Asset data grading method based on quality detection

Abstract

The invention discloses an asset data grading method based on quality detection. The method comprises the following steps: synchronously collecting asset data of heterogeneous data sources; configuring a quality detection rule of the SQL statement and the regular expression; performing asset data quality detection on the asset data through a quality detection rule, and summarizing quality detection results of the asset data; calculating a comprehensive score based on an integrity score, an accuracy score, a consistency score, an effectiveness score and a uniqueness score according to a quality detection result of the asset data; and performing level-to-level management on the asset data according to the comprehensive score, and performing quality reporting on the lowest level of asset data. According to the comprehensive score, through multi-dimensional asset data evaluation, the quality of the asset data can be evaluated more comprehensively, the correctness of asset data evaluation is ensured, asset data grading is performed according to the comprehensive score, an asset data management strategy can be formulated in a targeted manner, and the management efficiency and effect of the asset data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of asset data quality detection, and specifically, to a method for classifying asset data based on quality detection. Background Art

[0002] Generally, in the construction of a data center project, the data sources are ever-changing. When using ETL (Extract-Transform-Load) tool software for data extraction, cleaning, and transformation, some important data is either missing or does not conform to the specifications. Therefore, before the data after extraction, cleaning, and transformation is stored in the database, relevant quality detection needs to be carried out on the data, the normal data is stored in the database, and the problem data and the analysis results of the problem data are provided to urge the data source to improve the data quality.

[0003] However, the source data in the data center is different, and the data is diverse. The monitoring rules or detection rules applicable to different data will also be correspondingly different. Currently, generally, multiple preset different detection rules are used to uniformly detect different source data, but there are some situations where the detection rules have nothing to do with the source data type. For example, the source data is an ID number, and one of the detection rules is to detect whether the time is illegal. There is no correlation between the source data and the detection rule for whether the time is illegal. When using this detection rule to detect the source data, it is equivalent to an ineffective detection, which affects the detection efficiency. Or, the preset detection rules do not cover a wide enough range and cannot comprehensively detect the data, which not only affects the quality of the data stored in the database, but also causes abnormalities in the problem data and the analysis results of the problem data, and cannot correctly supervise the data source to improve the data quality.

[0004] Therefore, when currently detecting the source data, it is impossible to flexibly respond to the changing monitoring rules or detection rules according to the source data to be detected, so as to output the data analysis tracking investigation of the problem to the data source, thereby urging the improvement of the data quality, which not only affects the detection efficiency, but also affects the quality of the data stored in the database. At the same time, it will also cause abnormalities in the problem data and the analysis results of the problem data, and cannot correctly supervise the data source to improve the data quality. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method for classifying asset data based on quality detection, which improves the quality of asset data through quality detection of asset data, and comprehensively scores and classifies and manages the quality of asset data.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solutions: A method for classifying asset data based on quality detection specifically includes the following steps:

[0007] Step S1, synchronously collect asset data of heterogeneous data sources;

[0008] Step S2: Configure the quality detection rules for SQL statements and regular expressions;

[0009] Step S3: Perform quality detection on asset data through the quality detection rules, and summarize the quality detection results of the asset data;

[0010] Step S4: Calculate the comprehensive score based on the integrity score, accuracy score, consistency score, validity score, and uniqueness score according to the quality detection results of the asset data;

[0011] Step S5: Conduct hierarchical management of asset data according to the comprehensive score, and report the quality of the lowest-level asset data.

[0012] Furthermore, the quality detection rules include: null value verification rule, primary key verification rule, duplicate value verification rule, value range verification rule, email address verification rule, and phone number verification rule; the null value verification rule is used for the integrity detection of asset data, the primary key verification rule and the duplicate value verification rule are used for the uniqueness detection of asset data, the value range verification rule is used for the consistency and accuracy detection of asset data, and the email address verification rule and the phone number verification rule are used for the validity and accuracy detection of asset data.

[0013] Furthermore, the null value verification rule, primary key verification rule, and duplicate value verification rule are all configured through SQL statements, and the value range verification rule, email address verification rule, and phone number verification rule are all configured through regular expressions.

