Enterprise data capitalization level evaluation method, device, equipment and medium

Abstract

The invention provides an enterprise data capitalization level assessment method and device, equipment and a medium, effectively provides an effective enterprise data capitalization level assessment scheme, and solves the problems that an existing assessment scheme is insufficient in scientificity and low in accuracy and cannot be applied to actual enterprises. The method comprises the following steps: determining a plurality of influence factors influencing the enterprise data capitalization level, establishing an asset analysis model based on each influence factor, and obtaining a plurality of indexes corresponding to the plurality of influence factors; respectively obtaining a first-level index weight and a second-level index weight based on the plurality of evaluation dimensions, and fusing the first-level index weight and the second-level index weight to obtain a comprehensive index weight; establishing a multistage evaluation parameter set and a fuzzy evaluation matrix; and calculating an enterprise data capitalization level score based on the fuzzy evaluation matrix corresponding to each influence factor and the weight vector of the comprehensive index weight so as to determine the enterprise data capitalization level of the enterprise.

Description

Technical Field

[0001] The present application relates to the field of enterprise evaluation technology, and more specifically, to a method, device, equipment, and medium for evaluating the level of enterprise data assetization. Background Art

[0002] Data asset management is at the core of enterprise digital transformation, helping companies improve data quality, reduce data costs, increase data value, and create new business models. Specifically, it serves as a core driver of digital transformation, a means of enhancing data value, an effective means of improving decision-making efficiency, a key factor in enhancing enterprise competitiveness, and a vital guarantee for sustainable development. Data asset management primarily encompasses data governance, data quality, data security, data compliance, and data applications. For enterprises, clarifying the level of data assetization is crucial; it impacts their future development direction and digital transformation strategy.

[0003] Although existing enterprises also have some data assetization level assessment plans, they lack a unified industry assessment framework and quantitative indicator system. The technical standards and assessment dimensions adopted by different enterprises vary greatly, resulting in difficulties in cross-departmental / supply chain collaboration and difficulty in establishing a unified digital transformation benchmark. The integrated assessment of upstream and downstream industry data (such as user behavior data and supply chain inventory data) in the enterprise is hindered; existing assessment plans are mostly static periodic inspections (such as annual audits), which cannot monitor data quality fluctuations, security risk changes or compliance status in real time, and tend to focus on a single dimension (such as separately assessing data security levels or data quality); existing assessment plans rely too much on subjective weights, and the indicator weight settings in the adopted assessment models mostly rely on expert experience, lacking a dynamic parameter adjustment mechanism based on actual business impacts. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, device, equipment and medium for evaluating the level of enterprise data assetization. This method, device, equipment and medium for evaluating the level of enterprise data assetization effectively provide an effective enterprise data assetization level evaluation scheme, solving the problems of existing evaluation schemes such as lack of scientificity, low accuracy and inability to be applied in actual enterprises.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the level of enterprise data assetization, the method comprising:

[0006] Determine multiple factors that affect the enterprise data assetization level, establish an asset analysis model corresponding to the enterprise data assetization level based on the multiple factors, and obtain multiple indicators corresponding to each factor in the asset analysis model;

[0007] Based on multiple evaluation dimensions, the evaluation weights of the multiple indicators corresponding to the evaluation dimensions and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions are calculated respectively, and the first-level indicator weights and second-level indicator weights are integrated to obtain the comprehensive indicator weights of the multiple indicators;

[0008] Establishing a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators, and constructing a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set;

[0009] The enterprise data assetization level score of the enterprise is calculated based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score.

[0010] In combination with the first aspect, an embodiment of the present application provides a first possible implementation of the first aspect, wherein the step of calculating, based on multiple evaluation dimensions, the evaluation weights of the multiple indicators corresponding to the evaluation dimensions and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions respectively includes:

[0011] Based on the determined evaluation dimension, a corresponding evaluation calculation method is called to calculate the evaluation scores pre-input for the multiple indicators based on the evaluation calculation method to obtain a corresponding judgment matrix;

[0012] The judgment matrices of the corresponding evaluation dimensions are processed separately through the evaluation calculation methods of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions.

[0013] In combination with the first aspect, the embodiment of the present application provides a second possible implementation of the first aspect, wherein the evaluation dimension includes at least a hierarchical analysis dimension;

[0014] The judgment matrices of the corresponding evaluation dimensions are processed respectively by the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions, including:

[0015] Normalizing the judgment matrix according to a preset normalization processing network to obtain a normalized judgment matrix;

[0016] The maximum eigenvalue of the normalized judgment matrix is calculated, and the first-level indicator weight is obtained based on the maximum eigenvalue.

[0017] In combination with the first aspect, the embodiment of the present application provides a third possible implementation of the first aspect, wherein the evaluation dimension includes at least an entropy weight calculation dimension;

[0018] The step of processing the judgment matrices of the corresponding evaluation dimensions respectively by the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions based on the judgment matrices includes:

[0019] Standardizing the judgment matrix, calculating the information entropy of the standardized judgment matrix, and extracting the quantitative characteristics of the multiple indicators;

[0020] The quantitative characteristics of the multiple indicators and the information entropy are calculated based on a preset computing network to obtain the second-level indicator weights.

[0021] In combination with the first aspect, an embodiment of the present application provides a fourth possible implementation of the first aspect, wherein the step of calculating the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight includes:

[0022] Calculating a total fuzzy evaluation matrix of the multiple influencing factors based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector corresponding to the comprehensive indicator weights of the multiple influencing factors;

[0023] The weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight is calculated to obtain the enterprise data assetization level score.

[0024] In combination with the first aspect, an embodiment of the present application provides a fifth possible implementation of the first aspect, wherein, after calculating the weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight to obtain the enterprise data assetization level score, the method includes:

[0025] Determining whether the enterprise data assetization level score meets the preset weighted processing conditions;

[0026] If it is determined to be satisfied, corresponding processing measures will be taken according to the enterprise data assetization level score to obtain the final enterprise data assetization level.

