Data asset valuation system fused with data quality evaluation

Abstract

A data asset valuation system fusing data quality evaluation relates to the field of management systems, and is characterized by comprising an input module, a data evaluation module, a data operation module, an output module and a log module, the data asset valuation system fused with the data quality evaluation has the advantages that the data asset valuation system fused with the data quality evaluation can be fused with the data asset valuation system fused with the data quality evaluation, the comprehensiveness and the accuracy of a data asset valuation result can be improved, a scientific and flexible valuation model is established, different data asset characteristics and evaluation requirements are adapted, and the data asset valuation system fused with the data quality evaluation is suitable for popularization and application. The application scene of the data assets is accurately identified, the problem that the value of the data assets deviates from the real application value is solved, data asset valuation is realized by means of informatization, the result is guaranteed to be fair and accurate, and the influence of manual adjustment is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of management systems, and in particular to improving the comprehensiveness and accuracy of data asset valuation results, establishing a scientific and flexible valuation model that is adapted to different data asset characteristics and evaluation requirements, accurately identifying application scenarios of data assets, and solving the problem that the value of data assets deviates from the true application value. A data asset valuation system that integrates data quality evaluation is implemented by information technology to ensure fair and accurate results and effectively avoid the impact of manual adjustments. Background Art

[0002] Data asset valuation refers to the professional behavior of asset valuation institutions and their asset valuation professionals complying with laws, administrative regulations and asset valuation standards, assessing and estimating the value of data assets for specific purposes on the valuation base date according to the entrustment, and issuing asset valuation reports.

[0003] As a new production factor, data has been on par with traditional production factors such as land, labor, capital and technology, and has jointly promoted the growth of the modern economy. In this digital age, the importance of data assets has become increasingly prominent. It not only records the operation of the enterprise, but also contains huge business value and innovation potential. Therefore, how to accurately evaluate the value of data assets has become the focus of attention of enterprises and even the entire society. Data asset valuation can reasonably quantify the economic value brought by data and help enterprises better manage and utilize data resources. Through comprehensive, objective and scientific valuation of data assets, enterprises can more clearly understand the role of data in business decision-making, product innovation and market insights, and formulate data strategies more accurately based on this, optimize resource allocation and achieve sustainable innovation.

[0004] As an asset, data assets should have measurable actual value. Data assets are different from other assets. Data assets have the characteristics of non-physicality, reliance, shareability, processability, and value volatility. These characteristics will have a certain impact on the valuation of data assets. At the same time, according to the requirements of the "Guiding Opinions on Data Asset Valuation", when valuing data assets, the factors that affect the value of data assets should be analyzed, namely cost factors, scenario factors, market factors and market factors. Among them, cost factors include the initial expenses, direct costs, indirect costs, opportunity costs and related taxes involved in the formation of data assets. Scenario factors include the corresponding scope of use, application scenarios, business models, market prospects, financial forecasts and application risks of data assets. Market factors mainly include the main trading markets related to data assets, market activity, market participants and accessibility. Quality factors mainly include the accuracy, consistency, completeness, standardization, timeliness and accessibility of data.

[0005] In view of the above data asset evaluation content, if the cost method, income method and market method of traditional asset evaluation are directly applied to value data assets, it is easy to cause a high deviation between the value of data assets and the true value. Therefore, combined with the development of data asset valuation informatization, it is necessary to develop a data asset valuation system and use informatization to promote the informatization transformation of data asset valuation business. The core algorithm of the data asset valuation system combines the advantages and limitations of traditional evaluation methods, and establishes a data asset valuation system that integrates data quality evaluation from the perspective of the characteristics of data assets themselves, the stage they are in, the future application potential, and the release of value driven by data applications. Summary of the invention

[0006] The embodiment of the present invention provides a data asset valuation system that integrates data quality evaluation. The method of the present invention can improve the comprehensiveness and accuracy of data asset valuation results, establish a scientific and flexible valuation model, adapt to different data asset characteristics and evaluation requirements, accurately identify application scenarios of data assets, and solve the problem that the value of data assets deviates from the actual application value. It realizes data asset valuation by information technology, ensures that the results are fair and accurate, and effectively avoids the impact of manual adjustments.

[0007] The present invention provides a data asset valuation system integrating data quality evaluation, wherein the system comprises: an input module, a data evaluation module, a data operation module, an output module, and a log module; wherein the input module collects data information from multiple data sources, supports users to directly input data assets and related information, and automatically accesses and integrates data resources in external databases;

[0008] Input module: used to input and store data information, including basic information of data assets, basic information of enterprises, and information on business needs for evaluation; receive and store data assets transmitted from external databases; transmit the acquired data information and data assets to the data evaluation module in the form of data packets;

[0009] Data evaluation module: After receiving the data assets and data information provided by the input module, it starts working and conducts multi-dimensional and in-depth analysis and evaluation of the quality of the data assets. After the evaluation is completed, the data evaluation module passes the results together with the original data assets and data information to the data operation module;

[0010] Data operation module: performs necessary cleaning, conversion and formatting of data to ensure that the data is suitable for subsequent valuation calculations. The data operation module accesses the valuation resource library to obtain the parameter information and algorithm model required to build the data operation model, builds a valuation model suitable for the current data assets, and calculates the specific valuation results of the data assets by calling the execution program of the valuation model. The data operation module passes the complete evaluation information to the output module;

[0011] Output module: presents the evaluation results in a user-friendly way. Users can select a suitable evaluation result template according to their needs, organize and fill in various information received from the data operation module according to the template format, and export the evaluation report in text format for users to review, share, and use as a basis for decision-making;

[0012] Log module: records the operation information, error information, warning information and key data flow information of each module and each stage in the system, and conducts problem troubleshooting, performance analysis and auditing through detailed log records.

[0013] A data asset valuation system integrating data quality evaluation, wherein the input module includes an input unit and an acquisition unit; wherein:

[0014] Input unit: Receives and stores data assets. Establishes a communication connection between the receiving end of the input unit and the output end of the external database, accesses the external database to obtain data assets; the output end of the input unit establishes a communication connection with the receiving end of the acquisition unit to send data assets to the acquisition unit;

[0015] Acquisition unit: acquires and stores data information, including basic information of data assets, basic information of the enterprise, and information on business needs, to support the operation of data evaluation modules and data calculation modules.

[0016] A data asset valuation system integrating data quality evaluation, wherein the data evaluation module includes a determination unit, an evaluation unit, and an evaluation resource library, wherein:

[0017] Determination unit: receives data information and data assets from the input module, is used to receive data information and data assets transmitted by the input module, executes the determination program to obtain the determination result and sends the determination result to the evaluation unit and the data operation module, and sends the received data information and data assets to the evaluation unit and the data operation module;

[0018] Evaluation unit: according to the judgment result sent by the judgment unit, start the corresponding data evaluation program, after receiving the execution instruction of the data evaluation task, synchronously receive and store data information and data assets, use data assets as the data source for running the data evaluation program, access the established rule library and indicator library in parallel, run the data evaluation program to calculate and obtain the data evaluation result, and send the data information, data assets and data evaluation result to the data calculation module in the form of data packets;

[0019] Evaluation resource library: the standards, models, algorithms, and rules required for data evaluation, used to store evaluation rule information and evaluation indicator information; used to manage evaluation rule information and evaluation indicator information, where the management modes can be create, query, update, delete, add, and index.

[0020] A data asset valuation system integrating data quality evaluation, wherein the determination unit comprises a receiving subunit, a pre-determination subunit, a decision distribution subunit, a report verification subunit, a final determination execution subunit, and a result subunit; wherein:

[0021] Receiving subunit: Receives and stores data information and data assets transmitted by the input module, and triggers the operation signal of the pre-determination subunit upon receiving the data information;

[0022] Pre-determination subunit: reads and analyzes the data information according to the data information and data assets stored in the receiving subunit, and synchronously calls the preset pre-determination program to determine whether the data information includes a professional work report; when it is determined that the data information includes a professional work report, generates a preliminary determination result that includes the professional work report; when it is determined that the data information does not include a professional report, generates a preliminary determination result that does not include the professional work report;

[0023] Decision distribution subunit: Based on the output results of the pre-determination subunit, the decision distribution subunit performs further processing. When receiving a preliminary determination result containing a professional work report, it generates a start verification instruction, and simultaneously sends the identified professional work report to the report verification subunit for subsequent processing; when receiving a preliminary determination result that does not contain a professional work report, it generates an execution data evaluation task instruction, and simultaneously sends the execution data evaluation task instruction and the received data information and data assets to the evaluation unit;

[0024] Report verification subunit: After receiving the start verification instruction and the data information of the professional work report, the report verification subunit starts the data verification program, calls the data verification program to perform document parsing, keyword matching, semantic analysis, pattern recognition, and information extraction operations on the professional work report to determine whether the professional work report contains quality evaluation information; when the professional work report does not contain quality evaluation information, a result without quality evaluation information is generated and displayed visually;

[0025] Final judgment execution subunit: according to the output result of the report verification subunit, the verification result is subjected to final judgment operation processing. When the final judgment result contains quality evaluation information, an instruction to abandon the data evaluation task is generated, and the instruction to abandon the data evaluation is sent to the evaluation unit to interrupt the operation of the data evaluation program, write the extracted quality evaluation information into the data evaluation result template, and send the data information, data assets, and data evaluation results to the data operation module in the form of a data packet, and visualize the data evaluation results; when the final judgment result does not contain quality evaluation information, an instruction to execute the data evaluation task is generated, and the instruction to execute the data evaluation task is sent to the evaluation unit to start the execution of the data evaluation program, and the data information and data assets are sent to the evaluation unit in the form of a data packet;

[0026] Result subunit: The result subunit is used to store the processing results generated by the pre-determination subunit, the pre-determination subunit, the verification subunit, and the final judgment execution unit, and supports the query, analysis, call, editing, and visual display of processing results.

[0027] A data asset valuation system integrating data quality evaluation, wherein the evaluation unit includes an instruction receiving subunit, a data preparation subunit, a model building and configuration subunit, a rule matching and automatic updating subunit, an indicator matching and adaptive adjustment subunit, and an evaluation output subunit; wherein:

[0028] Instruction receiving subunit: receiving the data evaluation task execution instruction sent by the determination unit, triggering the start of the data evaluation program, triggering the data evaluation program start device when receiving the data evaluation task execution instruction, triggering the data evaluation program interrupt device when receiving the data evaluation task abandonment instruction, and recording the instruction reception time and start time for subsequent tracking and auditing;

[0029] Data preparation subunit: When the data evaluation program is started, it receives and stores the data information and data assets transmitted by the determination unit, prepares information for the data evaluation model, and provides data query and retrieval interfaces so that the data evaluation module can quickly access the required data;

[0030] Model building and configuration subunit: builds data evaluation models based on the received data information, provides model management and configuration interface, and supports modification, update and version control of data evaluation models;

[0031] Rule matching and automatic update subunit: calls and reads the rule information in the evaluation resource library, matches it with the rule layer of the data evaluation model, and automatically updates the rule information in the data evaluation model according to the matching results. It provides rule update log and version control functions to track the rule change history and restore the old version;

[0032] Indicator matching and adaptive adjustment subunit: calls and reads indicator information in the evaluation resource library, matches it with the indicator layer of the data evaluation model, and adaptively adjusts the indicator information in the data evaluation model according to the matching results; supports dynamic addition, deletion and weight adjustment of indicators to adapt to different evaluation scenarios and needs;

[0033] Evaluation output subunit: Receives data information and data assets from the input module, and passes the data information and data assets as input sources to the data evaluation model; runs the evaluation layer running program of the data evaluation model to perform mathematical operations on the input data. Writes the data evaluation model operation results into the data evaluation result template, simultaneously visualizes the data evaluation results, and sends the data information, data assets, and data evaluation results to the data operation module in the form of data packets; the evaluation output subunit provides data evaluation result query and report generation functions, so that users can easily view and analyze data evaluation results;

[0034] The mathematical formula of the evaluation layer of the above data evaluation model is:

[0035]

[0036] Where AQ represents the result of data quality evaluation operation;

[0037] A represents the number of data elements in the data asset that meet the requirements of the data evaluation index. The data acquisition method is to iteratively process each data element in the data asset according to the index layer information in the data evaluation model, perform index calculation on each data element, and simultaneously determine whether each data source element meets the requirements of the determined index layer information, and record the number of data that meet the conditions;

[0038] B represents the total number of data elements in the data asset. The data is obtained by traversing the data asset, calculating each data element of the data asset, and accumulating the counting results to obtain the total number of data elements in the data asset;

[0039] α ij Represents the quality evaluation weight parameter. The data comes from the quality evaluation weight parameter set in the data evaluation database.

