A method and system for opening inspection and testing data based on data classification and grading

Abstract

The present invention discloses a method and system for opening inspection and testing data based on data classification and grading, and relates to the technical field of opening inspection and testing data. The present invention aims to solve the problem of inconsistent data classification standards of different laboratories through multi-dimensional analysis and comprehensive evaluation, and improve data interoperability and sharing efficiency. By performing a series of data-driven analysis methods on initial laboratory data, classification indicators of laboratory data, namely, comprehensive scores, can be obtained. According to the unified classification and screening method of comprehensive scores, the problem of inconsistent classification standards can be effectively solved. Furthermore, in order to make the process of obtaining comprehensive scores more scientific, the sensitivity, importance and compliance of laboratory data are analyzed to obtain various composite indexes, and on the premise of the composite indexes, a more standardized, accurate and scientific comprehensive score can be obtained, thereby effectively improving the accuracy of data classification standards.

Description

Technical Field

[0001] The present invention relates to the technical field of inspection and testing data opening, and in particular to an inspection and testing data opening method and system based on data classification and grading. Background Art

[0002] The inspection and testing data opening method based on data classification and grading is a technical means to balance the relationship between data sharing needs and information security protection. By classifying data according to its properties such as sensitivity and importance, and setting corresponding security levels according to different categories, a reasonable opening strategy can be formulated for each type of data. This method can not only promote the efficient use of data resources, but also effectively prevent the occurrence of security incidents such as data leakage.

[0003] In laboratory environments, data openness methods based on data classification and grading have been widely used. Laboratories usually deal with a large amount of experimental data, which includes both publicly available information and highly sensitive research results and personal privacy information. Therefore, how to maximize the value of data while ensuring data security has become one of the important issues facing laboratories.

[0004] At present, many laboratories have adopted a data classification and grading method to manage and open data. For example, for some low-sensitivity basic scientific research data, the laboratory may choose to make it fully public to facilitate academic exchanges and resource sharing; while for data involving commercial secrets or personal privacy, access rights will be strictly controlled and only authorized personnel or institutions will be allowed to view it.

[0005] In addition, in order to better implement data classification and grading management, the laboratory has also introduced a variety of technical supports, such as data encryption, access control, audit tracking, etc., to ensure the security and integrity of the data. At the same time, the laboratory will also regularly maintain and update the data management system to adapt to the ever-changing data security needs.

[0006] Although the inspection and testing data opening method based on data classification and grading has achieved remarkable results in laboratories, it still faces a major problem in practical application:

[0007] Different laboratories need to develop different data classification standards based on their own research directions, data types, and security requirements. For example, some laboratories may classify data involving personal privacy as the most sensitive level, while other laboratories believe that such data only requires a medium level of protection. This difference makes it difficult to find a common classification standard when exchanging data between different laboratories, which in turn affects the interoperability and sharing efficiency of data.

[0008] Even within the same laboratory, different project teams or researchers may adopt different data classification standards due to their respective business needs. For example, one project team may consider a certain type of experimental data to be highly sensitive, while another project team may consider such data to be less sensitive. Such internal differences may easily lead to confusion in data management and affect the consistency and reliability of data.

[0009] Therefore, the prior art urgently needs a technical solution for an inspection and testing data opening method and system based on data classification and grading. Summary of the invention

[0010] In order to solve the above technical problems, the present invention provides a method for opening inspection and testing data based on data classification and grading, which specifically includes the following steps:

[0011] Step S1, analyzing the laboratory data, obtaining a comprehensive score of the laboratory data according to the analysis result, and classifying the laboratory data according to the comprehensive score;

[0012] Step S1a, obtaining the sensitivity index, importance index and compliance index of each laboratory data;

[0013] Step S1b, based on the sensitivity index, importance index and compliance index of each laboratory data, obtaining a composite index of each laboratory data, wherein the composite index includes a first composite index, a second composite index, a third composite index and a comprehensive composite index;

[0014] Step S1b1, obtain the product of the sensitivity index and the importance index, and based on the ratio of the product to the compliance index, obtain the first composite index of each laboratory data ;