[0027] In combination with the first aspect, the embodiment of the present application provides a sixth possible implementation of the first aspect, wherein establishing a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators includes:

[0028] Based on the subordinate relationship between each influencing factor and the corresponding indicator, calling multiple indicators corresponding to the influencing factors respectively;

[0029] A multi-level evaluation parameter set with a subordinate relationship is constructed based on each of the influencing factors and the multiple indicators.

[0030] In a second aspect, an embodiment of the present application provides a device for evaluating the level of enterprise data assetization, the device comprising:

[0031] Establishing a module for determining multiple factors affecting the level of enterprise data assetization, establishing an asset analysis model corresponding to the level of enterprise data assetization based on the multiple factors, and obtaining multiple indicators corresponding to each of the factors in the asset analysis model;

[0032] A fusion module is used to calculate the evaluation weights of the evaluation dimensions corresponding to the multiple indicators and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions based on the multiple evaluation dimensions, and to fuse the first-level indicator weights and second-level indicator weights to obtain the comprehensive indicator weights of the multiple indicators;

[0033] A construction module, configured to establish a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators, so as to construct a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set;

[0034] A calculation module is used to calculate the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score.

[0035] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of any one of the methods for assessing the level of enterprise data assets are performed.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, executes the steps of any one of the methods for assessing the level of enterprise data assetization.

[0037] The embodiment of the present application provides a method for evaluating the level of enterprise data assetization. The method first determines the multiple influencing factors that affect the level of enterprise data assetization, establishes an asset analysis model corresponding to the enterprise data assetization level based on the multiple influencing factors, and obtains multiple indicators corresponding to each influencing factor of the asset analysis model; secondly, based on multiple evaluation dimensions, calculates the evaluation weights of the multiple indicators corresponding to the evaluation dimensions and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions, and integrates the first-level indicator weights and second-level indicator weights to obtain the comprehensive indicator weights of the multiple indicators; then, establishes a multi-level evaluation parameter based on the multiple influencing factors and the multiple indicators. set, so as to construct a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set; finally, based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, the enterprise data assetization level score of the enterprise is calculated, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score, thereby providing an effective, accurate and scientific enterprise data assetization level evaluation method, thereby improving the validity and objectivity of the evaluated enterprise data assetization level, solving the existence of subjectivity, and solving the problems of existing evaluation schemes such as insufficient scientificity, low accuracy and inability to be applied to actual enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A flow chart of a method for evaluating the level of enterprise data assetization provided by an embodiment of the present application is shown;

[0040] Figure 2 A schematic diagram of expert scoring distribution provided in an embodiment of the present application is shown;

[0041] Figure 3 A schematic diagram of the first-level fuzzy comprehensive evaluation results provided by an embodiment of the present application is shown;

[0042] Figure 4 A schematic diagram of the evaluation levels provided in the embodiment of the present application is shown;

[0043] Figure 5 The following is a structural block diagram of a device for evaluating the level of enterprise data assetization provided by an embodiment of the present application;

[0044] Figure 6 A structural block diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0046] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0047] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0048] The data assetization level assessment schemes currently implemented in existing enterprises lack a unified industry assessment framework and quantitative indicator system. The technical standards and assessment dimensions adopted by different enterprises vary greatly, resulting in difficulties in cross-departmental / supply chain collaboration and the establishment of a unified digital transformation benchmark. The integrated assessment of upstream and downstream industry data (such as user behavior data and supply chain inventory data) in the enterprise is hindered. Existing assessment schemes are mostly static periodic inspections (such as annual audits), which cannot monitor data quality fluctuations, security risk changes or compliance status in real time, and tend to focus on a single dimension (such as separately assessing data security levels or data quality). Existing assessment schemes rely too much on subjective weights, and the indicator weight settings in the adopted assessment models mostly rely on expert experience, lacking a dynamic parameter adjustment mechanism based on actual business impacts.

[0049] Based on this, the embodiments of the present application provide a method, device, electronic device and medium for evaluating the level of enterprise data assetization, which are described below through embodiments.

[0050] Example 1

[0051] To facilitate understanding of this embodiment, a method for evaluating the level of enterprise data assetization disclosed in this embodiment is first described in detail. Figure 1 The flowchart of a method for evaluating the level of enterprise data assetization is shown. The present application provides a method for evaluating the level of enterprise data assetization, the method comprising:

[0052] S101: Determine multiple factors influencing the level of enterprise data assetization, establish an asset analysis model corresponding to the level of enterprise data assetization based on the multiple factors, and obtain multiple indicators corresponding to each factor in the asset analysis model;

[0053] S102: Based on multiple evaluation dimensions, respectively calculating the evaluation weights of the multiple indicators corresponding to the evaluation dimensions and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions, and fusing the first-level indicator weights and second-level indicator weights to obtain a comprehensive indicator weight of the multiple indicators;

[0054] S103: establishing a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators, and constructing a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set;

[0055] S104: Calculate the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score.