[0040] β i It represents the weight parameter of each evaluation rule. The data is obtained by accessing the evaluation resource library and data information;

[0041] m represents the number of rule information in the rule layer of the data evaluation model;

[0042] n represents the number of indicator information in the indicator layer of the data evaluation model.

[0043] A data asset valuation system integrating data quality evaluation, wherein the model building and configuration subunit includes a model matching component, a rule layer configuration component, and an indicator layer configuration component, wherein:

[0044] Model matching component: based on the received data information, automatically matches the data information with the preset data evaluation model and selects the matching data evaluation model. The data evaluation model includes the rule layer, the indicator layer, and the evaluation layer.

[0045] Rule layer configuration component: Based on the selected data evaluation model, it automatically generates a rule layer rule information list and writes the read rule information into the data evaluation model;

[0046] Indicator layer configuration component: Based on the selected data evaluation model, it automatically generates an indicator layer indicator information list and writes the read indicator information into the data evaluation model.

[0047] A data asset valuation system integrating data quality evaluation, wherein the rule matching and automatic updating subunit comprises a rule reading component, a rule matching component, and a rule updating component, wherein:

[0048] Rule reading component: calls the rule information list of the rule layer, and synchronously accesses the evaluation resource library to read the evaluation rule information; the evaluation rule information includes accuracy, consistency, completeness, standardization, timeliness, and accessibility;

[0049] Rule matching component: Verify and match the read evaluation rule information with the rule layer information of the data evaluation model;

[0050] Rule update component: Updates the rule layer information of the data evaluation model based on the matching results; when the matching results are completely consistent, the rule layer information in the data evaluation model is kept original; when the matching results are not completely consistent, the data update overwrite operation is triggered to write the read evaluation rule information into the rule layer of the data evaluation model to overwrite and correct the inconsistent parts.

[0051] A data asset valuation system integrating data quality evaluation, wherein the indicator matching and adaptive adjustment subunit comprises an indicator reading component, an indicator matching component, and an indicator adaptive adjustment component, wherein:

[0052] Indicator reading component: according to the indicator layer information of the data evaluation model determined by the indicator matching and automatic update sub-unit, call the indicator layer indicator information list, and synchronously access the evaluation resource library to read the evaluation indicator information; the evaluation indicator information includes content accuracy, precision accuracy, record duplication rate, dirty data occurrence rate, element assignment consistency rate, element filling rate, record filling rate, data item filling rate, value domain compliance rate, metadata compliance rate, format compliance rate, security compliance rate, cycle timeliness, real-time timeliness, and accessibility;

[0053] Indicator matching component: Verify, extract, and match the read evaluation indicator information with the indicator layer information of the data evaluation model to check whether the indicators are consistent;

[0054] Indicator adaptive adjustment component: based on the matching results and user needs, update and adjust the indicator layer information of the data evaluation model; when the matching results are completely consistent, keep the indicator layer information in the data evaluation model original; when the matching results are not completely consistent, trigger the data update overwrite operation, write the read evaluation indicator information into the indicator layer of the data evaluation model, and overwrite and correct the inconsistent parts.

[0055] A data asset valuation system integrating data quality evaluation, wherein the data operation module includes a preprocessing unit, an operation unit, and an operation resource library; wherein:

[0056] Preprocessing unit: receiving and storing data assets, data information, and data evaluation results transmitted by the data evaluation module; calling the preprocessing program to perform preprocessing operations on the parameter information of the data operation model according to the received data information, data assets, and data evaluation results, and updating the parameter information according to the preprocessing results; transmitting data information, data assets, and data evaluation results to the operation unit;

[0057] Operation unit: receives and stores data assets, data information, and data evaluation results transmitted by the preprocessing unit; accesses case information in the valuation resource library based on the received data information, synchronously matches the data information with the case information, and automatically generates a data application scenario solution according to the preset application scenario solution template based on the matching result; based on the generated data application scenario solution, calls and runs the data operation model to obtain the data asset valuation result; is used to transmit data assets, data information, data evaluation results, and data asset valuation results to the output module for users to query and use;

[0058] Operational resource library: Stores and manages case information and valuation information, and provides comprehensive management support for this information, where the management modes include creation, query, update, deletion, addition, and indexing.

[0059] A data asset valuation system integrating data quality evaluation, wherein the output module includes a parsing unit, a selection unit, and an export unit; wherein:

[0060] Parsing unit: receives data packets from the data operation module, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; parses the received data packets, extracts key information, stores the extracted key information, and transmits it to the selection unit;

[0061] Selection unit: supports users to select one or more applicable templates from the preset evaluation result template library, and fills the data information processed by the analysis unit into the selected template;

[0062] Export unit: supports users to view the evaluation results online and provides the function of exporting the evaluation results in text format.

[0063] A data asset valuation system integrating data quality evaluation, wherein the preprocessing unit includes an information receiving subunit, a parameter initialization subunit, a parameter verification subunit, a verification result feedback subunit, and a parameter optimization subunit; wherein:

[0064] Information receiving subunit: receiving data assets, data information, data evaluation results, and obtaining information required for preprocessing and parameter configuration operations;

[0065] Parameter initialization subunit: calls the data asset valuation model, obtains the parameter information list required by the data asset valuation model, traverses the parameter information, performs comparison and matching operations on the data information obtained by the information receiving subunit, writes the data information that meets the parameter information requirements into the corresponding parameters, and completes the preliminary configuration and initialization of the parameter information;

[0066] Parameter verification subunit: accesses the valuation resource library to read the current valuation information, and compares and verifies the initialized parameter information with the valuation information stored in the valuation resource library;

[0067] Verification result feedback subunit: automatically determines whether the parameter information needs to be updated based on the parameter verification results. When the parameter verification results are completely consistent, the parameter information is kept as it is; when the parameter verification results are not completely consistent, the data synchronization mechanism is triggered to perform data update and overwrite operations, and the valuation information in the valuation resource library is written into the parameter information to overwrite and correct the inconsistent parts;

[0068] Parameter optimization subunit: After confirming that the parameter information is correct, the parameter optimization subunit performs final optimization on the parameters and passes the verified parameter information as input to the data operation model to provide data support for subsequent valuation calculations.

[0069] A data asset valuation system integrating data quality evaluation, wherein the operation unit includes a data preprocessing subunit, an application scenario generation subunit, a benefit value operation subunit, an application scenario judgment subunit, a combined benefit operation subunit, an asset valuation operation subunit, and a valuation result output subunit; wherein:

[0070] Data preprocessing subunit: receives data assets, data information, and data evaluation results, performs preliminary data cleaning and verification operations on the received information, and obtains the information required for the data calculation model;

[0071] Application scenario generation subunit: Based on the received data information and data evaluation results, intelligently generate one or more data application scenario solutions, the application scenario solution includes the application scenario number, application scenario name, application scenario introduction, and the industry content to which the application scenario belongs. Provide application scenario solution management and configuration interface, support modification, update and version control of application scenario solutions. The application scenario generation subunit includes resource library access component, solution initialization component, case information comparison component, and solution optimization generation component;

[0072] Among them, the resource library access component: accesses the valuation resource library to obtain predefined application scenario solution templates;

[0073] Solution initialization component: writes the received data information and data evaluation results into the application scenario solution template to generate the initial application scenario solution;

[0074] Case information comparison component: synchronously accesses the case information library of the valuation resource library, and compares, matches, and verifies the cached application scenario solution information with the case information;

[0075] Solution optimization generation component: automatically generates one or more application scenario solutions that conform to data logic based on the comparison and verification results;

[0076] The benefit value calculation subunit: according to the generated application scenario solution, the benefit value calculation model is called in parallel through multi-threading to calculate the benefit value calculation result of each application scenario solution; the benefit value calculation subunit includes an application scenario selection component, a parameter selection component, a benefit calculation component, a benefit correction component, and a benefit result component;

[0077] Application scenario selection component: calls the generated application scenario solution, obtains the application scenario data information for the benefit value calculation model, and writes the application scenario data information into the benefit value calculation model;

[0078] Parameter selection component: reads the received application scenario information and extracts the parameter information of the profit value calculation model; compares the extracted model parameter information with the various parameter information of the valuation resource library, uses the preset model parameter selection rules to screen and select the final model parameters, and writes the final model parameters into the profit value calculation model; the above model parameters include profit forecast parameters and data asset profit period parameters;

[0079] When extracting revenue forecast parameters, access the revenue forecast parameter table of the valuation resource library, select one or more parameters in the revenue forecast parameter table, generate and write revenue forecast parameters, where the revenue forecast parameter table includes cost, revenue, cost profit margin, profit month-on-month growth rate, number of users, usage frequency, single-use price, revenue conversion rate, revenue generation method, and revenue generation channel parameters;

[0080] When extracting the data asset income period parameters, access the income period table of the valuation resource library, select one or more parameters in the income period table, generate and write the data asset income period parameters, where the income period table includes the business information of the assessed unit, data asset authorization contract information, data asset rights restriction information, data asset utilization status information, data asset update frequency, and market demand change forecast information parameters;

[0081] Profit calculation component: call and run the profit value calculation model, calculate the profit value calculation result, and synchronously cache the profit value calculation result and send it to the profit correction component; the profit value calculation formula is as follows:

[0082] R t =P i ×Q i

[0083] Where:

[0084] R t Indicates the calculation result of the benefit value under the selected application scenario;

[0085] P i Indicates that under the selected application scenario, a one-time fee is charged for data assets within the data asset income period, and the fee information comes from the received data information and the parameter information extracted by accessing the valuation resource library;

[0086] Q i Indicates the number of times fees are collected within the data asset income period under the selected application scenario. The number of times fees are collected is derived from the received data information and the parameter information extracted by accessing the valuation resource library.

[0087] Profit correction component: reads the application scenario and data information, extracts the correction parameter information, and synchronously compares the correction parameter information with the various correction information in the valuation resource library. According to the comparison result, the profit value calculation result is corrected, and the profit value correction result is obtained by calculation, and the profit value correction result is sent to the profit result component;

[0088] The above correction parameter information is the weight correction parameter. When extracting the weight correction parameter, access the weight parameter table of the valuation resource library, select one or more parameters, generate and write the weight correction parameter, where the weight parameter table includes data quality, data capacity, data value density, market scarcity, market competitiveness, data set density, data governance capabilities, data update frequency, technology implementation capabilities, technology update speed, and data integration parameters;

[0089] Profit result component: receives the profit value correction result and writes it into the profit value calculation result, and simultaneously sends the application scenario plan, profit value calculation result, data information, data assets, and data evaluation results in the form of data packets;

[0090] Application scenario judgment subunit: count the received application scenario solutions and generate the number of application scenario solutions, judge the number of generated application scenario solutions, and when the judgment result is equal to 1, automatically generate an instruction to execute asset valuation operation, and send the instruction to execute asset valuation operation to the asset valuation operation subunit; when the judgment result is not equal to 1, automatically generate an instruction to execute combination income operation, and send the instruction to execute combination income operation to the combination income operation subunit;

[0091] Combination income calculation subunit: when receiving the instruction to execute combination income calculation, it triggers the combination income calculation starting device, calls the combination income value model, and calculates the combination income value; writes the combination income value into the income value calculation result, generates the instruction to execute the asset valuation calculation, and sends the instruction to execute the asset valuation calculation to the asset valuation calculation subunit, and sends the application scenario plan, income value calculation result, data information, data assets, and data evaluation result to the asset valuation calculation subunit in the form of a data packet; provides a model management and configuration interface, and supports the modification, update and version control of the combination income calculation model;

[0092] The formula for calculating the combined return value is as follows:

[0093]

[0094] Where:

[0095] F t Represents the combined return value;

[0096] R n Indicates the profit value calculation result obtained under the nth application scenario;

[0097] ω i Indicates the application scenario realization degree parameter. When obtaining the application scenario realization degree parameter, access the organizational capability table of the valuation resource library, select one or more parameters in the organizational capability table, generate and write the application scenario realization degree parameter, where the organizational capability table includes investment amount, investment period, investment payback period, business needs, market prospects, technical capabilities, management capabilities, security capabilities, risk assessment, application risks, market feedback information, and iterative optimization information parameters;

[0098] Represents the mean value of the application scenario realization parameter;

[0099] i represents the number of parameter information of realization degree of selected application scenarios;

[0100] n represents the number of application scenario solutions generated after the application scenario judgment subunit runs;

[0101] ROI n Represents the investment rate of return, where the calculation formula is: Where T n Indicates the total investment cost required to develop the nth application scenario. The data comes from the data information input by the user.