[0014] Furthermore, the calculation process of the comprehensive score of asset data in Step S4 is as follows:

[0015] F = αF 1 + βF 2 + γF 3 + δF 4 + εF 5

[0016] where F represents the comprehensive score of asset data, F 1 represents the integrity score of asset data, α represents the weight of F 1 , F 2 represents the accuracy score of asset data, β represents the weight of F 2 , F 3 represents the consistency score of asset data, γ represents the weight of F 3 , F 4 represents the validity score of asset data, δ represents the weight of F 4 , F 5 represents the uniqueness score of asset data, ε represents the weight of F5 Weight.

[0017] Furthermore, the calculation process of the integrity score of the asset data is as follows:

[0018] Integrity score of asset data = (∑(number of non - null values of field i in asset data / total number of field i in asset data) × weight of field i in asset data) / ∑ weight of field i × 100 + integrity compensation score of asset data;

[0019] Among them, the integrity compensation score of the asset data is determined according to the integrity after filling the asset data. If the integrity after filling the asset data exceeds the preset integrity threshold, the integrity compensation score of the asset data is given; otherwise, the integrity compensation score of the asset data is 0.

[0020] Furthermore, the calculation process of the accuracy score of the asset data is as follows:

[0021] Accuracy score of asset data = (number of accurate fields in asset data / total number of fields in asset data) × 100 - accuracy compensation score of asset data;

[0022] Among them, the accuracy compensation score of the asset data is determined according to the proportion of accurate fields in the asset data. If the proportion of accurate fields in the asset data is lower than the preset accuracy threshold, the accuracy compensation score of the asset data is given; otherwise, the accuracy compensation score of the asset data is 0.

[0023] Furthermore, the calculation process of the consistency score of the asset data is as follows:

[0024] Consistency score of asset data = (number of consistent fields in asset data / total number of fields in asset data) × 100.

[0025] Furthermore, the calculation process of the validity score of the asset data is as follows:

[0026] Validity score of asset data = (number of valid fields in asset data / total number of fields in asset data) × 100.

[0027] Furthermore, the calculation process of the uniqueness score of the asset data is as follows: Use the hash algorithm to perform hash processing on the unique identifier fields in the asset data. Calculate the uniqueness score of the asset data = (number of fields with unique hash values in asset data / total number of fields in asset data) × 100 - uniqueness compensation score of asset data, where the uniqueness compensation score of the asset data is determined according to the number of repeated hash values in the asset data fields.

[0028] Further, the specific process of classifying and managing asset data according to the comprehensive score in step S5 is as follows: If the comprehensive score of the asset data is above 90 points, the level of the asset data is excellent; if the comprehensive score range of the asset data is [80, 90), the level of the asset data is good; if the comprehensive score range of the asset data is [60, 80), the level of the asset data is qualified; if the comprehensive score range of the asset data is [0, 60), the level of the asset data is unqualified.

[0029] Compared with the prior art, the present invention has the following beneficial effects: The asset data classification method based on quality inspection of the present invention configures the quality inspection rules of asset data through SQL statements and regular expressions. The SQL statement provides various constraint mechanisms to ensure the integrity and uniqueness verification of asset data and improve the quality of asset data; regular expressions can quickly perform complex matching and control on strings, and through regular expressions, asset data that meets consistency, accuracy, and effectiveness can be efficiently screened out. By jointly performing quality inspection on asset data through SQL statements and regular expressions, more efficient, flexible, and accurate asset data detection can be achieved; at the same time, based on the quality inspection results of asset data, a comprehensive score is calculated based on integrity score, accuracy score, consistency score, effectiveness score, and uniqueness score. Through multi-dimensional asset data evaluation, the quality of asset data can be more comprehensively evaluated, the correctness of asset data evaluation can be ensured, and problems existing in asset data can be timely discovered according to the scores of each dimension. By calculating the comprehensive score based on multi-dimensional scores and classifying the asset data levels, targeted asset data management strategies can be formulated to improve the management efficiency and effect of asset data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of the asset data classification method based on quality inspection of the present invention;

[0031] Figure 2 is a flowchart of calculating the comprehensive score through the quality inspection results of asset data of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The technical solutions of the present invention will be further explained below with reference to the accompanying drawings.

