Data element system availability evaluation method and device, equipment and medium

Abstract

The invention discloses a data element system availability evaluation method and device, equipment and a medium. The method comprises the steps that a three-layer structure model composed of a target layer, an index layer and a scheme layer is constructed based on an AHP analytic hierarchy process; constructing a pairwise comparison judgment matrix for each index of the index layer and each entity of the scheme layer; in a judgment matrix of the index layer, a quantization rule of digits 1-9 is used for representing relative importance; checking the consistency of the judgment matrix; calculating the weight of each index and entity according to the feature vector of the judgment matrix; and calculating the score of each entity in the scheme layer according to the weight of each index in the index layer and the weight of each entity in the scheme layer. According to the AHP analytic hierarchy process, systematic evaluation is introduced, a multi-classification problem in a data element scene is constructed into a multi-level analysis structure model, and the accuracy and credibility of an evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital product service technology, and in particular to a method, device, equipment and medium for evaluating the availability of a data element system. Background Art

[0002] As a new type of production factor, data is the foundation of digitalization, networking, and intelligence. It has been rapidly integrated into various links such as production, distribution, circulation, consumption, and social service management, profoundly changing the mode of production, lifestyle, and social governance. As a traditional production factor, raw data needs to go through a multi-link evolution process from "resources" to "factors" and then to "circulation", and its ownership, form, etc. have undergone significant changes. At the same time, the marketization of data elements should follow the development path of "institutions define business models, and business models choose technical routes." Therefore, from the perspective of data elements, "security and development are the two wings of one body and the two wheels of drive. Security is the guarantee of development, and development is the purpose of security." In the current market, the data element industry chain has been initially formed, covering multiple links such as data collection, data storage, data service providers, data rights confirmation, and data element operation service providers. As the basic service facility for the market-oriented allocation reform of data elements, the data element system undertakes the four basic responsibilities of data rights confirmation, circulation and transaction, income distribution, and security governance. Its representative data element service platforms and solutions include the Lingze Data Elements 2.0 platform.

[0003] The importance of data element systems is becoming increasingly prominent, but the current evaluation methods for their availability have many limitations and shortcomings. Specifically, existing evaluation methods often focus too much on technical availability, such as system functions and system performance, while ignoring the unique attributes of data elements. This single-dimensional evaluation method is difficult to fully reflect the comprehensive effectiveness of data element systems, thus limiting its effectiveness in practical applications.

[0004] In addition, traditional evaluation methods rely on expert experience or simple questionnaires to determine the weights of each evaluation indicator, which makes the weight allocation highly subjective and lacks scientificity and objectivity. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for evaluating the availability of a data element system, which solves the problem in related technologies that the technical evaluation dimension is single, the weight allocation is highly subjective, and it is difficult to fully reflect the comprehensive effectiveness of the data element system.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, a method for evaluating the availability of a data element system is provided, comprising: According to the data element system evaluation indicators and candidate products, a three-layer structure model consisting of a target layer, an indicator layer and a solution layer is constructed based on the AHP hierarchical analysis method; wherein the main purpose scenario of the evaluation is clearly defined in the target layer; the indicator layer includes system security, system performance, security certification capability and functional coverage as key factors to be considered in the evaluation process; the solution layer includes the listed entities of the candidate products; For each indicator of the indicator layer, a judgment matrix for pairwise comparison is constructed; in the judgment matrix of the indicator layer, a quantitative rule of numbers 1 to 9 is used to represent relative importance; Performing consistency check on the judgment matrix of the indicator layer; According to the eigenvector of the judgment matrix of the indicator layer, each column of the judgment matrix is ​​summed, each value of each column is divided by the sum to obtain a value, and then the arithmetic mean is calculated based on each row to obtain a weight value, which is the weight of each indicator of the indicator layer; For each entity of the solution layer, a pairwise comparison judgment matrix is ​​constructed; in the judgment matrix of the solution layer, the quantification rule is used to represent the relative importance; Performing consistency check on the judgment matrix of the solution layer and calculating the weight of each entity of the solution layer; The score of each entity in the solution layer is calculated according to the weight of each indicator in the indicator layer and the weight of each entity in the solution layer.

[0007] In a first possible implementation of the first aspect, the quantization rule is specifically: to perform incremental division according to the degree of relative importance, the corresponding quantization value of equal importance is 1, the corresponding quantization value of slightly important is 3, the corresponding quantization value of relatively strong importance is 5, the corresponding quantization value of extremely important is 7, and the intermediate values ​​of two adjacent judgments are 2, 4, 6, and 8 respectively.

