerical range of the effective verification index Fl;

[0087] The multi-source data management system troubleshoots the effective resource usage space of the blockchain module according to the numerical range of the effective distributed area Qijl.

[0088] When the values ​​of the effective cleaning index Ri, effective dimension reduction index Tj and effective verification index Fl are between 0.95 and 1, no troubleshooting is required;

[0089] When the value range of the effective cleaning index Ri, the effective dimension reduction index Tj and the effective verification index Fl is less than 0.95, conduct an investigation;

[0090] When the value range of the effective distributed area Qijl exceeds the historical storage value, a spatial check is performed;

[0091] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for planning the data chain of electric energy meter verification data, characterized in that: The method includes a multi-source data storage system, a multi-source data computing system and a multi-source data management system; The multi-source data storage system is divided into a data cleaning module, a data dimension reduction module, a data fusion module, a data verification module, and a blockchain module; The data cleaning module is used to remove erroneous and incomplete data. The data cleaning module collects the data to be cleaned through the multi-source data storage system network client, numbers them, and connects them to the data fusion module through the network; The data dimension reduction module performs linear discriminant dimension reduction on the cleaned data. The data dimension reduction module collects the cleaned data through the multi-source data storage system network client, numbers the data, and connects it to the data fusion module through the network; The data fusion module fuses the reduced-dimensional data together. The data fusion module collects the reduced-dimensional data through the multi-source data storage system network client, and transmits the data to the data verification module through the network after numbering. The data verification module verifies the fused data. The data verification module collects the fused data through the multi-source data storage system network client. After verification, the data is numbered and transmitted to the blockchain module through the network. The blockchain module formally records the verified data on the blockchain and classifies it. The blockchain module collects the classified blockchain data through the network client of the data storage system, and transmits it to the multi-source data computing system through the network after numbering. The multi-source data computing system serves as the core computing platform of the multi-source data storage system. The multi-source data computing system calculates the effective cleaning index Ri according to the data cleaned according to the needs and the cleaned data. The multi-source data computing system calculates the effective dimension reduction index Tj according to the cleaned data and the dimension-reduced data. The multi-source data computing system calculates the effective verification index Fl according to the dimension-reduced data and the fused data. The multi-source data computing system calculates the effective distributed area Qijl according to the classified blockchain data. The multi-source data computing system and the multi-source data management system are connected through a network. The multi-source data management system implements classified management of the data cleaning module, data dimensionality reduction module, data fusion module, data verification module and blockchain module according to the information of the effective cleaning index Ri, the effective dimensionality reduction index Tj, the effective verification index Fl and the effective distributed area Qijl.

2. The method for planning the data upload of electric energy meter verification data according to claim 1 is characterized in that: The data cleaning module numbers the original values, missing values ​​and abnormal values ​​according to the data features that need to be cleaned. The original values, missing values ​​and abnormal values ​​are numbered I1, I2, and I3 respectively. The data dimension reduction module numbers the cleaned data according to the features of the cleaned data. The cleaned data are numbered E1, E2, E3, ... E n .

3. The method for planning the data upload of electric energy meter verification data according to claim 2 is characterized in that: The multi-source data calculation system calculates the effective cleaning index Pi based on the data to be cleaned and the cleaned data, and the calculation formula is: In the formula, Ri represents the effective cleaning index, represents the sum of the cleaned data, n represents the type of cleaned data, and I1+I2-I3 represents the sum of the cleaned data.

4. The method for planning the data upload of electric energy meter verification data according to claim 1 is characterized in that: The data fusion module numbers the data after dimensionality reduction according to its features, and the data after dimensionality reduction are numbered as J1, J2, J3, ...J m .

5. The method for planning the data upload of electric energy meter verification data according to claim 4 is characterized in that: The multi-source data calculation system calculates the effective dimension reduction index Tj based on the cleaned data and the dimension reduced data, and the calculation formula is: In the formula, Tj represents the effective dimension reduction index, represents the data after dimensionality reduction, m represents the type of dimensionality reduction data, represents the sum of the cleaned data, and n represents the type of cleaned data.

6. The method for planning the data upload of electric energy meter verification data according to claim 1, characterized in that: The data verification module numbers the fused data according to its features, and the fused data are numbered L1, L2, L3, ...L v .

7. The method for planning the data upload of electric energy meter verification data according to claim 6 is characterized in that: The multi-source data calculation system calculates the effective verification index Fl according to the dimension-reduced data and the fused data, and the calculation formula is: In the formula, Fl represents the effective verification index, represents the data after dimensionality reduction, m represents the type of dimensionality reduction data, represents the fused data and, v represents the fused data type.

8. The method for planning the data upload of electric energy meter verification data according to claim 1, characterized in that: The blockchain module numbers the actual recorded block header area data, the block header area standard data, the actual recorded block body area data and the block body area standard data according to the classified blockchain data characteristics, and the actual recorded block header area data are numbered A1, A2, A3, ... A v , the total number of the data is Qa, and the standard data numbers of the block header area are Ao1, Ao2, Ao3, ... Ao v The data is generally numbered Qao, and the actual recording block area data is numbered B1, B2, B3, ... B v The data is numbered as Qb, and the block area standard data is numbered as Bp1, Bp2, Bp3, ... Bp v , the total number of this data is Qbp.

9. The method for planning the data upload of electric energy meter verification data according to claim 1, characterized in that: The multi-source data computing system calculates the effective distributed area Qijl based on the classified blockchain data, and its calculation formula is: <h2 style=";text-align:left;direction:ltr">Qa = A1, A2, A3,…A<h2 style=";text-align:left;direction:ltr"> v Qao = Ao1、Ao2、Ao3、…Ao v <h2 style=";text-align:left;direction:ltr">Qb = B1, B2, B3, …B<h2 style=";text-align:left;direction:ltr"> v Qbp=Bp1、Bp2、Bp3、…Bp v Qijl=(Qa∩Qao)∪(Qb∩Qbp) In the formula, Qijl represents the effective distribution area, Qa=A1、A2、A3、…A v Indicates the actual record block header area data, Qao = Ao1, Ao2, Ao3, ... Ao v Indicates the standard data of the block header area, Qb = B1, B2, B3, ... B v It means Qb = B1, B2, B3, ... B v , Qbp=Bp1, Bp2, Bp3,...Bp v It represents the standard data of the block area, (Qa∩Qao) represents the valid data of the block header area, (Qb∩Qbp) represents the valid data of the block area, and v represents the number of data statistics.

10. The method for planning the data upload of electric energy meter verification data according to claim 9, characterized in that: The classification management process of the multi-source data management system: The multi-source data management system performs troubleshooting on the outlier detection program and duplicate data detection program of the data cleaning module according to the numerical range of the effective cleaning index Ri; The multi-source data management system performs troubleshooting on the linear discriminant analysis program of the data dimension reduction module according to the numerical range of the effective dimension reduction index Tj; The multi-source data management system performs troubleshooting on the fusion algorithm program of the data fusion module according to the numerical range of the effective verification index Fl; The multi-source data management system performs troubleshooting on the effective resource usage space of the blockchain module according to the numerical range of the effective distributed area Qijl.

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