[0033] As Figure 1 is a flowchart of the asset data classification method based on quality inspection of the present invention, and the asset data classification method specifically includes the following steps:

[0034] Step S1, synchronously collect asset data from heterogeneous data sources.

[0035] Step S2: Configure the quality detection rules for SQL statements and regular expressions. The quality detection rules in the present invention include: null value verification rule, primary key verification rule, duplicate value verification rule, value range verification rule, email address verification rule, and telephone number verification rule. The null value verification rule is used for the integrity detection of asset data. The primary key verification rule and the duplicate value verification rule are used for the uniqueness detection of asset data. The value range verification rule is used for the consistency and accuracy detection of asset data. The email address verification rule and the telephone number verification rule are used for the validity and accuracy detection of asset data. Among them, the null value verification rule, the primary key verification rule, and the duplicate value verification rule are all configured through SQL statements. The value range verification rule, the email address verification rule, and the telephone number verification rule are all configured through regular expressions. SQL statements provide a variety of constraint mechanisms to ensure the integrity and uniqueness verification of asset data and improve the quality of asset data. Regular expressions can quickly perform complex matching and control on strings. Through regular expressions, asset data that meets consistency, accuracy, and validity can be efficiently screened out. By jointly performing quality detection on asset data through SQL statements and regular expressions, more efficient, flexible, and accurate asset data detection can be achieved.

[0036] The null value verification rule is a key method for asset data verification. When asset data is entered or updated, it checks whether the required fields have been correctly filled to ensure that the data fields are not left blank, thereby maintaining the integrity of asset data and preventing the loss of necessary information. Among them, the null value in asset data usually refers to that no value is entered in the data field or an invalid value such as an empty string or a space is entered. By controlling the verification rule, the reliability and usability of asset data can be improved, ensuring the transmission and recording of key information in the business process and avoiding business interruptions or errors. The specific process of verifying by the null value verification rule is as follows: (1) Define the null value verification rule: clearly specify which fields need to be verified for null values and set the corresponding verification rules; (2) Verify before data entry: Before the user enters asset data, check whether the required fields are empty and give corresponding prompts; (3) Verify when data is submitted: Before the user submits asset data, check all required fields again to ensure no omission; (4) Exception handling: If the null value verification fails, the asset data cannot be saved or submitted, and appropriate error messages and correction guidance are provided. Therefore, set the non-null constraint for asset data fields at the database level to ensure that the asset data is not empty when saved to the database. The example is as follows:

[0037] select count(*)checkCount from tablename where columnname is null or columnname=”;

[0038] checkSql.append("select count(*) checkCount from");

[0039] checkSql.append(qualitySchemeItem.getTableName()).append(WHERE).append(qualitySchemeItem.getColumnName()).append(" is null");

[0040] checkSql.append("or").append(qualitySchemeItem.getColumnName()).append(" = ”");

[0041] The primary key verification rule is a key mechanism to ensure the uniqueness of asset data. The primary key is a combination of one or more fields used to uniquely identify each record in a database table. When entering or updating asset data, it is necessary to check whether the set primary key value is unique and conforms to the specified format or requirements, ensuring that each record is uniquely identifiable in the database and avoiding data duplication or conflicts. The specific process for verifying the primary key verification rule is as follows: (1) Define the primary key: When designing the database table, clearly specify which field or combination of fields is used as the primary key; (2) Verify before data entry: Before the user enters asset data, check whether the primary key value already exists and give corresponding prompts; (3) Verify when updating data: When updating a record, ensure that the new primary key value does not conflict with the existing records; (4) Exception handling: If the primary key verification fails, provide an appropriate error message and guide the user on how to modify the data to meet the primary key constraint. In programming logic, a query check is performed before data entry or update. The example is as follows:

[0042] SELECT count(*) checkCount FROM USER_CONSTRAINTS WHERE

[0043] CONSTRAINT_TYPE = 'P' and table_name = "";