[0056] In step S101, Xichen Wang et al. proposed that data quality issues include data accuracy, completeness, consistency, and timeliness, which affect the effective utilization and value realization of data. The value of data assets is influenced by their internal and external characteristics, namely data quality, scale, privacy, timeliness, and organizational, technological, and environmental factors. Data quality directly determines the efficiency of data use and the quality of the final results, and has a significant impact on the value of data assets. Organizational factors include decision-making and enterprise operating mechanisms, while environmental factors include policies and regulations, market competition, and other factors. For enterprises, clarifying the level of data assetization is of great importance, as it is related to their future development direction and digital transformation strategy. Therefore, based on existing research, this application categorizes the factors affecting the level of enterprise data assetization into three aspects: enterprise data quality, internal enterprise structure, and external environmental influences. The various influencing factors are enterprise data quality, internal enterprise structure, and external environmental influences. Enterprise data asset quality includes data integrity, data accuracy, data timeliness, data security, data credibility, and data availability. From the perspective of enterprise data itself, it influences the level of data element assetization and is the most intuitive influencing factor. The internal structure of an enterprise includes the enterprise organizational structure, enterprise decision-making mechanism, talent structure, technical facilities, business processes, and data management system. It is related to the future direction of the enterprise, among which human factors are relatively important. External environmental influences include relevant policies and regulations, industry competition impact, technological development level, industry market demand, etc. The impact of the macro environment on the level of enterprise data assetization has strong uncertainty and uncontrollable factors. Therefore, this application decomposes the level of enterprise data assetization into several constituent influencing factors, and clusters and combines the factors at different levels according to the mutual influence and affiliation between the influencing factors to form a multi-level asset analysis model. The asset analysis model includes multiple levels, namely the indicator layer, the criterion layer and the target layer. Different levels have corresponding indicators. The indicator of the target layer is the enterprise data assetization level, the criterion layer is the image factor of enterprise data quality, enterprise internal structure, and external environment influence, and the indicator layer has multiple indicators. The asset analysis model corresponds to the enterprise data assetization level evaluation system. The enterprise data assetization level evaluation system is shown in Table 1.

[0057] Table 1 Enterprise data assetization level evaluation system

[0058]

[0059] In step S102, a plurality of evaluation dimensions are set for the indicator layer and the criterion layer in the asset analysis model, one is the hierarchical analysis dimension, and the other is the entropy weight calculation dimension, that is, the evaluation weights of the evaluation dimensions corresponding to the plurality of indicators and the first-level indicator weights and the second-level indicator weights of the corresponding evaluation dimensions are calculated based on the hierarchical analysis dimension and the entropy weight calculation dimension, that is, the first-level indicator weight corresponds to the hierarchical analysis dimension, and the second-level indicator weight corresponds to the entropy weight calculation dimension, and the first-level indicator weight and the second-level indicator weight are fused to obtain the comprehensive indicator weight of the plurality of indicators, that is, each indicator in the indicator layer has a comprehensive indicator weight, and each comprehensive indicator weight is obtained by fusion of the first-level indicator weight calculated by the hierarchical analysis dimension and the second-level indicator weight calculated by the entropy weight calculation dimension, wherein the comprehensive indicator weight W=(W 1, W 2,...., W n-1 W n ) is calculated using a weighted approach, where:

[0060]

[0061] Among them, w i It is a comprehensive indicator weight that combines the hierarchical analysis dimension and the entropy weight calculation dimension.

[0062] In the specific implementation process of step S102, there is an embodiment in which: the calculation of the evaluation weights of the evaluation dimensions corresponding to the multiple indicators and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions based on the multiple evaluation dimensions includes:

[0063] S1021: Based on the determined evaluation dimension, a corresponding evaluation calculation method is called to calculate the evaluation scores pre-input for the multiple indicators based on the evaluation calculation method to obtain a corresponding judgment matrix;

[0064] S1022. Process the judgment matrices of the corresponding evaluation dimensions respectively through the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions.

[0065] In steps S1021-S1022, after determining that the evaluation dimensions are the hierarchical analysis dimension and the entropy weight calculation dimension, the present application calls the evaluation calculation methods pre-set for the hierarchical analysis dimension and the entropy weight calculation dimension, respectively, to calculate the evaluation scores pre-input for the multiple indicators based on the evaluation calculation method to obtain the corresponding judgment matrix; the evaluation calculation method can not only calculate the judgment matrix, but also process the judgment matrix of the corresponding evaluation dimension to obtain the indicator weights of the corresponding evaluation dimension, namely the first-level indicator weights and the second-level indicator weights, respectively, so as to analyze the indicators based on the obtained first-level indicator weights and second-level indicator weights, thereby performing a corresponding analysis on the data assetization level of the enterprise. The dual-dimensional analysis can better ensure the effectiveness and accuracy of the analysis of the enterprise's assetization level.

[0066] In the specific implementation process of step S1021, there is an embodiment in which: the evaluation dimension at least includes a hierarchical analysis dimension;

[0067] The judgment matrices of the corresponding evaluation dimensions are processed respectively by the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions, including:

[0068] S102111. Normalizing the judgment matrix according to a preset normalization processing network to obtain a normalized judgment matrix;

[0069] S102112. Calculate the maximum eigenvalue of the normalized judgment matrix, and obtain the first-level indicator weight based on the maximum eigenvalue.

[0070] In steps S102111-S102112, the normalization processing network is expressed by formula (2):

[0071]

[0072] where ω ij is the normalized value, n is the order of the judgment matrix A, and the maximum eigenvalue of the normalized judgment matrix is calculated based on formulas (3)-(4):

[0073]

[0074] ω=(ω1,ω2,ω3,…ω n ) Τ (4);

[0075] where λ max is the maximum eigenvalue of the judgment matrix A, ω is the eigenvector, and the maximum eigenvalue is processed based on formula (5) to obtain the first-level indicator weight:

[0076]

[0077] Where W i ′ is the first-level indicator weight calculated for the i-th indicator.

[0078] First, we invited 10 experts from related fields to carefully score the various influencing factors of the criterion layer and the indicators of the indicator layer to reflect the relative importance of each influencing factor and each indicator in the overall evaluation. Based on the scores of each expert, this application constructed a judgment matrix for the criterion layer and the indicator layer indicators respectively. The judgment matrix takes the elements of a certain layer as the criterion, compares the elements of the next layer with each other, and assigns values according to the 1-9 scaling method to construct a judgment matrix A = (a ij ) n×n , the judgment matrix A=(a ij ) n×n Element a in ij Indicates a i When the criterion is i The relative importance of aj is shown in Table 2. Then, the constructed judgment matrix was subjected to necessary consistency checks to ensure that the expert judgment matrix was logically self-consistent.