[0102] represents the mean of investment return rate;

[0103] Asset valuation operation subunit: When receiving the instruction to execute asset valuation operation, it triggers the data asset valuation operation start device, calls the data asset valuation model, calculates the data asset valuation, writes the combined income value into the income value calculation result, and sends the application scenario plan, data assets, data information, data evaluation results, and data asset valuation results in the form of a data packet to the valuation result output subunit; provides a model management and configuration interface, and supports the modification, update and version control of the asset valuation operation model;

[0104] The calculation formula for group data asset valuation is as follows:

[0105]

[0106] Where: P represents the final valuation of the data asset;

[0107] F t Represents the combined return value;

[0108] Adj t represents the adjusted parameter factor;

[0109] R f Indicates the risk-free rate of return of data asset applications. When obtaining the risk-free rate of return, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write the risk-free rate of return, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators, profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, safety risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters;

[0110] risk iRepresents the risk indicators of data asset applications. When obtaining risk indicators, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write risk indicators, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators, profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, security risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters;

[0111] Represents the mean value of data asset application risk indicators;

[0112] n represents the remaining income period of data assets, which is derived from the data information input by users;

[0113] i represents the number of risk indicators selected for data asset application;

[0114] t indicates the age information selected during data asset evaluation;

[0115] Valuation result output subunit: Receive and store the results of all calculation processes, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; organize the received data packets according to the preset format and send the data packets to the output module. The valuation result output subunit provides data asset valuation result query and report generation functions, so that users can easily view and analyze data asset valuation results.

[0116] It can be seen from this that:

[0117] A data asset valuation system that integrates data quality evaluation in an embodiment of the present invention can integrate a data asset valuation system with data quality evaluation. The method of the present invention can improve the comprehensiveness and accuracy of data asset valuation results, establish a scientific and flexible valuation model, adapt to different data asset characteristics and evaluation requirements, accurately identify application scenarios of data assets, and solve the problem of data asset value deviating from the actual application value. It realizes data asset valuation by information technology, ensures that the results are fair and accurate, and effectively avoids the impact of manual adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] Figure 1 A schematic diagram of the overall structure of a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0119] Figure 2 A schematic diagram of the structure of an input module in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0120] Figure 3 A schematic diagram of the structure of a data evaluation module in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0121] Figure 4 A schematic diagram of the structure of a determination unit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0122] Figure 5 A schematic diagram of the structure of an evaluation unit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0123] Figure 6 A schematic diagram of the structure of a model building and configuration subunit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0124] Figure 7 A schematic diagram of the structure of a rule matching and automatic updating subunit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0125] Figure 8 A schematic diagram of the structure of an indicator matching and adaptive adjustment subunit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0126] Fig. 9 A schematic diagram of the structure of a data operation module in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0127] Fig.10 A schematic diagram of the structure of an output module in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0128] Fig.11 A schematic diagram of the structure of a pre-processing unit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention;

[0129] Fig.12 A schematic diagram of the structure of a computing unit in a data asset valuation system integrating data quality evaluation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0130] In order to enable those skilled in the art to better understand the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0131] Embodiment 1:

[0132] Figure 1It is a data asset valuation system that integrates data quality evaluation, such as Figure 1 A data asset valuation system integrating data quality evaluation is shown, the system includes: an input module, a data evaluation module, a data operation module, an output module, and a log module; wherein the input module collects data information from multiple data sources, supports users to directly input data assets and related information, and automatically accesses and integrates data resources in external databases;

[0133] Input module: used to input and store data information, including basic information of data assets, basic information of enterprises, and information on business needs for evaluation; receive and store data assets transmitted from external databases; transmit the acquired data information and data assets to the data evaluation module in the form of data packets;

[0134] Data evaluation module: After receiving the data assets and data information provided by the input module, it starts working and conducts multi-dimensional and in-depth analysis and evaluation of the quality of the data assets. After the evaluation is completed, the data evaluation module passes the results together with the original data assets and data information to the data operation module;

[0135] Data operation module: performs necessary cleaning, conversion and formatting of data to ensure that the data is suitable for subsequent valuation calculations. The data operation module accesses the valuation resource library to obtain the parameter information and algorithm model required to build the data operation model, builds a valuation model suitable for the current data assets, and calculates the specific valuation results of the data assets by calling the execution program of the valuation model. The data operation module passes the complete evaluation information to the output module;

[0136] Output module: presents the evaluation results in a user-friendly way. Users can select a suitable evaluation result template according to their needs, organize and fill in various information received from the data operation module according to the template format, and export the evaluation report in text format for users to review, share, and use as a basis for decision-making;

[0137] Log module: records the operation information, error information, warning information and key data flow information of each module and each stage in the system, and conducts problem troubleshooting, performance analysis and auditing through detailed log records.

[0138] like Figure 2 As shown, a data asset valuation system integrating data quality evaluation, the input module includes an input unit and an acquisition unit; wherein:

[0139] Input unit: Receives and stores data assets. Establishes a communication connection between the receiving end of the input unit and the output end of the external database, accesses the external database to obtain data assets; the output end of the input unit establishes a communication connection with the receiving end of the acquisition unit to send data assets to the acquisition unit;

[0140] Acquisition unit: acquires and stores data information, including basic information of data assets, basic information of the enterprise, and information on business needs, to support the operation of data evaluation modules and data calculation modules.

[0141] like Figure 3 As shown, a data asset valuation system integrating data quality evaluation, the data evaluation module includes a determination unit, an evaluation unit, and an evaluation resource library, wherein:

[0142] Determination unit: receives data information and data assets from the input module, is used to receive data information and data assets transmitted by the input module, executes the determination program to obtain the determination result and sends the determination result to the evaluation unit and the data operation module, and sends the received data information and data assets to the evaluation unit and the data operation module;

[0143] Evaluation unit: according to the judgment result sent by the judgment unit, start the corresponding data evaluation program, after receiving the execution instruction of the data evaluation task, synchronously receive and store data information and data assets, use data assets as the data source for running the data evaluation program, access the established rule library and indicator library in parallel, run the data evaluation program to calculate and obtain the data evaluation result, and send the data information, data assets and data evaluation result to the data calculation module in the form of data packets;

[0144] Evaluation resource library: the standards, models, algorithms, and rules required for data evaluation, used to store evaluation rule information and evaluation indicator information; used to manage evaluation rule information and evaluation indicator information, where the management modes can be create, query, update, delete, add, and index.

[0145] like Figure 4 As shown, a data asset valuation system integrating data quality evaluation, the determination unit includes a receiving subunit, a pre-determination subunit, a decision distribution subunit, a report verification subunit, a final judgment execution subunit, and a result subunit; wherein,

[0146] Receiving subunit: Receives and stores data information and data assets transmitted by the input module, and triggers the operation signal of the pre-determination subunit upon receiving the data information;

[0147] Pre-determination subunit: reads and analyzes the data information according to the data information and data assets stored in the receiving subunit, and synchronously calls the preset pre-determination program to determine whether the data information includes a professional work report; when it is determined that the data information includes a professional work report, generates a preliminary determination result that includes the professional work report; when it is determined that the data information does not include a professional report, generates a preliminary determination result that does not include the professional work report;

[0148] Decision distribution subunit: Based on the output results of the pre-determination subunit, the decision distribution subunit performs further processing. When receiving a preliminary determination result containing a professional work report, it generates a start verification instruction, and simultaneously sends the identified professional work report to the report verification subunit for subsequent processing; when receiving a preliminary determination result that does not contain a professional work report, it generates an execution data evaluation task instruction, and simultaneously sends the execution data evaluation task instruction and the received data information and data assets to the evaluation unit;

[0149] Report verification subunit: After receiving the start verification instruction and the data information of the professional work report, the report verification subunit starts the data verification program, calls the data verification program to perform document parsing, keyword matching, semantic analysis, pattern recognition, and information extraction operations on the professional work report to determine whether the professional work report contains quality evaluation information; when the professional work report does not contain quality evaluation information, a result without quality evaluation information is generated and displayed visually;

[0150] Final judgment execution subunit: according to the output result of the report verification subunit, the verification result is subjected to final judgment operation processing. When the final judgment result contains quality evaluation information, an instruction to abandon the data evaluation task is generated, and the instruction to abandon the data evaluation is sent to the evaluation unit to interrupt the operation of the data evaluation program, write the extracted quality evaluation information into the data evaluation result template, and send the data information, data assets, and data evaluation results to the data operation module in the form of a data packet, and visualize the data evaluation results; when the final judgment result does not contain quality evaluation information, an instruction to execute the data evaluation task is generated, and the instruction to execute the data evaluation task is sent to the evaluation unit to start the execution of the data evaluation program, and the data information and data assets are sent to the evaluation unit in the form of a data packet;

[0151] Result subunit: The result subunit is used to store the processing results generated by the pre-determination subunit, the pre-determination subunit, the verification subunit, and the final judgment execution subunit, and supports the query, analysis, call, editing, and visual display of processing results.

[0152] like Figure 5 As shown, a data asset valuation system integrating data quality evaluation, the evaluation unit includes an instruction receiving subunit, a data preparation subunit, a model building and configuration subunit, a rule matching and automatic updating subunit, an indicator matching and adaptive adjustment subunit, and an evaluation output subunit; wherein:

[0153] Instruction receiving subunit: receiving the data evaluation task execution instruction sent by the determination unit, triggering the start of the data evaluation program, triggering the data evaluation program start device when receiving the data evaluation task execution instruction, triggering the data evaluation program interrupt device when receiving the data evaluation task abandonment instruction, and recording the instruction reception time and start time for subsequent tracking and auditing;

[0154] Data preparation subunit: When the data evaluation program is started, it receives and stores the data information and data assets transmitted by the determination unit, prepares information for the data evaluation model, and provides data query and retrieval interfaces so that the data evaluation module can quickly access the required data;

[0155] Model building and configuration subunit: builds data evaluation models based on the received data information, provides model management and configuration interface, and supports modification, update and version control of data evaluation models;

[0156] Rule matching and automatic update subunit: calls and reads the rule information in the evaluation resource library, matches it with the rule layer of the data evaluation model, and automatically updates the rule information in the data evaluation model according to the matching results. It provides rule update log and version control functions to track the rule change history and restore the old version;

[0157] Indicator matching and adaptive adjustment subunit: calls and reads indicator information in the evaluation resource library, matches it with the indicator layer of the data evaluation model, and adaptively adjusts the indicator information in the data evaluation model according to the matching results; supports dynamic addition, deletion and weight adjustment of indicators to adapt to different evaluation scenarios and needs;

[0158] Evaluation output subunit: Receives data information and data assets from the input module, and passes the data information and data assets as input sources to the data evaluation model; runs the evaluation layer running program of the data evaluation model to perform mathematical operations on the input data. Writes the data evaluation model operation results into the data evaluation result template, simultaneously visualizes the data evaluation results, and sends the data information, data assets, and data evaluation results to the data operation module in the form of data packets; the evaluation output subunit provides data evaluation result query and report generation functions, so that users can easily view and analyze data evaluation results;

[0159] The mathematical formula of the evaluation layer of the above data evaluation model is:

[0160]

[0161] Where AQ represents the result of data quality evaluation operation;

[0162] A represents the number of data elements in the data asset that meet the requirements of the data evaluation index. The data acquisition method is to iteratively process each data element in the data asset according to the index layer information in the data evaluation model, perform index calculation on each data element, and simultaneously determine whether each data source element meets the requirements of the determined index layer information, and record the number of data that meet the conditions;

[0163] B represents the total number of data elements in the data asset. The data is obtained by traversing the data asset, calculating each data element of the data asset, and accumulating the counting results to obtain the total number of data elements in the data asset;

[0164] α ij Represents the quality evaluation weight parameter. The data comes from the quality evaluation weight parameter set in the data evaluation database.

[0165] β i It represents the weight parameter of each evaluation rule. The data is obtained by accessing the evaluation resource library and data information;

[0166] m represents the number of rule information in the rule layer of the data evaluation model;

[0167] n represents the number of indicator information in the indicator layer of the data evaluation model.

[0168] like Figure 6 As shown, a data asset valuation system integrating data quality evaluation, the model building and configuration subunit includes a model matching component, a rule layer configuration component, and an indicator layer configuration component, wherein:

[0169] Model matching component: based on the received data information, automatically matches the data information with the preset data evaluation model and selects the matching data evaluation model. The data evaluation model includes the rule layer, the indicator layer, and the evaluation layer.

[0170] Rule layer configuration component: Based on the selected data evaluation model, it automatically generates a rule layer rule information list and writes the read rule information into the data evaluation model;

[0171] Indicator layer configuration component: Based on the selected data evaluation model, it automatically generates an indicator layer indicator information list and writes the read indicator information into the data evaluation model.

[0172] like Figure 7 As shown, a data asset valuation system integrating data quality evaluation, the rule matching and automatic update subunit includes a rule reading component, a rule matching component, and a rule updating component, wherein:

[0173] Rule reading component: calls the rule information list of the rule layer, and synchronously accesses the evaluation resource library to read the evaluation rule information; the evaluation rule information includes accuracy, consistency, completeness, standardization, timeliness, and accessibility;

[0174] Rule matching component: Verify and match the read evaluation rule information with the rule layer information of the data evaluation model;

[0175] Rule update component: Updates the rule layer information of the data evaluation model based on the matching results; when the matching results are completely consistent, the rule layer information in the data evaluation model is kept original; when the matching results are not completely consistent, the data update overwrite operation is triggered to write the read evaluation rule information into the rule layer of the data evaluation model to overwrite and correct the inconsistent parts.