[0044] checkSql.append("SELECT count(*) checkCount FROM USER_CONSTRAINTS WHERE

[0045] CONSTRAINT_TYPE = 'P' and Table_name = ′ ");

[0046] checkSql.append(qualitySchemeItem.getTableName()).append("′");

[0047] Duplicate value verification is another key data validation method in data quality management. It checks for the existence of identical data records or field values, prevents the entry of duplicate data, ensures that the saved asset data is unique, and avoids asset data redundancy and conflicts. The specific process of duplicate value verification is as follows: (1) Define duplicate value verification rules: Based on business requirements and standards, clarify which fields or records need to be verified for duplicate values; (2) Pre-data entry verification: Before users enter asset data, check for the existence of identical records or field values; (3) Post-data entry verification: After asset data is entered, periodic or real-time duplicate value checks are required to ensure the continuous uniqueness of the data; (4) Process duplicate data: If duplicate data is found, through corresponding processing mechanisms, including single-field verification, multi-field combination verification, and fuzzy matching verification, perform operations such as merging records and deleting duplicate items. An example of query checking before data entry in programming logic is as follows:

[0048] select count(1) checkCount, columnname as check_column from tablename group by columnname having count(1)>1;

[0049] checkSql.append("select count(1) checkCount,

[0050] ").append(qualitySchemeItem.getColumnName()).append(" as check_column from");

[0051] checkSql.append(qualitySchemeItem.getTableName()).append(" group by

[0052] ").append(qualitySchemeItem.getColumnName());

[0053] checkSql.append("HAVING COUNT(1)>1");

[0054] The value range verification rule is used to ensure that the asset data values input or recorded are within the given valid range, thereby guaranteeing the accuracy and consistency of the data. The specific process of verification through the value range verification rule is as follows: (1) Set the range: According to business requirements and standards, set reasonable upper and lower limits for each data field; (2) Input verification: When the user inputs asset data, check whether the input value is within the set range; (3) Feedback mechanism: If the input value exceeds the range, give a clear prompt or warning and require the user to re-enter; (4) Record and audit: Record the verification process and results of the asset data for subsequent audit and traceability. Set field constraint conditions at the database level, such as using the CHECK constraint to ensure that the data values are within the specified range. The example is as follows:

[0055] select count(1)checkCount from tableName where columnname in();

[0056] checkSql.append("select count(1)checkCount from

[0057] ").append(qualitySchemeItem.getTableName()).append(WHERE);

[0058] checkSql.append(qualitySchemeItem.getColumnName()).append("in(").

[0059] The email address verification rule and the phone number verification rule are used to detect the validity and accuracy of asset data. Use regular expressions to ensure that the input email address conforms to the standard format, [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$; The phone number verification formulates corresponding regular expressions for verification according to the phone number formats of different countries or regions, such as

[0060] \+?[0-9]{1,4}[-.]?(\([0-9]{1,3}\)[-.]?|[0-9]{1,4})[-.]?[0-9]{1,4}[-.]?[0-9]{1,9}$.

[0061] Step S3: Perform asset data quality detection on the asset data through the quality detection rules and summarize the quality detection results of the asset data.

[0062] Step S4: Such as Figure 2, calculate the comprehensive score based on the integrity score, accuracy score, consistency score, validity score, and uniqueness score according to the quality inspection results of the asset data. Calculate the comprehensive score based on the integrity score, accuracy score, consistency score, validity score, and uniqueness score according to the quality inspection results of the asset data. Through multi-dimensional asset data evaluation, the quality of asset data can be evaluated more comprehensively, ensuring the correctness of asset data evaluation. The calculation process of the comprehensive score of asset data is as follows:

[0063] F = αF 1 + βF 2 + γF 3 + δF 4 + εF 5

[0064] Among them, F represents the comprehensive score of the asset data, F 1 represents the integrity score of the asset data, α represents F 1 's weight, F 2 represents the accuracy score of the asset data, β represents F 2 's weight, F 3 represents the consistency score of the asset data, γ represents F 3 's weight, F 4 represents the validity score of the asset data, δ represents F 4 's weight, F 5 represents the uniqueness score of the asset data, ε represents F 5 's weight.