[0008] In a second possible implementation manner of the first aspect, the consistency check specifically includes: Calculate the consistency index CI: in, , ， A is the consistency matrix; is an eigenvalue of the consistency matrix A; yes The maximum eigenvalue of a positive reciprocal matrix of order; , , , are the weights of each indicator or entity respectively; Find The corresponding average random consistency index RI is used to calculate the consistency ratio CR: If CR<0.1, the constructed judgment matrix is ​​considered consistent, otherwise it needs to be modified.

[0009] In a third possible implementation manner of the first aspect, the evaluation method further includes: The positioning and role of the entity in the market are analyzed according to the score.

[0010] In a second aspect, a data element system availability assessment device is provided, comprising: The system construction module is used to construct a three-layer structure model consisting of a target layer, an indicator layer and a solution layer based on the AHP hierarchical analysis method according to the data element system evaluation indicators and candidate products; wherein the main purpose scenario of the evaluation is clearly defined in the target layer; the indicator layer includes system security, system performance, security certification capability and functional coverage as key factors to be considered in the evaluation process; the solution layer includes the listed entities of the candidate products; The indicator layer calculation module is used to construct a pairwise comparison judgment matrix for each indicator of the indicator layer; in the judgment matrix of the indicator layer, a quantitative rule of numbers 1 to 9 is used to represent relative importance; Performing consistency check on the judgment matrix of the indicator layer; According to the eigenvector of the judgment matrix of the indicator layer, each column of the judgment matrix is ​​summed, each value of each column is divided by the sum to obtain a value, and then the arithmetic mean is calculated based on each row to obtain a weight value, which is the weight of each indicator of the indicator layer; A scheme layer calculation module, used for constructing a pairwise comparison judgment matrix for each entity of the scheme layer; in the judgment matrix of the scheme layer, the quantization rule is used to represent the relative importance; Performing consistency check on the judgment matrix of the solution layer and calculating the weight of each entity of the solution layer; The entity score calculation module is used to calculate the score of each entity in the solution layer according to the weight of each indicator in the indicator layer and the weight of each entity in the solution layer.

[0011] In a first possible implementation of the second aspect, the quantization rule is specifically as follows: incremental division is performed according to the degree of relative importance, with equal importance corresponding to a quantization value of 1, slightly important corresponding to a quantization value of 3, relatively strong importance corresponding to a quantization value of 5, extremely important corresponding to a quantization value of 7, and extremely important corresponding to a quantization value of 9, and the intermediate values ​​of two adjacent judgments are 2, 4, 6, and 8 respectively.

[0012] In a second possible implementation manner of the second aspect, the consistency check specifically includes: Calculate the consistency index CI: in, , ， A is the consistency matrix; is an eigenvalue of the consistency matrix A; yes The maximum eigenvalue of a positive reciprocal matrix of order; , , , are the weights of each indicator or entity respectively; Find The corresponding average random consistency index RI is used to calculate the consistency ratio CR: If CR<0.1, the constructed judgment matrix is ​​considered consistent, otherwise it needs to be modified.

[0013] In a third possible implementation manner of the second aspect, the evaluation device further includes: The entity analysis module is used to analyze the positioning and role of the entity in the market according to the score.

[0014] According to a third aspect, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the data element system availability assessment method as described in the first aspect.

[0015] In a fourth aspect, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the data element system availability assessment method as described in the first aspect are implemented.

[0016] The data element system availability evaluation method of the present invention has the following advantages: The AHP hierarchical analysis method improves the accuracy of system availability evaluation in data element scenarios by introducing systematic evaluation, combining qualitative and quantitative methods, and consistency verification. We construct multi-classification problems in data element scenarios into a multi-level analysis structure model, decompose complex system evaluation problems into multiple components, and perform layered analysis according to the mutual influence and affiliation of factors, which makes the evaluation process more systematic and organized. At the same time, in the evaluation process, various evaluation indicators introduced in the data element scenario can correct logical errors that occur when constructing evaluation evidence through consistency verification in the evaluation, thereby improving the accuracy and credibility of the evaluation results.

[0017] The device, electronic device and readable storage medium corresponding to the data element system availability evaluation method of the present invention can achieve the same technical effect. To avoid repetition, they will not be described here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for evaluating the availability of a data element system provided in an embodiment of the present application; Figure 2 A schematic flow chart of another data element system availability evaluation method provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a data element system availability assessment device provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of the present application are clearly described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present application.

[0020] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0021] The description of the method flow in the specification of the present application and the steps of the flowchart in the drawings of the present specification do not have to be strictly executed according to the step numbers, and the method steps can be executed in a different order. Moreover, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0022] The following is a detailed description of the data element system availability evaluation method, device, equipment and medium provided in the embodiments of the present application in combination with the accompanying drawings and preferred embodiments.