[0079] According to the scale, the multiple influencing factors of the quasi-measurement layer and the multiple indicators of the indicator layer are compared between the levels, and the judgment matrix A is constructed. ij ) n×n , where n represents the number of factors involved in the pairwise comparison, A1 represents the overall judgment matrix, and B1 represents the first-level judgment matrix. This comparison method can be used to construct judgment matrices at other levels. See Table 3 for details of the specific judgment matrix.

[0080]

[0081] After obtaining the judgment matrix A, the judgment matrix of the corresponding evaluation dimension is processed separately through the evaluation calculation method of the corresponding evaluation dimension to obtain the indicator weight of the corresponding evaluation dimension. The processing method corresponding to the hierarchical analysis dimension is different from the processing method corresponding to the entropy weight calculation dimension, so the weights of the first-level indicator matrix and the second-level indicator matrix are different.

[0082] Table 2 Explanation of the meaning of scales 1-9 in the AHP

[0083]

[0084] Table 3 First level judgment matrix

[0085]

[0086] The consistency test is completed by calculating the consistency index CI and the random consistency index RI, and then comparing the consistency ratio CR (CI / RI). The consistency test results of the 10 expert judgment matrix are as follows, where A (i) CR (i = 1, 2, 3...10) represents the consistency test result of the i-th expert:

[0087] A (1) CR=0.0176<0.1,A (2) CR=0.0176<0.1,A (3) CR=0.0089<0.1,

[0088] A (4) CR=0.0000<0.1,A (5) CR=0.0176<0.1,A (6) CR=0.0036<0.1,

[0089] A (7) CR=0.0000<0.1,A (8) CR=0.0517<0.1,A (9) CR=0.0000<0.1,

[0090] A (10) CR=0.0834<0.1

[0091] The above consistency test results show that the judgment matrices of all 10 experts passed the test, proving that the experts have a high level of consistency. Based on this, this application uses the geometric mean method to aggregate the judgment matrices of each expert to obtain the target layer group judgment matrix. The specific results are summarized in Table 4.

[0092] Table 4 First-level group decision-making judgment matrix

[0093]

[0094] After the consistency test of the target layer group judgment matrix, the test result is ACR=0.0000<0.1. The result shows that the consistency test of the target layer group decision judgment matrix has passed, proving its effectiveness. Based on this, the weight rankings of the three indicator factors of the criterion layer are enterprise data quality, enterprise internal structure, and external environmental influence, showing that the assetization level of enterprise data elements is mainly affected by enterprise data quality factors, followed by enterprise internal structure, while the external environment has a relatively small impact. This application continues to construct the judgment matrix of the indicator layer according to the above method to ensure the consistency and accuracy of the entire evaluation process. The results are shown in Tables 5 to 7.

[0095] Table 5B1 Layer Group Decision Judgment Matrix

[0096]

[0097] A consistency check of the above group decision matrix yielded a CR ratio of 0.0368 (CR < 0.1), demonstrating its validity and feasibility. Based on the calculated weights, a consistency check of the judgment matrix for the group objective layer at level B1 within the enterprise data quality indicator layer yielded a CR ratio of 0.0368, which is less than the threshold of 0.1. This indicates that the judgment matrix for the group objective layer passed the consistency check and is valid and feasible. Based on the weights calculated from this matrix, the influencing factors within the enterprise data quality indicator layer can be ranked by weight. The results show that data credibility has the greatest impact on enterprise data assetization, followed by data availability. Specifically, the order is: data credibility > data availability > data accuracy > data timeliness > data integrity > data security.

[0098] Table 6B2 layer group decision judgment matrix

[0099]

[0100] A consistency check of the judgment matrix for the group objective layer at level B2 revealed a consistency ratio (CR) of 0.0228, which is less than the threshold of 0.1. This indicates that the judgment matrix for the group objective layer passed the consistency check, demonstrating its validity and feasibility. Based on the weights calculated from this matrix, the influencing factors at the enterprise internal structure indicator layer can be ranked by weight. The ranking of the factors influencing enterprise data assetization within the enterprise internal structure indicator layer is as follows: talent structure > technical facilities > enterprise organizational structure > business processes > enterprise decision-making mechanisms.

[0101] Table 7B3 layer group decision judgment matrix

[0102]

[0103] Similarly, a consistency check of the group decision matrix at layer B3 showed a consistency ratio (CR) of 0.0455, also below the threshold of 0.1. Therefore, the consistency check passed, proving that the group decision matrix is valid and feasible. Based on the weights calculated from this matrix, the influencing factors in the external environmental impact indicator layer can be ranked by weight. The ranking of factors influencing enterprise data assetization within this indicator layer is as follows: technological development level > industry competition impact > relevant policies and regulations > industry market demand. Within the enterprise data quality indicator layer, data credibility and data availability dominate, indicating that ensuring the authenticity of data sources and the efficient accessibility of information flows is paramount in the data assetization process. Furthermore, managing data accuracy and timeliness helps ensure dynamic updates and reduce information redundancy. Data integrity and security, while relatively less weighted, play an essential role in preventing potential information distortion and risk spillover. Therefore, enterprises should focus on improving the verifiability and collectability of data's core value and establishing comprehensive real-time information feedback and risk isolation mechanisms to ensure robust management and maximize the value of data assets throughout their lifecycle.

[0104] In the hierarchical dimension, this application performs a comprehensive weight calculation on the influencing factor indicators of the enterprise data assetization level. This step involves weighted calculation of the indicator layer and the criterion layer to determine the relative importance of each indicator in the overall evaluation. The calculation results are shown in Table 8.

[0105] Table 8 Weights of first-level indicators

[0106]

[0107] By calculating the weights for each objective layer, we found that the weights for enterprise data asset quality, internal enterprise structure, and external environmental influence are 0.4393, 0.4086, and 0.1521, respectively. This indicates that among the factors influencing the assetization of enterprise data elements, the primary consideration is enterprise data quality, which plays a crucial role in the assessment of enterprise data element assetization. Data credibility and availability are the primary factors influencing enterprise data element assetization. Secondly, internal enterprise structure is a secondary factor influencing the assetization of enterprise data elements. Within an enterprise, the organizational structure determines the flow of data and the paths for information exchange, and the enterprise's decision-making mechanism in turn influences how the enterprise manages and utilizes data. The talent structure and technical infrastructure also determine the objective strength of the enterprise's data processing capabilities. External environmental factors are also a key factor influencing the assetization of enterprise data elements. These factors primarily include relevant policies and regulations, industry competition, market demand, and the current level of data technology development. Because different enterprises operate in different industries, positions, and levels of development, they need to understand their own situation from both an internal and external perspective. Based on market conditions and policy environment, rationally adjust future development strategies.