[0065] The calculation process of the integrity score of the asset data in the present invention is as follows:

[0066] Integrity score of asset data = (∑(number of non-null values of field i in asset data / total number of field i in asset data) × weight of field i in asset data) / ∑ weight of field i × 100 + integrity compensation score of asset data;

[0067] Among them, the integrity compensation score of the asset data is determined by interpolation for the fields with a large number of missing non-null values in the asset data, and is determined according to the integrity of the asset data after filling. If the integrity of the asset data after filling exceeds the preset integrity threshold, the integrity compensation score of the asset data is given. If the integrity of the asset data is significantly improved after filling, the integrity compensation score of the asset data is set to 6 - 10 points; if the integrity of the asset data reaches the preset integrity threshold after filling, the integrity compensation score of the asset data is set to 1 - 5 points; otherwise, the integrity compensation score of the asset data is 0.

[0068] The calculation process of the accuracy score of the asset data in the present invention is as follows:

[0069] Accuracy score of asset data = (Number of accurate fields in asset data / Total number of fields in asset data) × 100 - Accuracy compensation score of asset data;

[0070] Among them, the accuracy compensation score of asset data is determined according to the proportion of accurate fields in asset data. If the proportion of accurate fields in asset data is lower than the preset accuracy threshold, an accuracy compensation score is given to the asset data. If the accuracy of the asset data seriously affects business decisions, the accuracy compensation score of the asset data is set to 6 - 10 points; if the accuracy of the asset data is lower than the preset defect conversion threshold but has no impact on business decisions, the accuracy compensation score of the asset data is set to 1 - 5 points; otherwise, the accuracy compensation score of the asset data is 0.

[0071] The calculation process of the consistency score of asset data in the present invention is as follows:

[0072] Consistency score of asset data = (Number of consistent fields in asset data / Total number of fields in asset data) × 100.

[0073] The calculation process of the validity score of asset data in the present invention is as follows:

[0074] Validity score of asset data = (Number of valid fields in asset data / Total number of fields in asset data) × 100.

[0075] The calculation process of the uniqueness score of asset data in the present invention is: Use the hash algorithm to perform hash processing on the uniqueness identification fields in the asset data, and calculate the uniqueness score of the asset data = (Number of fields with unique hash values in the asset data / Total number of fields in the asset data) × 100 - Uniqueness compensation score of the asset data, where the uniqueness compensation score of the asset data is determined according to the number of repeated hash values in the asset data fields.

[0076] Step S5: Perform hierarchical management of asset data according to the comprehensive score. Specifically, if the comprehensive score of the asset data is above 90 points, the level of the asset data is excellent; if the comprehensive score range of the asset data is [80, 90), the level of the asset data is good; if the comprehensive score range of the asset data is [60, 80), the level of the asset data is qualified; if the comprehensive score range of the asset data is [0, 60), the level of the asset data is unqualified, and report the quality of the unqualified asset data. The present invention calculates the comprehensive score based on multi - dimensional scoring and conducts hierarchical classification of asset data, which can formulate asset data management strategies targeted, improve the management efficiency and effect of asset data, and can timely discover problems existing in asset data according to the scores of each dimension.

[0077] In one technical solution of the present invention, there is also provided a computer-readable storage medium storing a computer program, and the computer program causes a computer to execute an asset data grading method based on quality detection.

[0078] In one technical solution of the present invention, there is also provided an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, an asset data grading method based on quality detection is implemented.

[0079] In one technical solution of the present invention, there is also provided a computer program product including a computer program, and when the computer program is executed by a processor, an asset data grading method based on quality detection is implemented.

[0080] In the embodiments disclosed in the present application, the computer storage medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0081] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0082] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art of this technology, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A method for grading asset data based on quality inspection, characterized in that: The specific steps include: Step S1, synchronously collect asset data from heterogeneous data sources; Step S2: configure quality detection rules for SQL statements and regular expressions; Step S3: Perform asset data quality inspection on the asset data through quality inspection rules, and summarize the quality inspection results of the asset data; Step S4: Calculate a comprehensive score based on the completeness score, accuracy score, consistency score, effectiveness score, and uniqueness score according to the quality inspection results of the asset data; Step S5: Perform hierarchical management of asset data according to the comprehensive score, and report the quality of the lowest-level asset data.