[0176] like Figure 8 As shown, a data asset valuation system integrating data quality evaluation, the index matching and adaptive adjustment subunit includes an index reading component, an index matching component, and an index adaptive adjustment component, wherein:

[0177] Indicator reading component: according to the indicator layer information of the data evaluation model determined by the indicator matching and automatic update sub-unit, call the indicator layer indicator information list, and synchronously access the evaluation resource library to read the evaluation indicator information; the evaluation indicator information includes content accuracy, precision accuracy, record duplication rate, dirty data occurrence rate, element assignment consistency rate, element filling rate, record filling rate, data item filling rate, value domain compliance rate, metadata compliance rate, format compliance rate, security compliance rate, cycle timeliness, real-time timeliness, and accessibility;

[0178] Indicator matching component: Verify, extract, and match the read evaluation indicator information with the indicator layer information of the data evaluation model to check whether the indicators are consistent;

[0179] Indicator adaptive adjustment component: based on the matching results and user needs, update and adjust the indicator layer information of the data evaluation model; when the matching results are completely consistent, keep the indicator layer information in the data evaluation model original; when the matching results are not completely consistent, trigger the data update overwrite operation, write the read evaluation indicator information into the indicator layer of the data evaluation model, and overwrite and correct the inconsistent parts.

[0180] like Fig. 9 As shown, a data asset valuation system integrating data quality evaluation, the data operation module includes a preprocessing unit, an operation unit, and an operation resource library; wherein:

[0181] Preprocessing unit: receiving and storing data assets, data information, and data evaluation results transmitted by the data evaluation module; calling the preprocessing program to perform preprocessing operations on the parameter information of the data operation model according to the received data information, data assets, and data evaluation results, and updating the parameter information according to the preprocessing results; transmitting data information, data assets, and data evaluation results to the operation unit;

[0182] Operation unit: receives and stores data assets, data information, and data evaluation results transmitted by the preprocessing unit; accesses case information in the valuation resource library based on the received data information, synchronously matches the data information with the case information, and automatically generates a data application scenario solution according to the preset application scenario solution template based on the matching result; based on the generated data application scenario solution, calls and runs the data operation model to obtain the data asset valuation result; is used to transmit data assets, data information, data evaluation results, and data asset valuation results to the output module for users to query and use;

[0183] Operational resource library: Stores and manages case information and valuation information, and provides comprehensive management support for this information, where the management modes include creation, query, update, deletion, addition, and indexing.

[0184] like Fig.10 As shown, a data asset valuation system integrating data quality evaluation, the output module includes a parsing unit, a selection unit, and an export unit; wherein:

[0185] Parsing unit: receives data packets from the data operation module, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; parses the received data packets, extracts key information, stores the extracted key information, and transmits it to the selection unit;

[0186] Selection unit: supports users to select one or more applicable templates from the preset evaluation result template library, and fills the data information processed by the analysis unit into the selected template;

[0187] Export unit: supports users to view the evaluation results online and provides the function of exporting the evaluation results in text format.

[0188] like Fig.11 As shown, a data asset valuation system integrating data quality evaluation, the preprocessing unit includes an information receiving subunit, a parameter initialization subunit, a parameter verification subunit, a verification result feedback subunit, and a parameter optimization subunit; wherein:

[0189] Information receiving subunit: receiving data assets, data information, data evaluation results, and obtaining information required for preprocessing and parameter configuration operations;

[0190] Parameter initialization subunit: calls the data asset valuation model, obtains the parameter information list required by the data asset valuation model, traverses the parameter information, performs comparison and matching operations on the data information obtained by the information receiving subunit, writes the data information that meets the parameter information requirements into the corresponding parameters, and completes the preliminary configuration and initialization of the parameter information;

[0191] Parameter verification subunit: accesses the valuation resource library to read the current valuation information, and compares and verifies the initialized parameter information with the valuation information stored in the valuation resource library;

[0192] Verification result feedback subunit: automatically determines whether the parameter information needs to be updated based on the parameter verification results. When the parameter verification results are completely consistent, the parameter information is kept as it is; when the parameter verification results are not completely consistent, the data synchronization mechanism is triggered to perform data update and overwrite operations, and the valuation information in the valuation resource library is written into the parameter information to overwrite and correct the inconsistent parts;

[0193] Parameter optimization subunit: After confirming that the parameter information is correct, the parameter optimization subunit performs final optimization on the parameters and passes the verified parameter information as input to the data operation model to provide data support for subsequent valuation calculations.

[0194] like Fig.12 As shown, a data asset valuation system integrating data quality evaluation, the operation unit includes a data preprocessing subunit, an application scenario generation subunit, a benefit value operation subunit, an application scenario judgment subunit, a combined benefit operation subunit, an asset valuation operation subunit, and a valuation result output subunit; wherein:

[0195] Data preprocessing subunit: receives data assets, data information, and data evaluation results, performs preliminary data cleaning and verification operations on the received information, and obtains the information required for the data calculation model;

[0196] Application scenario generation subunit: Based on the received data information and data evaluation results, intelligently generate one or more data application scenario solutions, the application scenario solution includes the application scenario number, application scenario name, application scenario introduction, and the industry content to which the application scenario belongs. Provide application scenario solution management and configuration interface, support modification, update and version control of application scenario solutions. The application scenario generation subunit includes resource library access component, solution initialization component, case information comparison component, and solution optimization generation component;

[0197] Among them, the resource library access component: accesses the valuation resource library to obtain predefined application scenario solution templates;

[0198] Solution initialization component: writes the received data information and data evaluation results into the application scenario solution template to generate the initial application scenario solution;

[0199] Case information comparison component: synchronously accesses the case information library of the valuation resource library, and compares, matches, and verifies the cached application scenario solution information with the case information;

[0200] Solution optimization generation component: automatically generates one or more application scenario solutions that conform to data logic based on the comparison and verification results;

[0201] The benefit value calculation subunit: according to the generated application scenario solution, the benefit value calculation model is called in parallel through multi-threading to calculate the benefit value calculation result of each application scenario solution; the benefit value calculation subunit includes an application scenario selection component, a parameter selection component, a benefit calculation component, a benefit correction component, and a benefit result component;

[0202] Application scenario selection component: calls the generated application scenario solution, obtains the application scenario data information for the benefit value calculation model, and writes the application scenario data information into the benefit value calculation model;

[0203] Parameter selection component: reads the received application scenario information and extracts the parameter information of the profit value calculation model; compares the extracted model parameter information with the various parameter information of the valuation resource library, uses the preset model parameter selection rules to screen and select the final model parameters, and writes the final model parameters into the profit value calculation model; the above model parameters include profit forecast parameters and data asset profit period parameters;

[0204] When extracting revenue forecast parameters, access the revenue forecast parameter table of the valuation resource library, select one or more parameters in the revenue forecast parameter table, generate and write revenue forecast parameters, where the revenue forecast parameter table includes cost, revenue, cost profit margin, profit month-on-month growth rate, number of users, usage frequency, single-use price, revenue conversion rate, revenue generation method, and revenue generation channel parameters;

[0205] When extracting the data asset income period parameters, access the income period table of the valuation resource library, select one or more parameters in the income period table, generate and write the data asset income period parameters, where the income period table includes the business information of the assessed unit, data asset authorization contract information, data asset rights restriction information, data asset utilization status information, data asset update frequency, and market demand change forecast information parameters;

[0206] Profit calculation component: call and run the profit value calculation model, calculate the profit value calculation result, and synchronously cache the profit value calculation result and send it to the profit correction component; the profit value calculation formula is as follows:

[0207] R t =P i ×Q i

[0208] Where:

[0209] R t Indicates the calculation result of the benefit value under the selected application scenario;

[0210] P i Indicates that under the selected application scenario, a one-time fee is charged for data assets within the data asset income period, and the fee information comes from the received data information and the parameter information extracted by accessing the valuation resource library;

[0211] Q i Indicates the number of times fees are collected within the data asset income period under the selected application scenario. The number of times fees are collected is derived from the received data information and the parameter information extracted by accessing the valuation resource library.

[0212] Profit correction component: reads the application scenario and data information, extracts the correction parameter information, and synchronously compares the correction parameter information with the various correction information in the valuation resource library. According to the comparison result, the profit value calculation result is corrected, and the profit value correction result is obtained by calculation, and the profit value correction result is sent to the profit result component;

[0213] The above correction parameter information is the weight correction parameter. When extracting the weight correction parameter, access the weight parameter table of the valuation resource library, select one or more parameters, generate and write the weight correction parameter, where the weight parameter table includes data quality, data capacity, data value density, market scarcity, market competitiveness, data set density, data governance capabilities, data update frequency, technology implementation capabilities, technology update speed, and data integration parameters;

[0214] Profit result component: receives the profit value correction result and writes it into the profit value calculation result, and simultaneously sends the application scenario plan, profit value calculation result, data information, data assets, and data evaluation results in the form of data packets;

[0215] Application scenario judgment subunit: count the received application scenario solutions and generate the number of application scenario solutions, judge the number of generated application scenario solutions, and when the judgment result is equal to 1, automatically generate an instruction to execute asset valuation operation, and send the instruction to execute asset valuation operation to the asset valuation operation subunit; when the judgment result is not equal to 1, automatically generate an instruction to execute combination income operation, and send the instruction to execute combination income operation to the combination income operation subunit;

[0216] Combination income calculation subunit: when receiving the instruction to execute combination income calculation, it triggers the combination income calculation starting device, calls the combination income value model, and calculates the combination income value; writes the combination income value into the income value calculation result, generates the instruction to execute the asset valuation calculation, and sends the instruction to execute the asset valuation calculation to the asset valuation calculation subunit, and sends the application scenario plan, income value calculation result, data information, data assets, and data evaluation result to the asset valuation calculation subunit in the form of a data packet; provides a model management and configuration interface, and supports the modification, update and version control of the combination income calculation model;

[0217] The formula for calculating the combined return value is as follows:

[0218]

[0219] Where:

[0220] F t Represents the combined return value;

[0221] R n Indicates the profit value calculation result obtained under the nth application scenario;

[0222] ω i Indicates the application scenario realization degree parameter. When obtaining the application scenario realization degree parameter, access the organizational capability table of the valuation resource library, select one or more parameters in the organizational capability table, generate and write the application scenario realization degree parameter, where the organizational capability table includes investment amount, investment period, investment payback period, business needs, market prospects, technical capabilities, management capabilities, security capabilities, risk assessment, application risks, market feedback information, and iterative optimization information parameters;

[0223] Represents the mean value of the application scenario realization parameter;

[0224] i represents the number of parameter information of realization degree of selected application scenarios;

[0225] n represents the number of application scenario solutions generated after the application scenario judgment subunit runs;

[0226] ROI n Represents the investment rate of return, where the calculation formula is: Where T n Indicates the total investment cost required to develop the nth application scenario. The data comes from the data information input by the user.

[0227] represents the mean of investment return rate;

[0228] Asset valuation operation subunit: When receiving the instruction to execute asset valuation operation, it triggers the data asset valuation operation start device, calls the data asset valuation model, calculates the data asset valuation, writes the combined income value into the income value calculation result, and sends the application scenario plan, data assets, data information, data evaluation results, and data asset valuation results in the form of a data packet to the valuation result output subunit; provides a model management and configuration interface, and supports the modification, update and version control of the asset valuation operation model;

[0229] The calculation formula for group data asset valuation is as follows:

[0230]

[0231] Where: P represents the final valuation of the data asset;

[0232] F t Represents the combined return value;

[0233] Adj t represents the adjusted parameter factor;

[0234] R f Indicates the risk-free rate of return of data asset applications. When obtaining the risk-free rate of return, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write the risk-free rate of return, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators, profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, safety risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters;

[0235] risk i Represents the risk indicators of data asset applications. When obtaining risk indicators, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write risk indicators, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators, profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, security risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters;

[0236] Represents the mean value of data asset application risk indicators;

[0237] n represents the remaining income period of data assets, which is derived from the data information input by users;

[0238] i represents the number of risk indicators selected for data asset application;

[0239] t indicates the age information selected during data asset evaluation;

[0240] Valuation result output subunit: Receive and store the results of all calculation processes, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; organize the received data packets according to the preset format and send the data packets to the output module. The valuation result output subunit provides data asset valuation result query and report generation functions, so that users can easily view and analyze data asset valuation results.