[0108] Enterprise data quality encompasses six secondary indicators: data integrity, data accuracy, data timeliness, data security, data credibility, and data availability. The weights for each indicator are 0.0391, 0.0595, 0.0460, 0.0240, 0.1393, and 0.1315, respectively. The order of consideration is data credibility, data availability, data accuracy, data timeliness, data integrity, and data security. Data credibility and availability are key factors influencing the assetization of data elements. High credibility and availability of data generated by an enterprise are essential for treating data as an asset. Therefore, when enterprises liquidate data as an asset, their credibility and availability must be their primary considerations.

[0109] The internal structure of an enterprise encompasses five secondary indicators: organizational structure, decision-making mechanism, talent structure, technical facilities, and business processes. The weights for each indicator are 0.0676, 0.0656, 0.1064, 0.1014, and 0.0676, respectively. The order of consideration is talent structure, technical facilities, internal structure, business processes, and decision-making mechanism. Compared to internal structure, decision-making mechanism, and business processes, talent structure and technical facilities are more susceptible to change. The flow of digital talent and the evolution of technical facilities have a relatively significant impact on the assetization of enterprise data elements. Therefore, internal structure is the primary consideration in data assetization.

[0110] External environmental influences encompass four secondary indicators: relevant policies and regulations, industry competition, technological development, and industry market demand. The weights for each indicator are 0.0359, 0.0333, 0.0544, and 0.0285, respectively. The ranking of these indicators is technological development, relevant policies and regulations, industry competition, and industry market demand. External environmental influences influence the assetization of enterprise data elements at a macro level. Relevant policies and regulations provide guidance and reference for enterprises, while industry competition and market demand provide the driving force and product orientation. Technological development provides the technical support for enterprise data assetization.

[0111] By calculating the weights of the target layer, we found that the weights of enterprise data asset quality, internal architecture, and external environment are 0.4393, 0.4086, and 0.1521, respectively. Data quality is the primary influencing factor, with data credibility and availability being particularly critical. Internal architecture is a secondary factor, with talent structure and infrastructure and technology having a significant impact. The external environment, including policies and regulations, competition, market demand, and technological development, also has a significant impact on data assetization.

[0112] The secondary indicators of enterprise data quality include completeness, accuracy, timeliness, security, credibility and availability, with weights of 0.0391, 0.0595, 0.0460, 0.0240, 0.1393 and 0.1315 respectively, among which credibility and availability rank first.

[0113] The secondary indicators of the internal structure of an enterprise include organizational structure, decision-making mechanism, talent structure, technical facilities and business processes, with weights of 0.0676, 0.0656, 0.1064, 0.1014 and 0.0676 respectively, with talent structure and facility technology ranking first.

[0114] Secondary indicators of external environmental influences include policies and regulations, competition, market demand, and technological development, with weights of 0.0359, 0.0333, 0.0544, and 0.0285, respectively, with technological development ranking first. These weights provide a basis for a company's data assetization strategy.

[0115] In the specific implementation process of step S1021, there is another embodiment: the evaluation dimension at least includes an entropy weight calculation dimension;

[0116] The step of processing the judgment matrices of the corresponding evaluation dimensions respectively by the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions based on the judgment matrices includes:

[0117] S102121. Standardize the judgment matrix, calculate the information entropy of the standardized judgment matrix, and extract quantitative characteristics of the multiple indicators;

[0118] S102122. Calculate the quantitative characteristics of the multiple indicators and the information entropy based on a preset computing network to obtain second-level indicator weights.

[0119] In steps S10102121-102122, the judgment matrix is normalized based on formula (6):

[0120]

[0121] Among them, Y ij Indicates the value assigned by the jth indicator to the i-th indicator of the judgment matrix after standardization; a ij Indicates the value assigned by the jth indicator to the i-th indicator of the judgment matrix; a i The vector representing the i-th indicator of the asset valuation matrix;

[0122] The information entropy of the standardized judgment matrix is calculated based on formula (7):

[0123]

[0124] Where: E i represents the information entropy of the i-th indicator; m represents the number of indicators, and the quantitative characteristics are the specific numbers of the multiple indicators. That is, the preset computing network provided by formula (8) calculates the quantitative characteristics of the multiple indicators and the information entropy to obtain the second-level indicator weight:

[0125]

[0126] where w” i It represents the second-level indicator weight of the i-th indicator calculated by the entropy weight method.

[0127] In the entropy weight calculation dimension, eight experts were invited to score each of the 15 indicators at the indicator level on a scale of 1 to 5, indicating each indicator's importance to the assetization of data elements. The experts were numbered x1 to x8, and their scores for each indicator are shown in Table 9. A scoring matrix was constructed based on the expert scores, which allowed the calculation of the entropy value for each indicator.

[0128] Table 9 Score table of each indicator

[0129]

[0130] The weight of each indicator is calculated based on the entropy value. These weights reflect the relative importance of each indicator in the overall evaluation. Finally, these weights are organized into a weight matrix for subsequent comprehensive evaluation. Figure 2The distribution of the scores given by the eight experts and the average score is shown in Table 10. The weights of the second-level indicators calculated based on formulas (6)-(8) are shown in Table 10.