[0023] First, the application scenario of the data element system availability evaluation method in the embodiment of the present application is described in detail.

[0024] Current data element system availability assessment methods often focus on technical availability, such as system functions and system performance, while ignoring the unique attributes of data elements, such as data security, compliance, circulation efficiency, and measurement accuracy. This single-dimensional assessment method is difficult to fully reflect the comprehensive effectiveness of the data element system. At the same time, traditional assessment methods rely on expert experience or simple questionnaires when determining the weights of each assessment indicator, which leads to a strong subjectivity in weight allocation and a lack of scientificity and objectivity. Different experts or stakeholders may attach different importance to the same indicator, which affects the fairness and accuracy of the assessment results.

[0025] Therefore, in the current data element system availability assessment, a significant shortcoming is the lack of a comprehensive and systematic availability assessment method. This leads to the management entities in various links of the data element market often relying on scattered performance indicators or subjective experience to evaluate the performance of the system, making it difficult to form accurate and scientific evaluation conclusions. This deficiency not only limits the efficient operation of the data element system, but may also have a negative impact on user satisfaction, system security and compliance, and thus affect the sustainable development and market competitiveness of the entire system.

[0026] In response to the above-mentioned technical problems, this application provides a data element system availability assessment method. In addition to the technical level, it further forms a comprehensive, systematic and accurate assessment plan from the dimension of the unique attributes of data elements. It is of great significance to improve the comprehensive performance of the data element system, ensure its safe and compliant operation, and promote the healthy development of the data economy.

[0027] See also Figure 1-2 , the present application embodiment provides a method for evaluating the availability of a data element system, such as Figure 1-2 As shown, the evaluation method of the embodiment of the present application includes: Step S1, based on the data element system evaluation indicators and candidate products, a three-layer structural model consisting of a target layer, an indicator layer and a solution layer is constructed based on the AHP hierarchical analysis method; wherein, the main purpose scenario of the evaluation is clearly defined in the target layer, for example: selecting a data element system with high availability under the information innovation scenario (information technology application innovation); the indicator layer includes system security, system performance, security certification capabilities and functional coverage as key factors to be considered in the evaluation process; the solution layer includes the entities of the listed candidate products, such as product A of company A, product B of company B, and product C of company C.

[0028] The analytic hierarchy process, referred to as AHP, breaks down the problem into multiple levels, compares and assigns weights to each level, and derives the overall weight ranking of the goals to help decision makers make reasonable decisions.

[0029] Step S2, for each indicator of the indicator layer, a judgment matrix for pairwise comparison is constructed; in the judgment matrix of the indicator layer, a quantitative rule of numbers 1 to 9 is used to represent relative importance.

[0030] Furthermore, a quantitative rule of numbers 1 to 9 is used to represent relative importance, see Table 1 below, specifically: according to the degree of relative importance, the corresponding quantitative value is 1 for equal importance, 3 for slightly important, 5 for relatively strong importance, 7 for extremely important, 9 for extremely important, and the intermediate values ​​of two adjacent judgments are 2, 4, 6, and 8 respectively. The judgment matrix at the indicator level will affect the importance of the indicators in choosing a product. Fill the factors for product comparison into the judgment matrix by scoring them two by two. For example, if system security is more important than system performance and is extremely important, then the value should be assigned to the system security / system performance table matrix as 9, so this is actually a positive reciprocal matrix.

[0031] The following is an example of a judgment matrix for the indicator layer of a data element system under the background of information innovation, see Table 2 below: System security System Performance Security authentication capabilities Functional coverage System security 1 1 / 4 2 1 / 3 System Performance 4 1 8 2 Security authentication capabilities 1 / 2 1 / 8 1 1 / 5 Functional coverage 3 1 / 2 5 1 and 8.5 1.88 16.00 3.53 The performance of the system is very important in the data element information creation scenario. The comparison value of system performance and system security is 4, which means that the importance of system performance is 4 times that of system security. In the process of storing important data and core data, users have extremely high requirements for system processing speed, transmission efficiency and storage capacity. At the same time, the comparison value of system performance and security authentication capability is 8, which highlights the core position of system performance in data element processing. In addition, the comparison value of system performance and functional coverage is 2, which also illustrates the important role of system performance in ensuring the comprehensiveness and flexibility of business.