[0241] The following is an example of a specific implementation:

[0242] A data asset valuation system integrating data quality evaluation, the data asset valuation system includes an input module, a data evaluation module, a data operation module, an output module, and a log module. Among them:

[0243] 1. Input module: The input module has input, receiving and storage functions, and is used to input and store data information, including basic information of data assets, basic information of the enterprise, and information on evaluating business needs; receive and store data assets transmitted from external databases; and transmit the acquired data information and data assets to the data evaluation module in the form of data packets.

[0244] 1.1 The receiving end of the input module establishes a communication connection with the output end of the external database to receive and store data assets. The output end of the input module is connected to the receiving end of the data evaluation module for signal transmission of data assets and data information.

[0245] 1.2 The input module includes an input unit and an acquisition unit.

[0246] 1.3 Input unit: The input unit serves as the data access point of the system. The function of the input unit is to receive and store data assets. The receiving end of the input unit is connected to the output end of the external database to access the external database to obtain data assets. The output end of the input unit is connected to the receiving end of the acquisition unit to send data assets to the acquisition unit.

[0247] 1.4 The acquisition unit described in: The function of the acquisition unit is to acquire and store data information, which includes basic information of data assets, basic information of the enterprise, and assessment business demand information, and supports the operation of the data evaluation module and the data operation module.

[0248] (1) Data information acquisition method: Provide a user input page to support users to directly input the required data information through the interface. The data information includes basic information of data assets, basic information of the enterprise, and information on business needs for evaluation. Synchronously, establish a communication connection between the receiving end of the acquisition unit and the output end of the input unit, use ETL tools to read data from the data assets stored in the input unit, and use Python to read and parse data assets in batches in parallel to obtain data information. The data information includes basic information of data assets, basic information of the enterprise, and information on business needs for evaluation.

[0249] (2) The basic information of the data assets includes the name of the data assets, data asset details, data asset storage scale, data asset coverage area, data asset update time, data asset type, the industry to which the data assets belong, data asset rights information, data asset rights restrictions, whether the data assets have been developed into products, data asset usage methods, data asset income generation methods, and professional work report content.

[0250] (3) The basic information of the enterprise includes the enterprise name, enterprise qualifications, the industry to which the enterprise belongs, and the enterprise financial data.

[0251] (4) The assessment business demand information includes the data asset assessment base date, data asset assessment purpose, and value type.

[0252] (5) The output end of the acquisition unit establishes a communication connection with the receiving end of the data evaluation module to send data information and data assets to the data evaluation module.

[0253] 2. Data evaluation module: The data evaluation module is the core module in the system responsible for data quality evaluation and verification. The data evaluation module uses the data assets stored in the input module as the data source, extracts and verifies the received data information according to the received data information, determines whether to start the data evaluation running program based on the extraction results and verification results, and generates the judgment results synchronously. According to the judgment results, the data evaluation program is run to perform the data evaluation task and store the data evaluation results, and the received data information and data evaluation results are synchronously sent to the data operation module in the form of data packets.

[0254] 2.1 The data evaluation module includes a determination unit, an evaluation unit, and an evaluation resource library. The receiving end of the data evaluation module establishes a communication connection with the output end of the input module to receive data information and data assets. The output end of the data evaluation module establishes a communication connection with the receiving end of the data operation module to transmit data information, data assets, and data evaluation results.

[0255] 2.2 The judgment unit: The judgment unit is the front-end processing part of the data evaluation module, responsible for receiving data information and data assets from the input module. The function of the judgment unit is to receive data information and data assets transmitted from the input module, execute the judgment program to obtain the judgment result and send the judgment result to the evaluation unit and the data operation module. Synchronously, the received data information and data assets are sent to the evaluation unit and the data operation module.

[0256] (1) The receiving end of the above-mentioned determination unit establishes a communication connection with the output end of the input module, and the output end of the determination unit is communicated with the receiving end of the evaluation unit and the receiving end of the data operation module.

[0257] (2) The determination unit includes a receiving subunit, a pre-determination subunit, a decision distribution subunit, a report verification subunit, a final determination execution subunit, and a result subunit.

[0258] Receiving subunit: Receives and stores data information and data assets transmitted by the input module. When the receiving subunit receives the data information, it immediately triggers the pre-determination subunit operation signal.

[0259] Pre-determination subunit: reads and parses the data information according to the data information and data assets stored in the receiving subunit, and synchronously calls the preset pre-determination program to determine whether the data information includes a professional work report. When it is determined that the data information includes a professional work report, a preliminary determination result including the professional work report is generated; when it is determined that the data information does not include a professional report, a preliminary determination result not including the professional work report is generated.

[0260] Decision distribution subunit: According to the output results of the pre-determination subunit, the decision distribution subunit performs further processing. When receiving the "preliminary determination result containing professional work reports", the "start verification instruction" is generated, and the identified professional work reports are sent to the report verification subunit for subsequent processing. When receiving the "preliminary determination result not containing professional work reports", the "execute data evaluation task" instruction is generated, and the "execute data evaluation task" instruction and the received data information and data assets are sent to the evaluation unit simultaneously.

[0261] Report verification subunit: After receiving the "start verification instruction" and the data information of the professional work report, the report verification subunit starts the data verification program. The data verification program is called to perform document parsing, keyword matching, semantic analysis, pattern recognition, and information extraction operations on the professional work report to determine whether the professional work report contains quality evaluation information. When the professional work report does not contain quality evaluation information, the "no quality evaluation information" result is generated and visualized.

[0262] Final judgment execution subunit: According to the output result of the report verification subunit, the verification result is processed by final judgment operation. When the final judgment result is "contains quality evaluation information", the "abandon data evaluation task" instruction is generated, and the "abandon data evaluation instruction" is sent to the evaluation unit to interrupt the operation of the data evaluation program. Synchronously, the extracted quality evaluation information is written into the data evaluation result template, and the data information, data assets, and data evaluation results are sent to the data operation module in the form of a data packet to visualize the data evaluation results.

[0263] When the final judgment result is "no quality evaluation information is included", an "execute data evaluation task" instruction is generated and sent to the evaluation unit to start the data evaluation program. Synchronously, the data information and data assets are sent to the evaluation unit in the form of data packets.

[0264] Result subunit: The result subunit is used to store the processing results generated by the pre-determination subunit, the pre-determination subunit, the verification subunit, and the final judgment execution subunit, and supports the query, analysis, call, editing, and visual display of processing results.

[0265] 2.3 Evaluation unit: The evaluation unit is the core execution unit of the data evaluation module. It starts the corresponding data evaluation program according to the judgment result sent by the judgment unit. After the evaluation unit executes the instruction of "execute data evaluation task" received, it synchronously receives and stores data information and data assets, uses data assets as the data source for running the data evaluation program, accesses the established rule library and indicator library in parallel, runs the data evaluation program to calculate and obtain the data evaluation result. Synchronously, the evaluation unit sends the data information, data assets, and data evaluation results to the data calculation module in the form of data packets.

[0266] (1) The receiving end of the evaluation unit establishes a communication connection with the output end of the determination unit, and the output end of the evaluation unit establishes a communication connection with the receiving end of the data operation module.

[0267] (2) When the evaluation unit receives the instruction of "abandoning the data evaluation task", the data evaluation program is interrupted. When the evaluation unit receives the instruction of "executing the data evaluation task", the data evaluation program starting device is triggered to run the data evaluation program.

[0268] (3) The evaluation unit includes an instruction receiving subunit, a data preparation subunit, a model building and configuration subunit, a rule matching and automatic updating subunit, an indicator matching and adaptive adjustment subunit, and an evaluation output subunit.

[0269] Instruction receiving subunit: Receives the data evaluation task execution instruction sent by the determination unit and triggers the start of the data evaluation program. When the "execute data evaluation task" instruction is received, the data evaluation program start device is triggered. When the "abandon data evaluation task" instruction is received, the data evaluation program interrupt device is triggered. Synchronize and record the instruction reception time and start time for subsequent tracking and auditing.

[0270] Data preparation subunit: When the data evaluation program is started, it receives and stores the data information and data assets transmitted by the determination unit, and prepares information for the data evaluation model. It provides data query and retrieval interfaces so that the data evaluation module can quickly access the required data.

[0271] Model building and configuration subunit: Build a data evaluation model based on the received data information. Provide a model management and configuration interface to support the modification, update and version control of the data evaluation model. The model building and configuration subunit includes a model matching component, a rule layer configuration component, and an indicator layer configuration component.

[0272] Model matching component: Based on the received data information, the data information is automatically matched with the preset data evaluation model and a matching data evaluation model is selected. The data evaluation model includes a rule layer, an indicator layer, and an evaluation layer.

[0273] Rule layer configuration component: Based on the selected data evaluation model, it automatically generates a rule layer rule information list and writes the read rule information into the data evaluation model.

[0274] Indicator layer configuration component: Based on the selected data evaluation model, it automatically generates an indicator layer indicator information list and writes the read indicator information into the data evaluation model.

[0275] Rule matching and automatic update subunit: call to read the rule information in the evaluation resource library, match it with the rule layer of the data evaluation model, and automatically update the rule information in the data evaluation model according to the matching results. Provide rule update log and version control functions to track the rule change history and restore the old version. The rule matching and automatic update subunit includes rule reading component, rule matching component, and rule update component.

[0276] Rule reading component: Call the rule information list of the rule layer above, and synchronously access the evaluation resource library to read the evaluation rule information. The evaluation rule information includes accuracy, consistency, completeness, standardization, timeliness, and accessibility.

[0277] Rule matching component: Verifies and matches the read evaluation rule information with the rule layer information of the data evaluation model.

[0278] Rule update component: Update the rule layer information of the data evaluation model according to the matching results. When the matching results are completely consistent, keep the rule layer information in the data evaluation model as it is. When the matching results are not completely consistent, trigger the data update overwrite operation, write the read evaluation rule information into the rule layer of the data evaluation model, and overwrite and correct the inconsistent parts.

[0279] Indicator matching and adaptive adjustment subunit: calls to read indicator information in the evaluation resource library, matches it with the indicator layer of the data evaluation model, and adaptively adjusts the indicator information in the data evaluation model according to the matching results. It supports the dynamic addition, deletion and weight adjustment of indicators to adapt to different evaluation scenarios and needs. The indicator matching and adaptive adjustment subunit includes indicator reading component, indicator matching component, and indicator adaptive adjustment component.

[0280] Indicator reading component: According to the indicator layer information of the data evaluation model determined by the indicator matching and automatic update sub-unit, the indicator layer indicator information list is called, and the evaluation resource library is accessed synchronously to read the evaluation indicator information. The evaluation indicator information includes content accuracy, precision accuracy, record duplication rate, dirty data occurrence rate, element assignment consistency rate, element filling rate, record filling rate, data item filling rate, value domain compliance rate, metadata compliance rate, format compliance rate, security compliance rate, cycle timeliness, real-time timeliness, and accessibility.

[0281] Indicator matching component: Verify, extract, and match the read evaluation indicator information with the indicator layer information of the data evaluation model to check whether the indicators are consistent.

[0282] Indicator adaptive adjustment component: Update and adjust the indicator layer information of the data evaluation model according to the matching results and user needs. When the matching results are completely consistent, the indicator layer information in the data evaluation model is kept as it is. When the matching results are not completely consistent, the data update overwrite operation is triggered to write the read evaluation indicator information into the indicator layer of the data evaluation model to overwrite and correct the inconsistent parts.

[0283] Evaluation output subunit: Receives data information and data assets from the input module, and passes the data information and data assets as input sources to the data evaluation model. Runs the evaluation layer running program of the data evaluation model to perform mathematical operations on the input data. Writes the data evaluation model operation results into the data evaluation result template, simultaneously visualizes the data evaluation results, and sends the data information, data assets, and data evaluation results to the data operation module in the form of data packets. The evaluation output subunit provides data evaluation result query and report generation functions, so that users can easily view and analyze data evaluation results.

[0284] The mathematical formula of the evaluation layer of the above data evaluation model is:

[0285]

[0286] Where AQ represents the result of data quality evaluation operation;

[0287] A represents the number of data elements in the data asset that meet the requirements of the data evaluation index. The data acquisition method is to iteratively process each data element in the data asset according to the index layer information in the data evaluation model, perform index accounting on each data element, and simultaneously determine whether each data source element meets the requirements of the determined index layer information, and record the number of data that meet the conditions.

[0288] B represents the total number of data elements in the data asset. The data acquisition method is to traverse the data asset, calculate each data element of the data asset, and accumulate the counting results to obtain the total number of data elements in the data asset.

[0289] α ij Represents the quality evaluation weight parameter. The data comes from the quality evaluation weight parameter that has been set in the data evaluation library.

[0290] β i It represents the weight parameter of each evaluation rule. The data is obtained by accessing the evaluation resource library and data information.

[0291] m represents the number of rule information in the rule layer of the data evaluation model.