[0015] Step S1b2: Obtain the product of the importance index and the compliance index, and obtain the second composite index of each laboratory data based on the ratio of the product to the sensitivity index. ;

[0016] Step S1b3: Obtain the product of the sensitivity index and the compliance index, and based on the ratio of the product to the importance index, obtain the third composite index of each laboratory data. ;

[0017] Step S1b4, obtaining a comprehensive composite index of each laboratory data according to the first composite index, the second composite index and the third composite index of each laboratory data;

[0018] Among them, the calculation formula for the comprehensive composite index of each laboratory data is:

[0019] ;

[0020] in, Represents the comprehensive composite index of the i-th laboratory data; Represents the first composite index of the i-th laboratory data ; Represents the second composite index of the i-th laboratory data ; Represents the third composite index of the i-th laboratory data ; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; represents the compliance index of the i-th laboratory data;

[0021] Step S1c, calculating the weight of each laboratory data according to the comprehensive composite index of each laboratory data;

[0022] Step S1c1, obtaining the sum of the comprehensive composite index of each laboratory data;

[0023] Step S1c2, obtaining the weight of each laboratory data according to the sum of the comprehensive composite index of each laboratory data and the comprehensive composite index of each laboratory data;

[0024] The calculation formula for obtaining the weight of each laboratory data is:

[0025] ;

[0026] in, Represents the weight of the i-th laboratory data; represents the comprehensive composite index of the i-th laboratory data; n represents the total number of laboratory data;

[0027] Step S1d, obtaining a comprehensive score for each laboratory data according to the sensitivity index, importance index, compliance index, first composite index, second composite index, third composite index, comprehensive composite index and weight of each laboratory data;

[0028] Step S1d1, obtaining a basic score for each laboratory data according to the sensitivity index, importance index and legal compliance index of each laboratory data;

[0029] The calculation formula for obtaining the basic score of each laboratory data is:

[0030] ;

[0031] in, represents the basic score of the ith laboratory data; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; represents the compliance index of the i-th laboratory data;

[0032] Step S1d2, obtaining a composite score of each laboratory data according to the first composite index, the second composite index and the third composite index of each laboratory data;

[0033] The calculation formula for obtaining the composite score of each laboratory data is:

[0034] ;

[0035] in, represents the composite score of the ith laboratory data; Represents the first composite index of the i-th laboratory data ; Represents the second composite index of the i-th laboratory data ; Represents the third composite index of the i-th laboratory data ; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; represents the compliance index of the i-th laboratory data;

[0036] Step S1d3, combining the basic score, composite score and weight of each laboratory data to obtain a comprehensive score for each laboratory data;

[0037] The calculation formula for obtaining the comprehensive score of each laboratory data is:

[0038] ;

[0039] in, represents the comprehensive score of the i-th laboratory data; Represents the weight of the i-th laboratory data; represents the basic score of the ith laboratory data; represents the composite score of the ith laboratory data;

[0040] Step S1e, setting at least two thresholds for the comprehensive score, and classifying each laboratory data according to the thresholds;

[0041] Step S2: Based on the classification results, perform classification processing on the laboratory data;

[0042] Step S3: Make data opening decisions based on the classification and grading results of laboratory data.

[0043] A system for opening inspection and testing data based on data classification and grading, which implements the above-mentioned method for opening inspection and testing data based on data classification and grading, includes the following modules:

[0044] Data analysis and classification module: used to analyze laboratory data, obtain a comprehensive score of the laboratory data based on the analysis results, and classify the laboratory data based on the comprehensive score;

[0045] Data classification module: connected to the data analysis and classification module, used to perform classification processing on the laboratory data based on the classification results;

[0046] Decision-making module: connected to the data classification module, used to make data opening decisions based on the classification and classification results of laboratory data.