[0032] At the same time, system security is equally important in the current context of comprehensive information innovation. Although in the judgment matrix, the importance of system performance is slightly higher than that of system security, the gap between the two is not significant (the comparison value of system security and system performance is 1 / 4). System security is directly related to the privacy protection and security of data, and is an indispensable part of the data element scenario. As can be seen from the table, the comparison value of system security and security authentication capability is 2, and the importance of system security in ensuring data security is higher than that of security authentication capability. At the same time, the comparison value of system security and functional coverage is an inverse relationship of 1 / 3, so we say that while pursuing comprehensive functions, we cannot ignore the investment and guarantee of system security.

[0033] In the data element industry, the main purpose of security certification capability is to ensure that software products follow relevant security standards and specifications during the design and development process, thereby reducing the risk of security problems in the actual operation of the software. Through software security certification, the security performance of the software can be effectively improved, user data and privacy can be protected, and malicious attacks and data leaks and other security incidents can be prevented. Although its importance is slightly inferior to security and performance (the comparison value of security certification capability and system performance is 1 / 8), it is one of the important means to ensure data security. As can be seen from the table, the comparison value of security certification capability and functional coverage is 1 / 5, which reflects the importance of users to the security certification mechanism in the data element scenario.

[0034] "Data" is a "new generation of data intelligence platform" that covers the full range of data element and asset capabilities such as "data collection, governance, storage, calculation, use, flow, operation, and financing", so functional coverage is also one of the important indicators that need to be considered in the data element scenario. Although the importance of functional coverage is relatively low in the judgment matrix (the comparison value of functional coverage and system performance is 1 / 2), a fully functional and easily expandable system can provide users with a better user experience and adapt to changing market needs. Therefore, we give a comparison value of 5 for functional coverage and security authentication capability, which means that while ensuring data security, we also need to pay attention to the functional perfection and scalability of the system.

[0035] Step S3, performing consistency check on the judgment matrix of the indicator layer.

[0036] The importance of data elements was scored based on multiple aspects, and a judgment matrix for weight analysis was constructed. However, since it involves subjective judgment, consistency verification is required to ensure logical rationality.

[0037] If the matrix A = The following conditions are met, = 1 / and >0, then the matrix A is called a positive and negative matrix. At the same time, if the positive and negative matrices of A satisfy * A is called a consistency matrix. Assume that the consistency matrix A has an eigenvalue of n, which is generally the number of evaluation indicators, rows or columns of the n-order consistency matrix.

[0038] Now assume that the maximum eigenvalue of the n-order positive reciprocal matrix is , add the sum of each column of the above matrix, divide each value of each column by the sum and fill in the matrix (normalize by column), and finally perform arithmetic mean calculation on each row of the judgment matrix, see Table 3 below: = , so = . System security System Performance Security authentication capabilities Functional coverage w A System security 0.12 0.13 0.13 0.09 0.1176 0.47 System Performance 0.47 0.53 0.5 0.57 0.5175 2.08 Product safety certification capabilities 0.06 0.07 0.06 0.06 0.0611 0.25 Functional coverage 0.35 0.27 0.31 0.28 0.3038 1.22 Define an n-order positive reciprocal matrix if and only if the largest eigenvalue = n is the consistency matrix, if >n, then the n-order positive reciprocal matrix is ​​called an inconsistency matrix. Since most of the artificially constructed judgment matrices are positive and negative matrices, contradictions are inevitable, that is, it is difficult to construct a consistency matrix. However, by calculating the consistency ratio CR and moving closer to the consistency matrix, this effect can be eliminated. Based on this, the consistency check process of the embodiment of the present application specifically includes: Step S301, calculate the consistency index CI: in, , ， A is the consistency matrix; is an eigenvalue of the consistency matrix A; yes The maximum eigenvalue of a positive reciprocal matrix of order; , , , are the weights of each indicator or entity respectively; Step S302, search The corresponding average random consistency index RI is used to calculate the consistency ratio CR: If CR<0.1, the constructed judgment matrix is ​​considered consistent, otherwise it needs to be modified.

[0039] Among them, the average random consistency index RI is shown in Table 4 below: Step S4: According to the eigenvector of the judgment matrix of the indicator layer, sum each column of the judgment matrix, divide each value of each column by the sum to get a value, and then calculate the arithmetic mean based on each row to get the weight value, which is the weight of each indicator of the indicator layer. See the w value in Table 3 above.

[0040] Step S5: for each entity at the solution layer, a judgment matrix for pairwise comparison is constructed; in the judgment matrix at the solution layer, the above-mentioned quantification rule is used to represent relative importance.

[0041] In the factor evaluation project, the solutions of multiple manufacturers are evaluated and compared from the four primary indicator levels of system security, system performance, security certification capability and functional coverage. This step S5 further constructs a judgment matrix including the indicator layer and the solution layer to provide a scientific basis for the subsequent optimization analysis.