[0292] n represents the number of indicator information in the indicator layer of the data evaluation model.

[0293] 2.4 Evaluation resource library: The evaluation resource library is the knowledge base and tool library of the data evaluation module, which contains the standards, models, algorithms, and rules required for data evaluation. The evaluation resource library is used to store evaluation rule information and evaluation indicator information; it is used to manage evaluation rule information and evaluation indicator information, where the management mode can be creation, query, update, deletion, addition, and indexing.

[0294] The above-mentioned evaluation resource library includes an evaluation rule library and an evaluation index library, which are used to provide data for the calculation of the evaluation unit.

[0295] The above-mentioned evaluation rule library is used to store and manage evaluation rule information, support creation, query, update, deletion, addition, and index of evaluation rule information, and provide data for the operation of evaluation units. Evaluation rules include accuracy, consistency, completeness, standardization, timeliness, and accessibility.

[0296] The above-mentioned evaluation index library is used to store and manage evaluation index information, support the creation, query, update, deletion, addition and index of evaluation index information, and provide data for the operation of evaluation units. Evaluation indicators include content accuracy, precision accuracy, record duplication rate, dirty data occurrence rate, element assignment consistency rate, element filling rate, record filling rate, data item filling rate, value domain compliance rate, metadata compliance rate, format compliance rate, security compliance rate, cycle timeliness, real-time timeliness, and accessibility.

[0297] 3. Data operation module: The data operation module is the core processing module of the entire valuation system. It is responsible for receiving data assets, data information and data evaluation results from the data evaluation module, performing operations by running the preprocessing program and the data operation program, and finally calculating the valuation results of the data assets, and passing this information to the output module. It is used to receive and store data assets, data information and data evaluation results transmitted by the data evaluation module; based on the received data information, data assets and data evaluation results, call the preprocessing program to preprocess the preset data operation model parameter information, and simultaneously update the parameter information based on the preprocessing results; call the updated data operation model, generate a data application scenario solution based on the received data information, and synchronously run the data operation program based on the generated data application scenario solution to obtain the data asset valuation results; it is used to store the parameter information, algorithm model, case information and valuation information required to support the operation of the data asset valuation model; it is used to transmit data assets, data information, data evaluation results and data asset valuation results to the output module in the form of data packets.

[0298] 3.1 The data operation module includes a preprocessing unit, an operation unit, and an operation resource library. The receiving end of the data operation module establishes a communication connection with the output end of the data evaluation module to receive data information, data assets, and data evaluation results. The output end of the data operation module establishes a communication connection with the receiving end of the output module to transmit data assets, data information, data evaluation results, and data asset valuation results.

[0299] 3.2 The preprocessing unit described: The function of the preprocessing unit is to receive and store data assets, data information, and data evaluation results transmitted by the data evaluation module; to call the preprocessing program to perform preprocessing operations on the parameter information of the data operation model based on the received data information, data assets, and data evaluation results, and synchronously update the parameter information based on the preprocessing results; and to transmit data information, data assets, and data evaluation results to the operation unit.

[0300] (1) The receiving end of the preprocessing unit establishes a communication connection with the output end of the data evaluation module, and the output end of the preprocessing unit establishes a communication connection with the receiving end of the computing unit.

[0301] (2) The preprocessing unit includes an information receiving subunit, a parameter initialization subunit, a parameter verification subunit, a verification result feedback subunit, and a parameter optimization subunit.

[0302] Information receiving subunit: The information receiving subunit receives data assets, data information, data evaluation results, and obtains the information required for preprocessing and parameter configuration operation processing.

[0303] Parameter initialization subunit: Call the data asset valuation model to obtain the parameter information list required by the data asset valuation model. Traverse the parameter information and perform matching operations on the data information obtained by the information receiving subunit. Write the data information that meets the parameter information requirements into the corresponding parameters to complete the preliminary configuration and initialization of the parameter information.

[0304] Parameter verification subunit: accesses the valuation resource library to read the current valuation information, and compares and verifies the initialized parameter information with the valuation information stored in the valuation resource library.

[0305] Verification result feedback subunit: automatically determines whether the parameter information needs to be updated based on the parameter verification results. When the parameter verification results are completely consistent, the parameter information is kept as it is. When the parameter verification results are not completely consistent, the data synchronization mechanism is triggered to perform a data update and overwrite operation, and the valuation information in the valuation resource library is written into the parameter information to overwrite and correct the inconsistent parts.

[0306] Parameter optimization subunit: After confirming that the parameter information is correct, the parameter optimization subunit performs final optimization on the parameters and passes the verified parameter information as input to the data operation model to provide data support for subsequent valuation calculations.

[0307] 3.3 The operation unit described: The operation unit is the core component of the entire data operation module. The function of the operation unit is to receive and store the data assets, data information, and data evaluation results transmitted by the preprocessing unit; based on the received data information, access the case information of the valuation resource library, synchronously match the data information with the case information, and automatically generate a data application scenario plan according to the preset application scenario plan template based on the matching result; based on the generated data application scenario plan, call and run the data operation model to calculate the data asset valuation result; used to transmit the data assets, data information, data evaluation results, and data asset valuation results to the output module for users to query and use.

[0308] (1) The receiving end of the above-mentioned operation unit establishes a communication connection with the output end of the pre-processing unit, and the output end of the operation unit establishes a communication connection with the receiving end of the output module.

[0309] (2) The above-mentioned operation unit includes a data preprocessing subunit, an application scenario generation subunit, a benefit value operation subunit, an application scenario judgment subunit, a combination benefit operation subunit, an asset valuation operation subunit, and a valuation result output subunit.

[0310] Data preprocessing subunit: receives data assets, data information, and data evaluation results, performs preliminary data cleaning and verification operations on the received information, and obtains the information required for the data calculation model.

[0311] Application scenario generation subunit: Based on the received data information and data evaluation results, intelligently generate one or more data application scenario solutions. The above application scenario solutions include application scenario number, application scenario name, application scenario introduction, and industry content to which the application scenario belongs. Provide application scenario solution management and configuration interface, support modification, update and version control of application scenario solutions. The application scenario generation subunit includes resource library access component, solution initialization component, case information comparison component, and solution optimization generation component.

[0312] (1) Resource library access component: access the valuation resource library to obtain predefined application scenario solution templates.

[0313] (2) Solution initialization component: writes the received data information and data evaluation results into the application scenario solution template to generate the initial application scenario solution.

[0314] (3) Case information comparison component: synchronously accesses the case information library of the valuation resource library, and compares, matches, and verifies the cached application scenario solution information with the case information.

[0315] (4) Solution optimization generation component: Based on the comparison and verification results, automatically generate one or more application scenario solutions that conform to the data logic.

[0316] Profit value calculation subunit: Based on the generated application scenario solution, the profit value calculation model is called in parallel through multi-threading to calculate the profit value calculation result of each application scenario solution. The profit value calculation subunit includes application scenario selection component, parameter selection component, profit calculation component, profit correction component, and profit result component.

[0317] (1) Application scenario selection component: call the generated application scenario solution, obtain the application scenario data information for the benefit value calculation model, and write the application scenario data information into the benefit value calculation model.

[0318] (2) Parameter selection component: read the received application scenario information, extract the parameter information of the profit value calculation model, and synchronously compare the extracted model parameter information with the various parameter information of the valuation resource library, and use the preset model parameter selection rules to screen and select the final model parameters. Synchronously, write the final model parameters into the profit value calculation model. The above model parameters include profit forecast parameters and data asset profit period parameters.

[0319] When extracting revenue forecast parameters, access the revenue forecast parameter table of the valuation resource library, select one or more parameters in the revenue forecast parameter table, generate and write revenue forecast parameters, where the revenue forecast parameter table includes cost, revenue, cost profit margin, profit month-on-month growth rate, number of users, frequency of use, single-use price, revenue conversion rate, revenue generation method, and revenue generation channel parameters.

[0320] When extracting the data asset income period parameters, access the income period table of the valuation resource library, select one or more parameters in the income period table, generate and write the data asset income period parameters, where the income period table includes the operating information of the assessed unit, data asset authorization contract information, data asset rights restriction information, data asset utilization status information, data asset update frequency, and market demand change forecast information parameters.

[0321] (2) Profit calculation component: call and run the profit value calculation model, calculate and obtain the profit value calculation result, and synchronously cache the profit value calculation result and send it to the profit correction component. The profit value calculation formula is as follows:

[0322] R t =P i ×Q i

[0323] Where:

[0324] R t Indicates the calculation result of the benefit value under the selected application scenario;

[0325] P i Indicates that under the selected application scenario, a one-time fee is charged for data assets within the data asset income period, and the fee information comes from the received data information and the parameter information extracted by accessing the valuation resource library;

[0326] Q i It indicates the number of times fees are collected within the data asset income period under the selected application scenario. The number of times fees are collected comes from the received data information and the parameter information extracted by accessing the valuation resource library.

[0327] (3) Profit Correction Component: Reads the application scenario and data information, extracts the correction parameter information, and simultaneously compares the correction parameter information with the correction information in the valuation resource library. According to the comparison result, the profit value calculation result is corrected, and the profit value correction result is sent to the profit result component.

[0328] The above-mentioned correction parameter information is the weight correction parameter. When extracting the weight correction parameter, access the weight parameter table of the valuation resource library, select one or more parameters, generate and write the weight correction parameters, where the weight parameter table includes data quality, data capacity, data value density, market scarcity, market competitiveness, data set density, data governance capabilities, data update frequency, technology implementation capabilities, technology update speed, and data integration parameters.

[0329] (4) Profit result component: Receives the profit value correction result and writes it into the profit value calculation result, and simultaneously sends the application scenario plan, profit value calculation result, data information, data assets, and data evaluation result in the form of a data packet to the next step.

[0330] Application scenario judgment subunit: Count the received application scenario solutions and generate the number of application scenario solutions. Judge the number of generated application scenario solutions. When the judgment result is equal to 1, automatically generate the "Execute asset valuation operation instruction" and send the "Execute asset valuation operation instruction" to the "Asset valuation operation subunit"; when the judgment result is not equal to 1, automatically generate the "Execute combination income operation instruction" and send the "Execute combination income operation instruction" to the "Combination income operation subunit".

[0331] Combination income calculation subunit: When receiving the "execution combination income calculation instruction", the combination income calculation start device is triggered, the combination income value model is called, and the combination income value is calculated. The combination income value is written into the income value calculation result, and the "execution asset valuation calculation instruction" is generated, and the "execution asset valuation calculation instruction" is sent to the "asset valuation calculation subunit". Synchronously, the application scenario plan, income value calculation result, data information, data assets, and data evaluation results are sent to the "asset valuation calculation subunit" in the form of a data packet. Provide a model management and configuration interface to support the modification, update and version control of the combination income calculation model.

[0332] The formula for calculating the combined return value is as follows:

[0333]

[0334] Where:

[0335] F t Represents the combined return value;

[0336] R n Indicates the profit value calculation result obtained under the nth application scenario;

[0337] ω i It represents the application scenario realization parameter. When obtaining the application scenario realization parameter, access the organizational capability table of the valuation resource library, select one or more parameters in the organizational capability table, generate and write the application scenario realization parameter, where the organizational capability table includes investment amount, investment period, investment payback period, business needs, market prospects, technical capabilities, management capabilities, security capabilities, risk assessment, application risks, market feedback information, and iterative optimization information parameters.

[0338] Represents the mean value of the application scenario realization parameter.

[0339] i represents the number of parameter information of realization degree of the selected application scenario.

[0340] n represents the number of application scenario solutions generated after the application scenario judgment subunit runs.

[0341] ROI n Represents the investment rate of return, where the calculation formula is: Where T n Indicates the total investment cost required to develop the nth application scenario. The data comes from the data information input by the user.

[0342] Represents the mean return on investment.

[0343] Asset valuation operation subunit: When receiving the "execute asset valuation operation instruction", the data asset valuation operation start device is triggered, the data asset valuation model is called, and the data asset valuation is calculated. Synchronously, the combined income value is written into the income value calculation result, and the application scenario plan, data assets, data information, data evaluation results, and data asset valuation results are sent to the valuation result output subunit in the form of a data packet. Provide a model management and configuration interface to support the modification, update, and version control of the asset valuation operation model.

[0344] The calculation formula for group data asset valuation is as follows:

[0345]

[0346] Where: P represents the final valuation of the data asset;

[0347] F t Represents the combined return value;

[0348] Adj t represents the adjusted parameter factor;

[0349] R f It represents the risk-free rate of return of data asset application. When obtaining the risk-free rate of return, the investment risk table of the valuation resource library is accessed, one or more parameters in the investment risk table are selected, and the risk-free rate of return is generated and written. The investment risk table includes China's short-term and medium-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators (for the entire industry), profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, safety risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters.