[0131] Table 10 Second level indicator weights

[0132]

[0133] Table 11 Indicators and corresponding weights

[0134]

[0135] In step S103, the present application establishes a multi-level evaluation parameter set for the multiple influencing factors and the multiple indicators based on the fuzzy comprehensive evaluation method, and also constructs an evaluation set E, wherein the evaluation set adopts the DCMM maturity model level V = {V1, V2, V3, V4, V5} = {initial level (I), management level (II), defined level (III), quantitative management level (IV), optimization level (V)}, so as to construct a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set according to the membership function model, as shown in formula (9):

[0136]

[0137] Among them, R i is the fuzzy evaluation matrix; r ab The ratio of the number of experts who made the bth evaluation set for the ath parameter to the total number of experts.

[0138] In the specific implementation process of step S103, there is an embodiment in which: the establishing of a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators includes:

[0139] S1031. Based on the subordinate relationship between each influencing factor and the corresponding indicator, call multiple indicators corresponding to the influencing factors respectively;

[0140] S1032: Construct a multi-level evaluation parameter set with a subordinate relationship based on each of the influencing factors and the multiple indicators.

[0141] In steps S1031-S1032, based on the fact that each influencing factor has a corresponding indicator, there is a subordinate relationship between the influencing factors and the corresponding indicators, that is, the influencing factors are primary indicators, and the corresponding multiple indicators are secondary indicators. The evaluation parameter set U = {U1, U2, U3} = {enterprise data asset quality, enterprise internal structure, external environment impact}, and the corresponding multiple indicators are secondary indicators. The corresponding secondary indicator parameter set U1 = {U11, U12, U13, U14, U15, U16} = {data integrity, data accuracy, data timeliness, data security, data credibility, data availability}, U2 = {U21, U22, U23, U24, U25} = {enterprise organizational structure, enterprise decision-making mechanism, talent structure, technical facilities, business processes}, U3 = {U31, U32, U33, U34} = {relevant policies and regulations, industry competition impact, technological development level, industry market demand}.

[0142] In step S104, after obtaining the comprehensive indicator weight, the present application obtains the weight vector of the comprehensive indicator weight based on the comprehensive indicator weight, and calculates the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight. The enterprise data assetization level score is expressed in the form of membership. The weight vector is extracted from the comprehensive indicator weights of multiple influencing factors in each secondary indicator. The enterprise data assetization level of the enterprise is determined based on the enterprise data assetization level score and the judgment interval in the enterprise data assetization level table shown in Table 12. This study adopts a triple combination strategy of the hierarchical analysis method, the entropy weight method and the fuzzy comprehensive evaluation method to analyze and study the enterprise data assetization level, give full play to the respective advantages of the three different methods, organically combine qualitative analysis with quantitative analysis, eliminate the limitations of traditional evaluation methods in dealing with ambiguity and uncertainty, improve the comprehensiveness and objectivity of the research, and provide theoretical support and decision-making reference for promoting the process of enterprise data assetization.

[0143] Table 12 Assetization Level Rating Table

[0144]

[0145] In the specific implementation process of step S104, there is an embodiment in which the enterprise data assetization level score of the enterprise is calculated based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, including:

[0146] S10411. Calculate a total fuzzy evaluation matrix of the multiple influencing factors based on the fuzzy evaluation matrix corresponding to each influencing factor and a weight vector corresponding to the comprehensive indicator weights of the multiple influencing factors;

[0147] S10412. Calculate the weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight to obtain the enterprise data assetization level score.

[0148] In steps S10411-S10412, a weight vector W is obtained from the comprehensive intellectual property weights of multiple influencing factors in each secondary indicator. i , and the weight vector W i The fuzzy comprehensive evaluation matrix R corresponding to the above i By weighted calculation, we can obtain the fuzzy comprehensive evaluation vectors F1, F2, and F3 of enterprise data asset quality, enterprise internal structure, and external environmental impact. Then, we combine the weight vectors to form the total fuzzy evaluation matrix R = [F1 F2 F3] of the indicator layer. T and the weight vector W=(W1,W2,....,W n-1 W n ) is used to calculate the total fuzzy comprehensive evaluation set F, and the corresponding score is obtained by multiplying the total fuzzy comprehensive evaluation set F with the comment set E, as shown in formulas (10)-(12):

[0149] F i =W i ×R i (10)

[0150] F=[F1,F2,F3…F m ](11);

[0151] L=E×F T (12);

[0152] Wherein, L represents the enterprise data assetization level score, and the enterprise data assetization level score is represented based on the degree of membership.

[0153] Taking Company A as an example, we evaluated and analyzed its data assetization level. Company A is an internet company primarily engaged in cloud computing and big data. It has established a comprehensive data management system covering data collection, storage, processing, analysis, and security protection, and has developed a mature process and management system. Furthermore, the company regularly audits and assesses data quality, ensuring a high level of automation and digitization.

[0154] Enterprise Data Asset Quality: Company A primarily relies on automated data collection, preserving historical data for long-term trend analysis. Data accuracy is generally good, but data gaps and inconsistencies caused by system integration issues remain a concern. Regarding security, the company implements high-level data encryption and access control measures, and regularly conducts vulnerability detection and patching. The database utilizes a redundant backup strategy, with real-time backup of critical business data and weekly comprehensive data backups.

[0155] Internal Structure: Company A employs a flat management structure and has established a dedicated data management department responsible for handling data-related matters. Employees are generally highly skilled, with regular professional skills training. The data analysis team consists of 10-12 experienced data scientists and 8-10 senior data engineers. Business processes are highly standardized, managed and monitored through an ERP system, with regular optimization and improvement. Internally, the company prioritizes innovation and teamwork, resulting in high employee satisfaction and engagement. An employee feedback mechanism is in place to solicit feedback and suggestions.

[0156] External environmental influences: The current economic environment is stable, the industry's development prospects are positive, and policies supporting the development of data assets have been introduced, creating favorable conditions for business expansion. On the technological front, the rapid development of cloud computing, big data, and artificial intelligence technologies has provided companies with a rich array of technological options. Society's reliance on information technology services continues to increase, and market demand continues to grow. Simultaneously, rapid technological advancements in the industry require companies to continuously innovate.