2. The asset data classification method based on quality detection according to claim 1 is characterized in that: The quality detection rules include: null value verification rules, primary key verification rules, duplicate value verification rules, value range verification rules, email address verification rules and telephone number verification rules; the null value verification rules are used for integrity detection of asset data, the primary key verification rules and duplicate value verification rules are used for uniqueness detection of asset data, the value range verification rules are used for consistency and accuracy detection of asset data, and the email address verification rules and telephone number verification rules are used for validity and accuracy detection of asset data.

3. The asset data classification method based on quality inspection according to claim 2 is characterized in that: The null value check rule, primary key check rule and duplicate value check rule are all configured through SQL statements, and the value range check rule, email address check rule and phone number check rule are all configured through regular expressions.

4. The asset data classification method based on quality inspection according to claim 1 is characterized in that: The calculation process of the comprehensive score of the asset data in step S4 is: F=αF1+βF2+γF3+δF4+εF5 Among them, F represents the comprehensive score of asset data, F1 represents the integrity score of asset data, α represents the weight of F1, F2 represents the accuracy score of asset data, β represents the weight of F2, F3 represents the consistency score of asset data, γ represents the weight of F3, F4 represents the validity score of asset data, δ represents the weight of F4, F5 represents the uniqueness score of asset data, and ε represents the weight of F5.

5. The asset data classification method based on quality inspection according to claim 4 is characterized in that: The calculation process of the integrity score of the asset data is as follows: The integrity score of asset data = (∑(the number of non-null values ​​of field i in asset data / the total number of field i in asset data)×the weight of field i in asset data) / ∑the weight of field i×100+the integrity compensation score of asset data; Among them, the integrity compensation score of the asset data is determined based on the integrity of the asset data after filling. If the integrity of the asset data after filling exceeds the preset integrity threshold, the integrity compensation score of the asset data is given, otherwise, the integrity compensation score of the asset data is 0.

6. The asset data classification method based on quality inspection according to claim 4 is characterized in that: The calculation process of the accuracy score of the asset data is: Accuracy score of asset data = (number of accurate fields in asset data / total number of fields in asset data) × 100 - accuracy compensation score of asset data; Among them, the accuracy compensation score of the asset data is determined according to the proportion of accurate fields in the asset data. If the proportion of accurate fields in the asset data is lower than the preset accuracy threshold, the accuracy compensation score of the asset data is given; otherwise, the accuracy compensation score of the asset data is 0.

7. The asset data classification method based on quality inspection according to claim 4 is characterized in that: The calculation process of the consistency score of the asset data is as follows: The consistency score of asset data = (the number of consistent fields in asset data / the total number of fields in asset data) × 100.

8. The asset data classification method based on quality inspection according to claim 4 is characterized in that: The calculation process of the effectiveness score of the asset data is as follows: The validity score of asset data = (the number of valid fields in the asset data / the total number of fields in the asset data) × 100.

9. The asset data classification method based on quality inspection according to claim 4 is characterized in that: The calculation process of the uniqueness score of the asset data is: use a hash algorithm to hash the unique identification field in the asset data, and calculate the uniqueness score of the asset data = (the number of fields with unique hash values ​​in the asset data / the total number of fields in the asset data) × 100-the uniqueness compensation score of the asset data, wherein the uniqueness compensation score of the asset data is determined based on the number of repeated hash values ​​in the asset data field.

10. The asset data classification method based on quality inspection according to claim 4, characterized in that: The specific process of hierarchical management of asset data according to the comprehensive score in step S5 is: if the comprehensive score of the asset data is above 90 points, the grade of the asset data is excellent; if the comprehensive score range of the asset data is [80,90), the grade of the asset data is good; if the comprehensive score range of the asset data is [60,80), the grade of the asset data is qualified; if the comprehensive score range of the asset data is [0,60), the grade of the asset data is unqualified.

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