[0350] risk i Represents the risk indicator of data asset application. When obtaining the risk indicator, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write the risk indicator, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators (for the entire industry), profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, security risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters.

[0351] Represents the mean value of data asset application risk indicators.

[0352] n represents the remaining income period of the data asset, which comes from the data information input by the user.

[0353] i represents the number of risk indicators applied to the selected data assets.

[0354] t indicates the age information selected during data asset evaluation.

[0355] Valuation result output subunit: Receives and stores the results of all calculation processes, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results. Arranges the received data packets according to the preset format and sends the data packets to the output module. The valuation result output subunit provides data asset valuation result query and report generation functions, so that users can easily view and analyze data asset valuation results.

[0356] 4.4 Valuation resource library: The valuation resource library is the core component of the data operation module. Its main function is to store and manage case information and valuation information, and provide comprehensive management support for these information. The management modes include creation, query, update, deletion, addition, and indexing.

[0357] 4.4.1 The valuation resource library includes a resource library reading component, a resource library processing component, and a resource library application component.

[0358] Resource library reading component: The preprocessing unit and operation unit of the data operation module access the valuation resource library through the interface as needed, read the required evaluation rules, evaluation indicators, and valuation parameter information, and store them in the local system memory for subsequent use.

[0359] Resource library processing component: parses, converts and initializes the read resource library information locally to adapt to specific application scenarios.

[0360] Resource library application component: Based on the read parameter information and algorithm model, the initialized, updated, and optimized parameter information is written into the valuation model to support the valuation model's return value calculation and data asset valuation calculation, and ultimately generate data asset valuation results.

[0361] 4.4.2 The valuation resource library includes the case information library and the valuation information library.

[0362] The above-mentioned case information database is used to store solutions for data applications in various industries; store various data market information, industry research, competition information, historical transaction information, industry research information, and policy risk information; store entity tables required for generating application scenario solutions; and temporarily store generated application scenario solutions. It supports the creation, query, update, deletion, addition, and indexing of the case information database, and is used to provide data for the calculation of the preprocessing unit.

[0363] The valuation information library is used to store the valuation information required by the computing unit during the execution of the computing program; temporarily store the data information, data assets, and data evaluation results received by the computing unit; temporarily store the data information obtained during the computing unit's execution of the computing program; and permanently store the valuation results of the data assets obtained. The valuation information library supports creation, query, update, deletion, addition, and indexing, and is used to provide data for the computing unit's operations.

[0364] The above valuation information database includes the income forecast parameter table, income years table, weight parameter table, organizational capability table, and investment risk table. The income forecast parameter table includes cost, income, cost profit margin, profit month-on-month growth rate, number of users, frequency of use, single-use price, income conversion rate, income generation method, and income generation channel parameters; the income years table includes the operating information of the assessed unit, data asset authorization contract information, data asset rights restriction information, data asset utilization status information, data asset update frequency, and market demand change forecast information; the weight parameter table includes data quality, data capacity, data value density, market scarcity, market competitiveness, data set density, data governance capabilities, data update frequency, technology implementation capabilities, technology update speed, data According to the integration parameters; the organizational capability table includes investment amount, investment period, investment payback period, business needs, market prospects, technical capabilities, management capabilities, security capabilities, risk assessment, application risk, market feedback information, and iterative optimization information parameters; the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators (for the entire industry), profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, safety risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters.

[0365] 4. Output module: The output module is the terminal of the data asset valuation system. The main function of the output module is to receive data packets transmitted by the data operation module, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; support users to select applicable evaluation result templates from the template library according to their needs, and simultaneously write the data asset valuation results to the evaluation result template; use it to format and display the evaluation results according to user needs, and support users to export them in text format.

[0366] 4.1 The output module includes a parsing unit, a selection unit, and an export unit. The receiving end of the output module is connected to the output end signal of the data operation module.

[0367] 4.2 The parsing unit described: The parsing unit is used to receive data packets from the data operation module, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; parse the received data packets, extract key information, store the extracted key information and transmit it to the selection unit.

[0368] The parsing unit includes a data receiving subunit, a data verification subunit, a data parsing subunit, and a status feedback subunit.

[0369] Data receiving subunit: monitors the output port of the data operation module and receives data packets.

[0370] Data verification subunit: Verifies the format and integrity of received data packets to ensure data is correct.

[0371] Data parsing subunit: parses each part of the data packet (application scenario plan, data assets, data information, data evaluation results, data asset valuation results) into a data structure that can be recognized within the module.

[0372] Status feedback subunit: If data verification fails, error feedback is sent to the data operation module; after verification succeeds, subsequent processing continues.

[0373] 4.3 The selection unit described in claim 4: The selection unit is used to support the user to select one or more applicable templates from a preset evaluation result template library, and simultaneously fill the data information processed by the analysis unit into the selected template.

[0374] The selection unit includes a template display subunit, a template selection subunit, a data filling subunit, and a result preview subunit.

[0375] Template display subunit: displays all available evaluation result templates in the template library to the user.

[0376] Template selection subunit: receives the user's template selection operation and records the identifier of the selected template.

[0377] Data filling subunit: According to the structure of the selected template, the data information parsed by the parsing unit is automatically filled into the corresponding position of the template.

[0378] Result Preview subunit: Provides a preview function to allow users to view the filled assessment result template before formal export.

[0379] 4.3 The export unit described in claim 4: The export unit is used to support users to view the evaluation results online and provide the function of exporting the evaluation results in text format.

[0380] The export unit includes an online viewing subunit, a result export subunit, a format conversion subunit, a generation and preservation subunit, and an export feedback subunit;

[0381] Online View Subunit: Provides a user interface that enables users to view the populated assessment results template online.

[0382] Result export subunit: receives the user's export request and prepares according to the request parameters, including the export format and whether to include all information.

[0383] Format conversion subunit: Convert the assessment result template into the specified text format to ensure the accuracy of the information.

[0384] Generate and Save subunit: Generates the exported file and provides the user with the option to download or save it to a specified location.

[0385] Export feedback subunit: Feedback the results of the export operation to the user, including whether it is successful and file path information.

[0386] 5. Log module: The log module is mainly responsible for recording the operation information, error information, warning information and key data flow information of each module and each stage in the system. Through detailed log records, it is convenient to conduct problem troubleshooting, performance analysis and auditing.

[0387] 5.1 Main functions of the log module

[0388] Information logging: records all key operations, data processing, and error information in the system.

[0389] Log classification: classify and manage logs according to different modules and levels (such as INFO, DEBUG, WARN, ERROR).

[0390] Log query: Provides a log query interface to facilitate users to query logs based on time range, module, and log level conditions.

[0391] Log storage: Store log information in a suitable storage medium, such as a file system, database, or remote log service.

[0392] Performance monitoring: Monitor and record the performance data of each module of the system, such as processing time and throughput.

[0393] 5.2 Integration of log module in the system

[0394] (1) Input module

[0395] Log record: In the input module, record the source, time, and data volume of the received data information, data assets, and basic enterprise information.

[0396] Error handling: If the input data does not meet the requirements, record the error information and mark the status of data processing.

[0397] (2) Judgment unit in the data evaluation module: records the results of each step in the judgment process, including preliminary judgment results and data verification results. Evaluation unit: records the construction process of the evaluation model, the application of evaluation rules, and the calculation results of evaluation indicators. Evaluation resource library: records the creation, update, deletion, and access operations of the evaluation rule library and the evaluation indicator library.

[0398] (3) Preprocessing unit in the data operation module: records the initial assignment process of parameter information, the results of data comparison and matching, and data update operations. Operation unit: records the execution results and calculation details of the key steps of the return value calculation, application scenario solution generation, and data asset valuation calculation. Valuation information database: records the update and maintenance operations of the valuation information database.

[0399] (4) Parsing unit in the output module: records the data packet reception, parsing process, and data verification results. Selection unit: records the evaluation result template selected by the user, the data filling process and results. Export unit: records the export time, export format, and save path information of the evaluation report.

[0400] 5.3 Log level setting

[0401] (1) INFO: records basic information during normal system operation, such as data reception and model building.

[0402] (2) DEBUG: Records detailed debugging information for developers to troubleshoot problems.

[0403] (3)WARN: Records warning information that may affect the normal operation of the system but has not yet caused errors.

[0404] (4) ERROR: Records detailed error information when a system error occurs, including error time, error type, and error description.

[0405] 5.4 Log Storage and Query

[0406] Storage: Log information is stored in the local file system, database, or remote log service.

[0407] Query: Provides a log query interface, users can query logs according to time range, module name, and log level conditions to quickly locate problems.

[0408] The system provided by the implementation case of the present invention is a data asset valuation system that integrates data quality evaluation. The method of the present invention can improve the comprehensiveness and accuracy of data asset valuation results, establish a scientific and flexible valuation model, adapt to different data asset characteristics and evaluation needs, accurately identify application scenarios of data assets, and solve the problem that the value of data assets deviates from the actual application value. It realizes data asset valuation by information technology, ensures that the results are fair and accurate, and effectively avoids the impact of manual adjustments.

[0409] Although the embodiments of the present invention have been described by way of example, those skilled in the art will appreciate that there are many modifications and variations of the present invention without departing from the spirit of the present invention. It is intended that the appended claims include these modifications and variations without departing from the spirit of the present invention.

Claims

1. A data asset valuation system integrating data quality evaluation, characterized in that: The system includes: input module, data evaluation module, data operation module, output module, and log module; the input module collects data information from multiple data sources, supports users to directly input data assets and related information, and automatically accesses and integrates data resources in external databases; Input module: used to input and store data information, including basic information of data assets, basic information of enterprises, and information on business needs for evaluation; receive and store data assets transmitted from external databases; transmit the acquired data information and data assets to the data evaluation module in the form of data packets; Data evaluation module: After receiving the data assets and data information provided by the input module, it starts working and conducts multi-dimensional and in-depth analysis and evaluation of the quality of the data assets. After the evaluation is completed, the data evaluation module passes the results together with the original data assets and data information to the data operation module; Data operation module: performs necessary cleaning, conversion and formatting of data to ensure that the data is suitable for subsequent valuation calculations. The data operation module accesses the valuation resource library to obtain the parameter information and algorithm model required to build the data operation model, builds a valuation model suitable for the current data assets, and calculates the specific valuation results of the data assets by calling the execution program of the valuation model. The data operation module passes the complete evaluation information to the output module; Output module: presents the evaluation results in a user-friendly way. Users can select a suitable evaluation result template according to their needs, organize and fill in various information received from the data operation module according to the template format, and export the evaluation report in text format for users to review, share, and use as a basis for decision-making; Log module: records the operation information, error information, warning information and key data flow information of each module and each stage in the system, and conducts problem troubleshooting, performance analysis and auditing through detailed log records.

2. A data asset valuation system integrating data quality evaluation according to claim 1, characterized in that: The input module includes an input unit and an acquisition unit; wherein: Input unit: receives and stores data assets, establishes a communication connection between the receiving end of the input unit and the output end of the external database, and accesses the external database to obtain data assets; the output end of the input unit establishes a communication connection with the receiving end of the acquisition unit to send data assets to the acquisition unit; Acquisition unit: acquires and stores data information, including basic information of data assets, basic information of the enterprise, and information on business needs, to support the operation of data evaluation modules and data calculation modules.

3. A data asset valuation system integrating data quality evaluation according to claim 1, characterized in that: The data evaluation module includes a determination unit, an evaluation unit, and an evaluation resource library, wherein: Determination unit: receives data information and data assets from the input module, is used to receive data information and data assets transmitted by the input module, executes the determination program to obtain the determination result and sends the determination result to the evaluation unit and the data operation module, and sends the received data information and data assets to the evaluation unit and the data operation module; Evaluation unit: according to the judgment result sent by the judgment unit, start the corresponding data evaluation program, after receiving the execution instruction of the data evaluation task, synchronously receive and store data information and data assets, use data assets as the data source for running the data evaluation program, access the established rule library and indicator library in parallel, run the data evaluation program to calculate and obtain the data evaluation result, and send the data information, data assets and data evaluation result to the data calculation module in the form of data packets; Evaluation resource library: the standards, models, algorithms, and rules required for data evaluation, used to store evaluation rule information and evaluation indicator information; used to manage evaluation rule information and evaluation indicator information, where the management modes can be create, query, update, delete, add, and index.