[0157] Invite 10 domain experts to score each indicator in the criteria layer and construct fuzzy comprehensive evaluation matrices R1, R2 and R3 for enterprise data quality, enterprise internal structure and external environmental impact respectively.

[0158]

[0159] For the criterion layer, the first-level fuzzy comprehensive evaluation evaluates the enterprise data quality B1, the enterprise internal structure B2, and the external environment impact B3 in turn. The weight vector W of each second-level indicator, i.e., the asset weight of multiple influencing factors, is i The corresponding fuzzy comprehensive evaluation matrix R i By weighted calculation, we can obtain the fuzzy comprehensive evaluation vectors F1, F2, and F3 of the enterprise data asset quality, internal enterprise structure, and external environmental impact.

[0160] F1=W1×R1=(0,0,0.2661,0.3809,0.3529)

[0161] F2=W2×R2=(0,0,0.2176,0.4967,0.2856)

[0162] F3=W3×R3=(0,0,0.0813,0.3650,0.5536)

[0163] The first-level fuzzy comprehensive evaluation results of enterprise data quality B1, enterprise internal structure B2, and external environment impact B3 are as follows: Figure 3 As shown. According to the maximum membership principle, the evaluation levels of enterprise data quality B1, enterprise internal structure B2, and external environmental impact B3 are IV, IV, and V respectively, and the evaluation results are relatively reasonable. Then, the evaluation weight vectors are combined to form the total fuzzy evaluation matrix R = [F1 F2 F3] of the indicator layer. T and the criterion layer W=(W1,W2,....,W n-1 W n ) to calculate F.

[0164]

[0165] The evaluation level corresponding to the secondary fuzzy comprehensive evaluation result of enterprise data assetization degree A1 is as follows: Figure 4 As shown in the figure, based on the principle of maximum membership, the evaluation level of company A's enterprise data assetization is rated IV. The first-level fuzzy comprehensive evaluation results refer to the first-level fuzzy comprehensive evaluation of enterprise data quality (B1), internal enterprise structure (B2), and external environmental impact (B3). The second-level fuzzy comprehensive evaluation results refer to the second-level fuzzy comprehensive evaluation results of the enterprise data assetization degree (A1).

[0166] Since the IV and V membership degrees of the secondary fuzzy comprehensive evaluation are 0.4266 and 0.3584, respectively, which are relatively close, in the specific implementation process of step S104, there is another embodiment: after calculating the weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight to obtain the enterprise data assetization level score, the following steps are included:

[0167] S10421. Determine whether the enterprise data assetization level score meets the preset weighted processing conditions;

[0168] S10422. If the judgment is satisfied, corresponding processing measures are taken according to the enterprise data assetization level score to obtain the final enterprise data assetization level.

[0169] In steps S10421-S10422, the present application further determines based on the enterprise data assetization level score. If the enterprise data assetization level score meets the preset weighted processing condition, where the preset weighted processing condition is that there is a conflict in the enterprise data assetization level scores corresponding to the multiple influencing factors, that is, although the enterprise data assetization level scores or memberships corresponding to the multiple influencing factors are relatively close, they correspond to different enterprise data assetization levels, that is, the numerical differences between the memberships are relatively close but correspond to different enterprise data assetization levels, then the corresponding processing method is the weighted average method, and the level is set to T (20, 40, 60, 80, 100). Τ The scoring vector of , each value represents the score of the five evaluation results. Thus, the final data assetization level score of the enterprise (S) is:

[0170] S=F×T=82.868

[0171] The company's data assetization level is rated IV. There is still room for improvement in data asset quality and internal governance structures. The company will also maintain continuous innovation based on industry technological advancements and leverage the policy dividends supporting data asset development.

[0172] Example 2

[0173] This application also provides a device for evaluating the level of enterprise data assetization, such as Figure 5 The following is a block diagram of a device for assessing the level of enterprise data assetization. The functions implemented by this device correspond to the steps of executing the method for assessing the level of enterprise data assetization on a terminal device. This device can be understood as a component of a server including a processor. The device described in this application includes:

[0174] Establishing module 501, for determining multiple factors affecting the level of enterprise data assetization, establishing an asset analysis model corresponding to the level of enterprise data assetization based on the multiple factors, and obtaining multiple indicators corresponding to each factor of the asset analysis model;

[0175] A fusion module 502 is configured to calculate, based on multiple evaluation dimensions, the evaluation weights of the multiple indicators corresponding to the evaluation dimensions, as well as the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions, and to fuse the first-level indicator weights and second-level indicator weights to obtain a comprehensive indicator weight for the multiple indicators;

[0176] A construction module 503 is configured to establish a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators, so as to construct a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set;

[0177] The calculation module 504 is used to calculate the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score.

[0178] In a feasible implementation, the fusion module includes:

[0179] Based on the determined evaluation dimension, a corresponding evaluation calculation method is called to calculate the evaluation scores pre-input for the multiple indicators based on the evaluation calculation method to obtain a corresponding judgment matrix;

[0180] The judgment matrices of the corresponding evaluation dimensions are processed separately through the evaluation calculation methods of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions.

[0181] In a feasible implementation manner, the fusion module further includes:

[0182] A processing module, configured to perform normalization processing on the judgment matrix according to a preset normalization processing network to obtain a normalized judgment matrix;

[0183] The first calculation module is used to calculate the maximum eigenvalue of the normalized judgment matrix and obtain the first-level indicator weight based on the maximum eigenvalue.

[0184] In a feasible implementation, the fusion module also includes:

[0185] An extraction module, configured to standardize the judgment matrix, calculate the information entropy of the standardized judgment matrix, and extract quantitative features of the multiple indicators;

[0186] The second calculation module is used to calculate the quantitative characteristics of the multiple indicators and the information entropy based on a preset calculation network to obtain the second-level indicator weights.