[0047] The embodiments of the present invention have the following technical effects:

[0048] The present invention aims to solve the problem of inconsistent data classification standards between different laboratories or project groups through multi-dimensional analysis and comprehensive evaluation, and to improve data interoperability and sharing efficiency. Specifically, by performing a series of data-driven analysis methods on the initial laboratory data, the classification index of the laboratory data, that is, the comprehensive score, is obtained. The classification and screening method based on the unified comprehensive score can effectively solve the problem of inconsistent classification standards. Furthermore, in order to make the process of obtaining the comprehensive score more scientific and accurate, the sensitivity, importance and compliance of the laboratory data are analyzed to obtain various composite indexes, and on the premise of the composite indexes, a more standardized, accurate and scientific comprehensive score is obtained, thereby effectively improving the accuracy of the data classification standard. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a flow chart of a method for opening inspection and testing data based on data classification and grading provided by an embodiment of the present invention;

[0051] Figure 2 It is a framework diagram of an inspection and testing data opening system based on data classification and grading provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. 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 belong to the scope of protection of the present invention.

[0053] Embodiment 1: Figure 1 As shown, the present invention provides a method for opening inspection and testing data based on data classification and grading, comprising the following steps:

[0054] Step S1, analyzing the laboratory data, obtaining a comprehensive score of the laboratory data according to the analysis result, and classifying the laboratory data according to the comprehensive score.

[0055] Step S1a, obtaining the sensitivity index, importance index and compliance index of each laboratory data.

[0056] Extract all data sets that need to be analyzed from the laboratory database, including but not limited to text data, numerical data, image data, etc. Establish a keyword library containing common sensitive information, and use keyword matching technology to identify sensitive information in each laboratory data, such as personal name, ID number, telephone number, email address, etc. Calculate the corresponding score based on the number and type of sensitive information identified, and then add up the scores of all identified sensitive information to obtain the sensitivity index of each laboratory data.

[0057] The data sets that need to be analyzed also include but are not limited to experimental results, research papers, project reports, etc. The number of citations of each laboratory data in relevant literature is then counted. The more citations, the higher the importance. The data is evaluated by field experts for its criticality in scientific research projects. The more critical the data, the higher its importance. Based on the number of literature citations and the criticality of the project, the corresponding score is calculated for each laboratory data. Finally, the above scores are combined to obtain the importance index of each laboratory data.

[0058] The data sets that need to be analyzed also include but are not limited to personal data, business data, scientific research data, etc. Then, check whether each laboratory data complies with the data management decisions and standards within the laboratory, and certify the compliance of the data through a third-party organization to obtain a compliance score. Then, based on the results of internal review and third-party certification, calculate the corresponding score for each laboratory data. Finally, combine the above scores to obtain the compliance index of each laboratory data.

[0059] Among them, the above-mentioned compliance index, importance index, and sensitivity index are all obtained through existing technologies, and this embodiment will not be described in detail.

[0060] Step S1b, based on the sensitivity index, importance index and compliance index of each laboratory data, a composite index of each laboratory data is obtained, wherein the composite index includes a first composite index, a second composite index, a third composite index and a comprehensive composite index.

[0061] Step S1b1, obtain the product of the sensitivity index and the importance index, and based on the ratio of the product to the compliance index, obtain the first composite index of each laboratory data .

[0062] It is worth noting that the first composite index takes into account the sensitivity and importance of data, as well as the constraints of compliance. The higher the sensitivity and importance, the greater the value of the data, but the lower the compliance, the greater the risk of the data; by multiplying the sensitivity and importance, the value of the data can be more comprehensively assessed, and by dividing it by the compliance index, the risk of the data can be effectively controlled to ensure that the data complies with the requirements of laws and regulations during the process of opening and sharing, and to find a balance between the value and risk of the data, avoiding over-emphasis on one aspect and neglecting other aspects.

[0063] Step S1b2: Obtain the product of the importance index and the compliance index, and obtain the second composite index of each laboratory data based on the ratio of the product to the sensitivity index. .

[0064] It is worth noting that the second composite index takes into account the importance and compliance of data, while also taking into account the constraints of sensitivity. The higher the importance and compliance, the higher the credibility and reliability of the data, but the higher the sensitivity, the greater the risk of the data; by multiplying the importance and compliance, the reliability and credibility of the data can be more accurately assessed, and by dividing by the sensitivity index, the risk of the data can be effectively controlled to ensure that sensitive information will not be leaked during the process of opening and sharing the data, and to find a balance between the reliability and risk of the data, avoiding over-emphasis on one aspect and neglecting other aspects.