[0042] For example: System security: System security is the core indicator for ensuring data reliability and integrity, and directly affects the stability and risk resistance of the project. At the solution level, we obtained the following results: Product A of Company A: Emphasis on basic protection capabilities; Product B of Company B: It performs stably in large projects and has high attack and defense capabilities; C Company C Product: Focuses on the security needs of small and medium-sized scenarios.

[0043] Construct the following judgment matrix: The safety comparison between product A and product B is 1:4, indicating that the safety of product B is significantly better than that of product A; The security comparison between Product B and Product C is 2:1, indicating that Product B has stronger comprehensive capabilities in anti-attack and data encryption.

[0044] System performance: The system performance evaluation focuses on the performance in high-concurrency and high-traffic processing scenarios. The performance of each product is as follows: Product A from Company A: has good load balancing capabilities, but performs poorly when processing complex data streams; Product B of Company B: Suitable for medium loads, stable performance but weak scalability; Product C of Company C: The data processing efficiency is relatively high, but the performance may decline in continuous high-load scenarios.

[0045] Construct the following judgment matrix: The performance comparison between product A and product B is 5:1, which reflects the advantage of product A in load handling; The performance of product C is between the two, with a 2:1 comparison with product A.

[0046] Security authentication capabilities: Security authentication capability measures the system's ability to authenticate users and devices and manage access rights. The authentication capabilities of each product are as follows: Product A of Company A: provides basic authentication capabilities and is suitable for small-scale scenarios; Product B of Company B: Complete authentication function and support for multi-level permission management; Company C, Product C: The certification mechanism is relatively flexible, but the coverage is limited.

[0047] Construct the following judgment matrix: The certification capability comparison between product B and product A is 3:1, which reflects the technical advantage of product B; Product C is better than Product A in functionality, but lower than Product B, with a comparison ratio of 2:1 with Product A.

[0048] Functional coverage: Functional coverage is an important indicator for evaluating whether each product meets the needs of multiple scenarios. Products with wider coverage usually have higher applicability and market competitiveness: Product A of Company A: comprehensive functional modules, supporting a wide range of business scenarios; Product B of Company B: has fewer functional modules, but performs well in certain specific scenarios; Product C of Company C: Its functionality is slightly weaker and it mainly covers basic scenarios.

[0049] Construct the following judgment matrix: The functional coverage of product A relative to product B is 5:1, indicating that its functions are more comprehensive; The ratio of Product A to Product C is 7:1, reflecting its significant advantages in coverage breadth and applicability.

[0050] Step S6, perform consistency check on the judgment matrix of the solution layer and calculate the weight of each entity of the solution layer. This step is similar to steps S3-S4, and the judgment matrix of the solution layer is repeatedly checked for consistency and weight calculation. This step is the same as the above steps and will not be repeated.

[0051] Step S7, calculate the score of each entity in the solution layer according to the weight of each indicator in the indicator layer and the weight of each entity in the solution layer; analyze the positioning and role of the entity in the market according to the score, and the highest value is the best solution.

[0052] For example, the following table shows the weights of various indicators and entities obtained in the above steps. Perform a weighted calculation on each product entity, and the highest value obtained is the best solution.

[0053] Company A, Product A: 0.1176*0.1818+0.5175*0.5949+0.0611*0.2299+0.3038*0.738=0.56749172; For example, Company B’s product: 0.1176*0.7273+0.5175*0.1285+0.0611*0.6479+0.3038*0.1676=0.2425328; For example, Company C’s product C; 0.1176*0.0909+0.5175*0.2766+0.0611*0.1222+0.3038*0.0944=0.18997548.

[0054] In modern data governance scenarios, the performance of different solutions directly affects the overall performance of the system. The following is an analysis of the importance of each indicator in this case and its correlation with the actual scenario: System security: Data confidentiality and integrity are core requirements for scenarios such as smart cities and financial transactions. For example, in a smart government platform, data leakage will trigger a crisis of public trust, so product security assessment is particularly important.

[0055] System performance: Processing efficiency and response speed are crucial when it comes to large-scale data processing (such as real-time traffic analysis), directly affecting user experience and decision-making efficiency.

[0056] Security authentication capabilities: The strength of the authentication mechanism is related to whether the system can achieve refined permission management and identity authentication, such as protecting patient privacy in medical information systems.

[0057] Functional coverage: measures whether the solution can fully meet diverse needs. Especially in complex systems (such as smart city big data platforms), the comprehensiveness of functional modules determines the applicability and scalability of the system.