[0187] In a feasible implementation, the calculation module includes:

[0188] A third calculation module is configured to calculate a total fuzzy evaluation matrix of the multiple influencing factors based on the fuzzy evaluation matrix corresponding to each influencing factor and a weight vector corresponding to the comprehensive indicator weights of the multiple influencing factors;

[0189] The fourth calculation module is used to calculate the weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight to obtain the enterprise data assetization level score.

[0190] In a feasible implementation manner, the calculation module further includes:

[0191] A judgment module, used to determine whether the enterprise data assetization level score meets the preset weighted processing conditions;

[0192] The taking module is used to take corresponding processing measures for the enterprise data assetization level score if it is determined to be satisfied, so as to obtain the final enterprise data assetization level.

[0193] In a feasible implementation, the building module includes:

[0194] A calling module, configured to call a plurality of indicators corresponding to the influencing factors based on the subordinate relationship between the influencing factors and the corresponding indicators;

[0195] The first construction module is used to construct a multi-level evaluation parameter set with a subordinate relationship based on the influencing factors and the multiple indicators.

[0196] Example 3

[0197] The present application also provides an electronic device, such as Figure 6 As shown, it includes: a processor 601, a memory 602 and a bus 603, the memory 602 stores machine-readable instructions executable by the processor 601, and when the electronic device is running, the processor 601 and the memory 602 communicate through the bus 603, and when the machine-readable instructions are executed by the processor 601, any one of the steps of the enterprise data assetization level assessment method is executed.

[0198] Example 4

[0199] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program executes any one of the steps of the enterprise data assetization level assessment method.

[0200] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0201] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0202] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0203] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, platform server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0204] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the level of enterprise data assetization, characterized in that: The method comprises: Determine multiple factors that affect the enterprise data assetization level, establish an asset analysis model corresponding to the enterprise data assetization level based on the multiple factors, and obtain multiple indicators corresponding to each factor in the asset analysis model; Based on multiple evaluation dimensions, the evaluation weights of the multiple indicators corresponding to the evaluation dimensions and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions are calculated respectively, and the first-level indicator weights and second-level indicator weights are integrated to obtain the comprehensive indicator weights of the multiple indicators; Establishing a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators, and constructing a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set; The enterprise data assetization level score of the enterprise is calculated based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score.

2. The method according to claim 1, characterized in that The step of calculating the evaluation weights of the multiple indicators corresponding to the evaluation dimensions and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions based on the multiple evaluation dimensions includes: Based on the determined evaluation dimension, a corresponding evaluation calculation method is called to calculate the evaluation scores pre-input for the multiple indicators based on the evaluation calculation method to obtain a corresponding judgment matrix; The judgment matrices of the corresponding evaluation dimensions are processed separately through the evaluation calculation methods of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions.

3. The method according to claim 2, characterized in that The evaluation dimension at least includes a hierarchical analysis dimension; The judgment matrices of the corresponding evaluation dimensions are processed respectively by the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions, including: Normalizing the judgment matrix according to a preset normalization processing network to obtain a normalized judgment matrix; The maximum eigenvalue of the normalized judgment matrix is calculated, and the first-level indicator weight is obtained based on the maximum eigenvalue.

4. The method according to claim 2, characterized in that The evaluation dimension includes at least an entropy weight calculation dimension; The step of processing the judgment matrices of the corresponding evaluation dimensions respectively by the evaluation calculation method of the corresponding evaluation dimensions to obtain the indicator weights of the corresponding evaluation dimensions based on the judgment matrices includes: Standardizing the judgment matrix, calculating the information entropy of the standardized judgment matrix, and extracting the quantitative characteristics of the multiple indicators; The quantitative characteristics of the multiple indicators and the information entropy are calculated based on a preset computing network to obtain the second-level indicator weights.

5. The method according to claim 1, wherein The step of calculating the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight includes: Calculating a total fuzzy evaluation matrix of the multiple influencing factors based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector corresponding to the comprehensive indicator weights of the multiple influencing factors; The weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight is calculated to obtain the enterprise data assetization level score.

6. The method according to claim 5, characterized in that After calculating the weight vector of the total fuzzy evaluation matrix and the comprehensive indicator weight to obtain the enterprise data assetization level score, the method includes: Determining whether the enterprise data assetization level score meets the preset weighted processing conditions; If it is determined to be satisfied, corresponding processing measures will be taken according to the enterprise data assetization level score to obtain the final enterprise data assetization level.

7. The method according to claim 1, characterized in that The establishing of a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators includes: Based on the subordinate relationship between each influencing factor and the corresponding indicator, calling multiple indicators corresponding to the influencing factors respectively; A multi-level evaluation parameter set with a subordinate relationship is constructed based on each of the influencing factors and the multiple indicators.

8. An enterprise data assetization level assessment device, characterized in that: The device comprises: Establishing a module for determining multiple factors affecting the level of enterprise data assetization, establishing an asset analysis model corresponding to the level of enterprise data assetization based on the multiple factors, and obtaining multiple indicators corresponding to each of the factors in the asset analysis model; A fusion module is used to calculate the evaluation weights of the evaluation dimensions corresponding to the multiple indicators and the first-level indicator weights and second-level indicator weights of the corresponding evaluation dimensions based on the multiple evaluation dimensions, and to fuse the first-level indicator weights and second-level indicator weights to obtain the comprehensive indicator weights of the multiple indicators; A construction module, configured to establish a multi-level evaluation parameter set based on the multiple influencing factors and the multiple indicators, so as to construct a fuzzy evaluation matrix corresponding to each influencing factor based on the multi-level evaluation parameter set; A calculation module is used to calculate the enterprise data assetization level score of the enterprise based on the fuzzy evaluation matrix corresponding to each influencing factor and the weight vector of the comprehensive indicator weight, so as to determine the enterprise data assetization level of the enterprise based on the enterprise data assetization level score.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. When the machine-readable instructions are executed by the processor, the steps of the enterprise data assetization level assessment method as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of a method for evaluating the level of enterprise data assetization as described in any one of claims 1 to 7.

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