[0065] Step S1b3: Obtain the product of the sensitivity index and the compliance index, and based on the ratio of the product to the importance index, obtain the third composite index of each laboratory data. .

[0066] It is worth noting that the third composite index takes into account the sensitivity and compliance of data, while also taking into account the constraints of importance. The higher the sensitivity and compliance, the higher the security of the data, but the higher the importance, the greater the value of the data; by multiplying the sensitivity and compliance, the security of the data can be more comprehensively evaluated, and by dividing by the importance index, the value of the data can be effectively controlled to ensure that important information is not overly exposed during the opening and sharing of the data, and to find a balance between the security and value of the data, avoiding over-emphasis on one aspect and neglecting other aspects.

[0067] Step S1b4, obtaining a comprehensive composite index of each laboratory data according to the first composite index, the second composite index and the third composite index of each laboratory data.

[0068] It is worth noting that the comprehensive composite index takes into account the sensitivity, importance and compliance of the data. Through the cube root operation, it ensures that the weights of each composite index are relatively balanced, reflecting the comprehensive value and risk of the data. By combining the three composite indexes, the overall characteristics of the data can be evaluated more comprehensively. At the same time, the cube root operation ensures that the weights of each composite index are relatively balanced, avoiding the excessive influence of a certain index on the result. The comprehensive composite index provides a scientific basis for data classification and grading, which helps to make reasonable data openness decisions and ensure the security and rational use of data. That The calculation formula for the comprehensive composite index of each laboratory data is:

[0069] ;

[0070] in, Represents the comprehensive composite index of the i-th laboratory data; Represents the first composite index of the i-th laboratory data ; Represents the second composite index of the i-th laboratory data ; Represents the third composite index of the i-th laboratory data ; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; Represents the compliance index of the i-th laboratory data.

[0071] Step S1c, calculating the weight of each laboratory data according to the comprehensive composite index of each laboratory data;

[0072] Step S1c1, obtaining the sum of the comprehensive composite index of each laboratory data.

[0073] Step S1c2: Obtain the weight of each laboratory data according to the sum of the comprehensive composite index of each laboratory data and the comprehensive composite index of each laboratory data.

[0074] The calculation formula for obtaining the weight of each laboratory data is:

[0075] ;

[0076] in, Represents the weight of the i-th laboratory data; represents the comprehensive composite index of the i-th laboratory data; n represents the total number of laboratory data.

[0077] Step S1d, obtaining a comprehensive score for each laboratory data according to the sensitivity index, importance index, compliance index, first composite index, second composite index, third composite index, comprehensive composite index and weight of each laboratory data;

[0078] Step S1d1, obtaining a basic score for each laboratory data according to the sensitivity index, importance index and legal compliance index of each laboratory data;

[0079] The calculation formula for obtaining the basic score of each laboratory data is:

[0080] ;

[0081] in, represents the basic score of the ith laboratory data; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; Represents the compliance index of the i-th laboratory data.

[0082] Step S1d2: Obtain a composite score for each laboratory data according to the first composite index, the second composite index and the third composite index of each laboratory data.

[0083] The calculation formula for obtaining the composite score of each laboratory data is:

[0084] ;

[0085] in, represents the composite score of the ith laboratory data; Represents the first composite index of the i-th laboratory data ; Represents the second composite index of the i-th laboratory data ; Represents the third composite index of the i-th laboratory data ; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; represents the compliance index of the i-th laboratory data;

[0086] Step S1d3: Combine the basic score, composite score and weight of each laboratory data to obtain a comprehensive score for each laboratory data.