[0058] The total score of Company A's product A is 0.5675, significantly higher than other solutions. Its superior performance is mainly reflected in system performance and functional coverage, which makes it highly applicable in the following scenarios: Smart city data center: In city-level data management, it is necessary to integrate data sources from multiple fields such as transportation, environmental protection, and energy. With its high system performance, product A can efficiently process large-scale concurrent data streams, and its comprehensive functional coverage supports multi-module collaboration (such as data visualization, analysis, and prediction), making it the core choice for urban data governance.

[0059] Enterprise-level data integration platform: For the cross-departmental data integration needs of large enterprises, Product A can provide excellent response speed and wide-ranging functional support, such as real-time data synchronization and multi-dimensional report generation, greatly improving data operation efficiency.

[0060] Smart logistics platform: In scenarios where massive orders and logistics information need to be processed quickly (such as during the Double Eleven Shopping Festival), the high performance of Product A can effectively support system operation and avoid economic losses caused by data delays or system crashes.

[0061] Positioning and applicable scenarios of product B of company B: Product B of Company B ranked second with a total score of 0.2425. It performed outstandingly in terms of system security and security certification capabilities and is suitable for the following scenarios: Financial industry data platform: Banks and securities trading systems have extremely high requirements for data security. The excellent security performance and rigorous authentication mechanism of Product B can provide solid technical support for financial transactions and reduce the risks of fraud and illegal access.

[0062] Government information systems: In national tax systems or population data management platforms, data confidentiality and precise access rights are key requirements. Product B, with its outstanding capabilities in security authentication, can provide highly secure solutions for government systems.

[0063] Although it does not have an advantage in system performance and functional coverage, it is highly competitive in scenarios with high security requirements.

[0064] Positioning and applicable scenarios of C Company's C product: The total score of product C from company C is 0.1900. It is relatively insufficient in terms of performance and functional coverage and is suitable for small-scale scenarios or projects with limited budgets: Small and medium-sized enterprise data systems: For enterprises with limited operating budgets, C products can be a cost-effective choice to meet basic data processing needs.

[0065] Regional smart community platform: In a smart community of limited scale, the lightweight functions of C product are sufficient to support basic community data management, such as energy monitoring, garbage classification data collection, etc.

[0066] Based on the calculation results, Company A's Product A has the highest score of 0.56749172, so Company A's Product A is selected.

[0067] In the wave of digital transformation driven by data elements, the positioning and role of each product in the market segment are crucial. In the future, as the needs of scenarios become more diverse and complex, the evaluation model based on the AHP method will be continuously optimized to support decision-making more accurately, which can not only provide a clear basis for actual decision-making, but also point out the direction for product optimization and market layout.

[0068] Based on the above technical solution, this application has the following beneficial effects and advantages: 1. A comprehensive evaluation indicator system has been established: Based on the characteristics of data elements, a multi-dimensional evaluation indicator system including data security, compliance, circulation efficiency, market adaptability, etc. has been established to ensure the comprehensiveness and accuracy of the evaluation results.

[0069] 2. Through the AHP hierarchical analysis method, complex evaluation problems are decomposed into multiple levels, and a combination of quantitative and qualitative methods are used to scientifically allocate weights to indicators at each level, reduce the impact of subjective factors, and improve the fairness and objectivity of the evaluation.

[0070] 3. Simplified the evaluation process: Optimize the evaluation process, reduce unnecessary steps and links, and improve the efficiency and convenience of the evaluation. At the same time, use information technology to realize the automation and intelligence of the evaluation process and reduce the evaluation cost.

[0071] 4. Strengthened data security and privacy protection: During the evaluation process, we strictly abide by relevant laws, regulations and privacy protection policies, and take necessary technical and management measures to ensure that the security and privacy of sensitive data are effectively protected.

[0072] See also Figure 3 , corresponding to the above-mentioned data element system availability evaluation method embodiment, the present application embodiment provides a data element system availability evaluation device, the evaluation device comprising: The system construction module 1001 is used to construct a three-layer structure model consisting of a target layer, an indicator layer and a solution layer based on the AHP hierarchical analysis method according to the data element system evaluation indicators and candidate products; wherein the main purpose scenario of the evaluation is clearly defined in the target layer; the indicator layer includes system security, system performance, security certification capability and functional coverage as key factors to be considered in the evaluation process; the solution layer includes the listed entities of the candidate products; The indicator layer calculation module 1002 is used to construct a pairwise comparison judgment matrix for each indicator of the indicator layer; in the judgment matrix of the indicator layer, a quantitative rule of numbers 1 to 9 is used to represent relative importance; Performing consistency check on the judgment matrix of the indicator layer; According to the eigenvector of the judgment matrix of the indicator layer, each column of the judgment matrix is ​​summed, each value of each column is divided by the sum to obtain a value, and then the arithmetic mean is calculated based on each row to obtain a weight value, which is the weight of each indicator of the indicator layer; The solution layer calculation module 1003 is used to construct a pairwise comparison judgment matrix for each entity of the solution layer; in the judgment matrix of the solution layer, the quantitative rule is used to represent the relative importance; Performing consistency check on the judgment matrix of the solution layer and calculating the weight of each entity of the solution layer; The entity score calculation module 1004 is used to calculate the score of each entity in the solution layer according to the weight of each indicator in the indicator layer and the weight of each entity in the solution layer.

[0073] In one possible implementation, the quantization rule is specifically as follows: an incremental division is performed according to the degree of relative importance, with equal importance corresponding to a quantization value of 1, slightly important corresponding to a quantization value of 3, relatively strong importance corresponding to a quantization value of 5, extremely important corresponding to a quantization value of 7, and extremely important corresponding to a quantization value of 9. The intermediate values ​​of two adjacent judgments are 2, 4, 6, and 8, respectively.

[0074] In a possible implementation, the consistency check specifically includes: Calculate the consistency index CI: in, , ， A is the consistency matrix; is an eigenvalue of the consistency matrix A; yes The maximum eigenvalue of a positive reciprocal matrix of order; , , , are the weights of each indicator or entity respectively; Find The corresponding average random consistency index RI is used to calculate the consistency ratio CR: If CR<0.1, the constructed judgment matrix is ​​considered consistent, otherwise it needs to be modified.

[0075] In a possible implementation, the evaluation device further includes: The entity analysis module 1005 is used to analyze the positioning and role of the entity in the market according to the score.

[0076] The above-mentioned data element system availability assessment device implements the steps and various processes of the above-mentioned data element system availability assessment method embodiment and can achieve the same technical effect. In order to avoid repetition, it will not be repeated here.

[0077] See also Figure 4 Corresponding to the above-mentioned data element system availability evaluation method embodiment, the embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps and various processes of the above-mentioned data element system availability evaluation method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0078] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0079] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0080] Corresponding to the above-mentioned data element system availability assessment method embodiment, the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the above-mentioned data element system availability assessment method embodiment and the various processes of the embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0081] The processor is the processor in the electronic device described in the above embodiment of the present application. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0082] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article 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, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0084] It can be understood that the embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific embodiments, which are merely illustrative and not restrictive, and those skilled in the art are aware that various changes or equivalent replacements can be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, those of ordinary skill in the art can modify these features and embodiments to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention under the inspiration or teaching of the present application. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of the present application belong to the scope protected by the present invention.

Claims

1. A method for evaluating the availability of a data element system, characterized in that: include: According to the data element system evaluation indicators and candidate products, a three-layer structure model consisting of a target layer, an indicator layer and a solution layer is constructed based on the AHP hierarchical analysis method; wherein the main purpose scenario of the evaluation is clearly defined in the target layer; the indicator layer includes system security, system performance, security certification capability and functional coverage as key factors to be considered in the evaluation process; the solution layer includes the listed entities of the candidate products; For each indicator of the indicator layer, a judgment matrix for pairwise comparison is constructed; in the judgment matrix of the indicator layer, a quantitative rule of numbers 1 to 9 is used to represent relative importance; Performing consistency check on the judgment matrix of the indicator layer; According to the eigenvector of the judgment matrix of the indicator layer, each column of the judgment matrix is ​​summed, each value of each column is divided by the sum to obtain a value, and then the arithmetic mean is calculated based on each row to obtain a weight value, which is the weight of each indicator of the indicator layer; For each entity of the solution layer, a pairwise comparison judgment matrix is ​​constructed; in the judgment matrix of the solution layer, the quantification rule is used to represent the relative importance; Performing consistency check on the judgment matrix of the solution layer and calculating the weight of each entity of the solution layer; The score of each entity in the solution layer is calculated according to the weight of each indicator in the indicator layer and the weight of each entity in the solution layer.

2. The method for evaluating the availability of a data element system according to claim 1, characterized in that: The quantization rule is specifically as follows: the quantization is divided in ascending order according to the degree of relative importance, with equal importance corresponding to a quantization value of 1, slightly important corresponding to a quantization value of 3, relatively strong importance corresponding to a quantization value of 5, extremely important corresponding to a quantization value of 7, and extremely important corresponding to a quantization value of 9. The intermediate values ​​of two adjacent judgments are 2, 4, 6, and 8 respectively.