[0087] It is worth noting that, in step S1d1 to step S1d3, the method of first obtaining the basic score, then obtaining the composite score, and finally obtaining the comprehensive score can not only comprehensively evaluate the characteristics of the data, but also find a balance between the value and risk of the data, and provide a scientific basis for the classification, grading and open decision-making of the data; specifically, by adding the logarithmic values ​​of the sensitivity index, the importance index and the compliance index, the basic characteristics of the data can be comprehensively evaluated, and by the square root of the sum of the squares of the first composite index, the second composite index and the third composite index, the complex characteristics of the data can be comprehensively evaluated, and then combined with the basic score, the composite score and the weight, the comprehensive characteristics of the data can be comprehensively evaluated, ensuring the comprehensiveness and accuracy of the evaluation, as well as the fairness and rationality of the evaluation results;

[0088] The calculation formula for obtaining the comprehensive score of each laboratory data is:

[0089] ;

[0090] in, represents the comprehensive score of the i-th laboratory data; Represents the weight of the i-th laboratory data; represents the basic score of the ith laboratory data; Represents the composite score of the ith laboratory data.

[0091] Step S1e, setting at least two thresholds for the comprehensive score, and classifying each laboratory data according to the thresholds;

[0092] Exemplarily, at least two thresholds are set for the comprehensive score, including but not limited to a first threshold, a second threshold and a third threshold, and the first threshold, the second threshold and the third threshold are used to perform classification and screening of laboratory data; specifically, laboratory data with a comprehensive score less than the first threshold are classified into the first category; data with a comprehensive score greater than or equal to the first threshold and less than the second threshold are classified into the second category; finally, data with a comprehensive score greater than or equal to the third threshold are classified into the third category.

[0093] Step S2: Based on the classification results, perform classification processing on the laboratory data.

[0094] Based on the classification results, the above three types of data are graded and the security level can be set during the classification. The first type of data can be divided into public level; the second type of data can be divided into internal use level; and the third type of data can be divided into confidential level.

[0095] Step S3: Make data opening decisions based on the classification and grading results of laboratory data.

[0096] Finally, according to the results of the above classification and grading, make corresponding data opening decisions. Preferably, the opening decision for public-level data can be: public release without review, and provide data download links to allow free download and use; the opening decision for internal-use data can be: internal sharing, which requires a simple review process, and provides a data query interface, limits the number of downloads, and records access logs to ensure data traceability; the opening decision for confidential data can be: strict control of access rights, requiring high-level approval, and providing restricted data query services, prohibiting downloads, and regularly auditing access records to ensure data security.

[0097] Embodiment 2: Figure 2 As shown, the present invention also proposes a system for opening inspection and testing data based on data classification and grading, which executes the above-mentioned method for opening inspection and testing data based on data classification and grading, and includes the following modules:

[0098] Data analysis and classification module: used to analyze laboratory data, obtain a comprehensive score of the laboratory data based on the analysis results, and classify the laboratory data based on the comprehensive score;

[0099] Data classification module: connected to the data analysis and classification module, used to perform classification processing on the laboratory data based on the classification results;

[0100] Decision-making module: connected to the data classification module, used to make data opening decisions based on the classification and classification results of laboratory data.

[0101] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0102] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for opening inspection and testing data based on data classification and grading, characterized in that: The following steps are involved: Step S1, analyzing the laboratory data, obtaining a comprehensive score of the laboratory data according to the analysis result, and classifying the laboratory data according to the comprehensive score; the laboratory data includes: text data, numerical data, and image data; analyzing the laboratory data, obtaining a comprehensive score of the laboratory data according to the analysis result, and classifying the laboratory data according to the comprehensive score specifically includes: Step S1a, obtaining the sensitivity index, importance index and compliance index of each laboratory data; Step S1b, based on the sensitivity index, importance index and compliance index of each laboratory data, a composite index of each laboratory data is obtained, wherein the composite index includes a first composite index, a second composite index, a third composite index and a comprehensive composite index; the composite index of each laboratory data is obtained based on the sensitivity index, importance index and compliance index of each laboratory data, including: Step S1b1, obtain the product of the sensitivity index and the importance index, and based on the ratio of the product to the compliance index, obtain the first composite index of each laboratory data ; Step S1b2: Obtain the product of the importance index and the compliance index, and obtain the second composite index of each laboratory data based on the ratio of the product to the sensitivity index. ; Step S1b3: Obtain the product of the sensitivity index and the compliance index, and based on the ratio of the product to the importance index, obtain the third composite index of each laboratory data. ; Step S1b4, obtaining a comprehensive composite index of each laboratory data according to the first composite index, the second composite index and the third composite index of each laboratory data; Step S1c, calculating the weight of each laboratory data according to the comprehensive composite index of each laboratory data; Step S1d, obtaining a comprehensive score for each laboratory data according to the sensitivity index, importance index, compliance index, first composite index, second composite index, third composite index, comprehensive composite index and weight of each laboratory data; Step S1e, setting at least two thresholds for the comprehensive score, and classifying each laboratory data according to the thresholds; Step S2: Based on the classification results, perform classification processing on the laboratory data; Step S3: Make data opening decisions based on the classification and grading results of laboratory data.