3. The method for evaluating the availability of a data element system according to claim 1, characterized in that: The consistency check specifically includes: Calculate the consistency index CI: in, <h2 style=";text-align:left;direction:ltr">[AW]<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =A<h2 style=";text-align:left;direction:ltr"> i1 <h2 style=";text-align:left;direction:ltr"> *w1+A<h2 style=";text-align:left;direction:ltr"> i2 <h2 style=";text-align:left;direction:ltr"> *w2+A<h2 style=";text-align:left;direction:ltr"> i3 <h2 style=";text-align:left;direction:ltr"> *w3+A<h2 style=";text-align:left;direction:ltr"> i4 <h2 style=";text-align:left;direction:ltr"> *w4, A is the consistency matrix; n is an eigenvalue of the consistency matrix A; λ max is the maximum eigenvalue of the n-order positive reciprocal matrix; w1, w2, w3, w4 are the weights of each indicator or entity respectively; Find the average random consistency index RI corresponding to n, and use the following formula to calculate the consistency ratio CR: If CR<0.1, the constructed judgment matrix is ​​considered consistent, otherwise it needs to be modified.

4. The method for evaluating the availability of a data element system according to claim 1, characterized in that: The evaluation method also includes: The positioning and role of the entity in the market are analyzed according to the score.

5. A data element system availability evaluation device, characterized in that: include: The system construction module is used to construct a three-layer structure model consisting of a target layer, an indicator layer and a solution layer based on the AHP hierarchical analysis method according to the data element system evaluation indicators and candidate products; wherein the main purpose scenario of the evaluation is clearly defined in the target layer; the indicator layer includes system security, system performance, security certification capability and functional coverage as key factors to be considered in the evaluation process; the solution layer includes the listed entities of the candidate products; The indicator layer calculation module is used to construct a pairwise comparison judgment matrix for each indicator of the indicator layer; in the judgment matrix of the indicator layer, a quantitative rule of numbers 1 to 9 is used to represent relative importance; Performing consistency check on the judgment matrix of the indicator layer; According to the eigenvector of the judgment matrix of the indicator layer, each column of the judgment matrix is ​​summed, each value of each column is divided by the sum to obtain a value, and then the arithmetic mean is calculated based on each row to obtain a weight value, which is the weight of each indicator of the indicator layer; A scheme layer calculation module, used for constructing a pairwise comparison judgment matrix for each entity of the scheme layer; in the judgment matrix of the scheme layer, the quantization rule is used to represent the relative importance; Performing consistency check on the judgment matrix of the solution layer and calculating the weight of each entity of the solution layer; The entity score calculation module is used to calculate the score of each entity in the solution layer according to the weight of each indicator in the indicator layer and the weight of each entity in the solution layer.

6. The data element system availability evaluation device according to claim 5, characterized in that: The quantization rule is specifically as follows: the quantization is divided in ascending order according to the degree of relative importance, with equal importance corresponding to a quantization value of 1, slightly important corresponding to a quantization value of 3, relatively strong importance corresponding to a quantization value of 5, extremely important corresponding to a quantization value of 7, and extremely important corresponding to a quantization value of 9. The intermediate values ​​of two adjacent judgments are 2, 4, 6, and 8 respectively.

7. The data element system availability evaluation device according to claim 5, characterized in that: The consistency check specifically includes: Calculate the consistency index CI: in, <h2 style=";text-align:left;direction:ltr">[AW]<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =A<h2 style=";text-align:left;direction:ltr"> i1 <h2 style=";text-align:left;direction:ltr"> *w1+A<h2 style=";text-align:left;direction:ltr"> i2 <h2 style=";text-align:left;direction:ltr"> *w2+A<h2 style=";text-align:left;direction:ltr"> i3 <h2 style=";text-align:left;direction:ltr"> *w3+A<h2 style=";text-align:left;direction:ltr"> i4 <h2 style=";text-align:left;direction:ltr"> *w4, A is the consistency matrix; n is an eigenvalue of the consistency matrix A; λ max is the maximum eigenvalue of the n-order positive reciprocal matrix; w1, w2, w3, w4 are the weights of each indicator or entity respectively; Find the average random consistency index RI corresponding to n, and use the following formula to calculate the consistency ratio CR: If CR<0.1, the constructed judgment matrix is ​​considered consistent, otherwise it needs to be modified.

8. The data element system availability evaluation device according to claim 5, characterized in that: The evaluation device further comprises: The entity analysis module is used to analyze the positioning and role of the entity in the market according to the score.

9. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the data element system availability assessment method as described in any one of claims 1 to 4.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the data element system availability assessment method as described in any one of claims 1 to 4 are implemented.

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