2. The method for opening inspection and testing data based on data classification and grading according to claim 1 is characterized in that: The weight of each laboratory data is calculated based on the comprehensive composite index of each laboratory data, including: Step S1c1, obtaining the sum of the comprehensive composite index of laboratory data; Step S1c2, obtaining the weight of each laboratory data according to the sum of the comprehensive composite indexes of the laboratory data and the comprehensive composite index of each laboratory data; The calculation formula for obtaining the weight of each laboratory data is: ； in, Represents the weight of the i-th laboratory data; represents the comprehensive composite index of the i-th laboratory data; n represents the total number of laboratory data.

3. The method for opening inspection and testing data based on data classification and grading according to claim 1 is characterized in that: According to the sensitivity index, importance index, compliance index, first composite index, second composite index, third composite index, comprehensive composite index and weight of each laboratory data, a comprehensive score of each laboratory data is obtained, including: Step S1d1, obtaining a basic score for each laboratory data according to the sensitivity index, importance index and legal compliance index of each laboratory data; Step S1d2, obtaining a composite score of each laboratory data according to the first composite index, the second composite index and the third composite index of each laboratory data; Step S1d3: Combine the basic score, composite score and weight of each laboratory data to obtain a comprehensive score for each laboratory data.

4. The method for opening inspection and testing data based on data classification and grading according to claim 1 is characterized in that: The calculation formula for obtaining the comprehensive composite index of each laboratory data is: ； in, Represents the comprehensive composite index of the i-th laboratory data; Represents the first composite index of the i-th laboratory data ; Represents the second composite index of the i-th laboratory data ; The third composite index representing the i-th laboratory data ; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; Represents the compliance index of the i-th laboratory data.

5. The method for opening inspection and testing data based on data classification and grading according to claim 3 is characterized in that: The calculation formula for obtaining the basic score of each laboratory data is: ； in, represents the basic score of the ith laboratory data; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; Represents the compliance index of the i-th laboratory data.

6. The method for opening inspection and testing data based on data classification and grading according to claim 3 is characterized in that: The calculation formula for obtaining the composite score of each laboratory data is: ； in, represents the composite score of the ith laboratory data; Represents the first composite index of the i-th laboratory data ; Represents the second composite index of the i-th laboratory data ; Represents the third composite index of the i-th laboratory data ; represents the sensitivity index of the i-th laboratory data; Represents the importance index of the i-th laboratory data; Represents the compliance index of the i-th laboratory data.

7. The method for opening inspection and testing data based on data classification and grading according to claim 3 is characterized in that: The calculation formula for obtaining the comprehensive score of each laboratory data is: ； in, represents the comprehensive score of the i-th laboratory data; Represents the weight of the i-th laboratory data; represents the basic score of the ith laboratory data; Represents the composite score of the ith laboratory data.

8. A system for opening inspection and testing data based on data classification and grading, used for executing a method for opening inspection and testing data based on data classification and grading according to any one of claims 1 to 7, characterized in that: The system comprises: Data analysis and classification module: used to analyze laboratory data, obtain a comprehensive score of the laboratory data based on the analysis results, and classify the laboratory data based on the comprehensive score; Data classification module: connected to the data analysis and classification module, used to perform classification processing on the laboratory data based on the classification results; Decision-making module: connected to the data classification module, used to make data opening decisions based on the classification and classification results of laboratory data.

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