4. A data asset valuation system integrating data quality evaluation according to claim 3, characterized in that: The determination unit includes a receiving subunit, a pre-determination subunit, a decision distribution subunit, a report verification subunit, a final determination execution subunit, and a result subunit; wherein, Receiving subunit: Receives and stores data information and data assets transmitted by the input module, and triggers the operation signal of the pre-determination subunit upon receiving the data information; Pre-determination subunit: reads and analyzes the data information according to the data information and data assets stored in the receiving subunit, and synchronously calls the preset pre-determination program to determine whether the data information includes a professional work report; when it is determined that the data information includes a professional work report, generates a preliminary determination result that includes the professional work report; when it is determined that the data information does not include a professional report, generates a preliminary determination result that does not include the professional work report; Decision distribution subunit: Based on the output results of the pre-determination subunit, the decision distribution subunit performs further processing. When receiving a preliminary determination result containing a professional work report, it generates a start verification instruction, and simultaneously sends the identified professional work report to the report verification subunit for subsequent processing; when receiving a preliminary determination result that does not contain a professional work report, it generates an execution data evaluation task instruction, and simultaneously sends the execution data evaluation task instruction and the received data information and data assets to the evaluation unit; Report verification subunit: After receiving the start verification instruction and the data information of the professional work report, the report verification subunit starts the data verification program, calls the data verification program to perform document parsing, keyword matching, semantic analysis, pattern recognition, and information extraction operations on the professional work report to determine whether the professional work report contains quality evaluation information; when the professional work report does not contain quality evaluation information, a result without quality evaluation information is generated and displayed visually; Final judgment execution subunit: according to the output result of the report verification subunit, the verification result is subjected to final judgment operation processing. When the final judgment result contains quality evaluation information, an instruction to abandon the data evaluation task is generated, and the instruction to abandon the data evaluation is sent to the evaluation unit to interrupt the operation of the data evaluation program, write the extracted quality evaluation information into the data evaluation result template, and send the data information, data assets, and data evaluation results to the data operation module in the form of a data packet, and visualize the data evaluation results; when the final judgment result does not contain quality evaluation information, an instruction to execute the data evaluation task is generated, and the instruction to execute the data evaluation task is sent to the evaluation unit to start the execution of the data evaluation program, and the data information and data assets are sent to the evaluation unit in the form of a data packet; Result subunit: The result subunit is used to store the processing results generated by the pre-determination subunit, the pre-determination subunit, the verification subunit, and the final judgment execution unit, and supports the query, analysis, call, editing, and visual display of processing results.

5. The data asset valuation system integrating data quality evaluation according to claim 3 is characterized by: The evaluation unit includes an instruction receiving subunit, a data preparation subunit, a model building and configuration subunit, a rule matching and automatic updating subunit, an indicator matching and adaptive adjustment subunit, and an evaluation output subunit; wherein: Instruction receiving subunit: receiving the data evaluation task execution instruction sent by the determination unit, triggering the start of the data evaluation program, triggering the data evaluation program start device when receiving the data evaluation task execution instruction, triggering the data evaluation program interrupt device when receiving the data evaluation task abandonment instruction, and recording the instruction reception time and start time for subsequent tracking and auditing; Data preparation subunit: When the data evaluation program is started, it receives and stores the data information and data assets transmitted by the determination unit, prepares information for the data evaluation model, and provides data query and retrieval interfaces so that the data evaluation module can quickly access the required data; Model building and configuration subunit: builds data evaluation models based on the received data information, provides model management and configuration interface, and supports modification, update and version control of data evaluation models; Rule matching and automatic update subunit: calls and reads the rule information in the evaluation resource library, matches it with the rule layer of the data evaluation model, and automatically updates the rule information in the data evaluation model according to the matching results. It provides rule update log and version control functions to track the rule change history and restore the old version; Indicator matching and adaptive adjustment subunit: calls and reads indicator information in the evaluation resource library, matches it with the indicator layer of the data evaluation model, and adaptively adjusts the indicator information in the data evaluation model according to the matching results; supports dynamic addition, deletion and weight adjustment of indicators to adapt to different evaluation scenarios and needs; Evaluation output subunit: Receives data information and data assets from the input module, and passes the data information and data assets as input sources to the data evaluation model; runs the evaluation layer running program of the data evaluation model to perform mathematical operations on the input data. Writes the data evaluation model operation results into the data evaluation result template, simultaneously visualizes the data evaluation results, and sends the data information, data assets, and data evaluation results to the data operation module in the form of data packets; the evaluation output subunit provides data evaluation result query and report generation functions, so that users can easily view and analyze data evaluation results; The mathematical formula of the evaluation layer of the above data evaluation model is: Where AQ represents the result of data quality evaluation operation; A represents the number of data elements in the data asset that meet the requirements of the data evaluation index. The data acquisition method is to iteratively process each data element in the data asset according to the index layer information in the data evaluation model, perform index calculation on each data element, and simultaneously determine whether each data source element meets the requirements of the determined index layer information, and record the number of data that meet the conditions; B represents the total number of data elements in the data asset. The data is obtained by traversing the data asset, calculating each data element of the data asset, and accumulating the counting results to obtain the total number of data elements in the data asset; α ij Represents the quality evaluation weight parameter. The data comes from the quality evaluation weight parameter set in the data evaluation database. β i It represents the weight parameter of each evaluation rule. The data is obtained by accessing the evaluation resource library and data information; m represents the number of rule information in the rule layer of the data evaluation model; n represents the number of indicator information in the indicator layer of the data evaluation model.

6. A data asset valuation system integrating data quality evaluation according to claim 5, characterized in that: The model building and configuration subunit includes a model matching component, a rule layer configuration component, and an indicator layer configuration component, wherein: Model matching component: based on the received data information, automatically matches the data information with the preset data evaluation model and selects the matching data evaluation model. The data evaluation model includes the rule layer, the indicator layer, and the evaluation layer. Rule layer configuration component: Based on the selected data evaluation model, it automatically generates a rule layer rule information list and writes the read rule information into the data evaluation model; Indicator layer configuration component: Based on the selected data evaluation model, it automatically generates an indicator layer indicator information list and writes the read indicator information into the data evaluation model.

7. The data asset valuation system integrating data quality evaluation according to claim 5 is characterized by: The rule matching and automatic updating subunit includes a rule reading component, a rule matching component, and a rule updating component, wherein: Rule reading component: calls the rule information list of the rule layer, and synchronously accesses the evaluation resource library to read the evaluation rule information; the evaluation rule information includes accuracy, consistency, completeness, standardization, timeliness, and accessibility; Rule matching component: Verify and match the read evaluation rule information with the rule layer information of the data evaluation model; Rule update component: Updates the rule layer information of the data evaluation model based on the matching results; when the matching results are completely consistent, the rule layer information in the data evaluation model is kept original; when the matching results are not completely consistent, the data update overwrite operation is triggered to write the read evaluation rule information into the rule layer of the data evaluation model to overwrite and correct the inconsistent parts.

8. The data asset valuation system integrating data quality evaluation according to claim 5 is characterized by: The indicator matching and adaptive adjustment subunit includes an indicator reading component, an indicator matching component, and an indicator adaptive adjustment component, wherein: Indicator reading component: according to the indicator layer information of the data evaluation model determined by the indicator matching and automatic update sub-unit, call the indicator layer indicator information list, and synchronously access the evaluation resource library to read the evaluation indicator information; the evaluation indicator information includes content accuracy, precision accuracy, record duplication rate, dirty data occurrence rate, element assignment consistency rate, element filling rate, record filling rate, data item filling rate, value domain compliance rate, metadata compliance rate, format compliance rate, security compliance rate, cycle timeliness, real-time timeliness, and accessibility; Indicator matching component: Verify, extract, and match the read evaluation indicator information with the indicator layer information of the data evaluation model to check whether the indicators are consistent; Indicator adaptive adjustment component: based on the matching results and user needs, update and adjust the indicator layer information of the data evaluation model; when the matching results are completely consistent, keep the indicator layer information in the data evaluation model original; when the matching results are not completely consistent, trigger the data update overwrite operation, write the read evaluation indicator information into the indicator layer of the data evaluation model, and overwrite and correct the inconsistent parts.

9. The data asset valuation system integrating data quality evaluation according to claim 1 is characterized by: The data operation module includes a preprocessing unit, an operation unit, and an operation resource library; wherein: Preprocessing unit: receiving and storing data assets, data information, and data evaluation results transmitted by the data evaluation module; calling the preprocessing program to perform preprocessing operations on the parameter information of the data operation model according to the received data information, data assets, and data evaluation results, and updating the parameter information according to the preprocessing results; transmitting data information, data assets, and data evaluation results to the operation unit; Operation unit: receives and stores data assets, data information, and data evaluation results transmitted by the preprocessing unit; accesses case information in the valuation resource library based on the received data information, synchronously matches the data information with the case information, and automatically generates a data application scenario solution according to the preset application scenario solution template based on the matching result; based on the generated data application scenario solution, calls and runs the data operation model to obtain the data asset valuation result; is used to transmit data assets, data information, data evaluation results, and data asset valuation results to the output module for users to query and use; Operational resource library: Stores and manages case information and valuation information, and provides comprehensive management support for this information, where the management modes include creation, query, update, deletion, addition, and indexing.

10. The data asset valuation system integrating data quality evaluation according to claim 1, characterized in that: The output module includes a parsing unit, a selection unit, and an exporting unit; wherein: Parsing unit: receives data packets from the data operation module, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; parses the received data packets, extracts key information, stores the extracted key information, and transmits it to the selection unit; Selection unit: supports users to select one or more applicable templates from the preset evaluation result template library, and fills the data information processed by the analysis unit into the selected template; Export unit: supports users to view the evaluation results online and provides the function of exporting the evaluation results in text format.

11. The data asset valuation system integrating data quality evaluation according to claim 9, characterized in that: The preprocessing unit includes an information receiving subunit, a parameter initialization subunit, a parameter verification subunit, a verification result feedback subunit, and a parameter optimization subunit; wherein: Information receiving subunit: receiving data assets, data information, data evaluation results, and obtaining information required for preprocessing and parameter configuration operations; Parameter initialization subunit: calls the data asset valuation model, obtains the parameter information list required by the data asset valuation model, traverses the parameter information, performs comparison and matching operations on the data information obtained by the information receiving subunit, writes the data information that meets the parameter information requirements into the corresponding parameters, and completes the preliminary configuration and initialization of the parameter information; Parameter verification subunit: accesses the valuation resource library to read the current valuation information, and compares and verifies the initialized parameter information with the valuation information stored in the valuation resource library; Verification result feedback subunit: automatically determines whether the parameter information needs to be updated based on the parameter verification results. When the parameter verification results are completely consistent, the parameter information is kept as it is; when the parameter verification results are not completely consistent, the data synchronization mechanism is triggered to perform data update and overwrite operations, and the valuation information in the valuation resource library is written into the parameter information to overwrite and correct the inconsistent parts; Parameter optimization subunit: After confirming that the parameter information is correct, the parameter optimization subunit performs final optimization on the parameters and passes the verified parameter information as input to the data operation model to provide data support for subsequent valuation calculations.

12. The data asset valuation system integrating data quality evaluation according to claim 9, characterized in that: The operation unit includes a data preprocessing subunit, an application scenario generation subunit, a benefit value operation subunit, an application scenario judgment subunit, a combined benefit operation subunit, an asset valuation operation subunit, and a valuation result output subunit; wherein: Data preprocessing subunit: receives data assets, data information, and data evaluation results, performs preliminary data cleaning and verification operations on the received information, and obtains the information required for the data calculation model; Application scenario generation subunit: based on the received data information and data evaluation results, intelligently generate one or more data application scenario solutions, the application scenario solution includes the application scenario number, application scenario name, application scenario introduction, and the industry content to which the application scenario belongs, provides an application scenario solution management and configuration interface, and supports the modification, update and version control of the application scenario solution. The application scenario generation subunit includes a resource library access component, a solution initialization component, a case information comparison component, and a solution optimization generation component; Among them, the resource library access component: accesses the valuation resource library to obtain predefined application scenario solution templates; Solution initialization component: writes the received data information and data evaluation results into the application scenario solution template to generate the initial application scenario solution; Case information comparison component: synchronously accesses the case information library of the valuation resource library, and compares, matches, and verifies the cached application scenario solution information with the case information; Solution optimization generation component: automatically generates one or more application scenario solutions that conform to data logic based on the comparison and verification results; The benefit value calculation subunit: according to the generated application scenario solution, the benefit value calculation model is called in parallel through multi-threading to calculate the benefit value calculation result of each application scenario solution; the benefit value calculation subunit includes an application scenario selection component, a parameter selection component, a benefit calculation component, a benefit correction component, and a benefit result component; Application scenario selection component: calls the generated application scenario solution, obtains the application scenario data information for the benefit value calculation model, and writes the application scenario data information into the benefit value calculation model; Parameter selection component: reads the received application scenario information and extracts the parameter information of the profit value calculation model; compares the extracted model parameter information with the various parameter information of the valuation resource library, uses the preset model parameter selection rules to screen and select the final model parameters, and writes the final model parameters into the profit value calculation model; the above model parameters include profit forecast parameters and data asset profit period parameters; When extracting revenue forecast parameters, access the revenue forecast parameter table of the valuation resource library, select one or more parameters in the revenue forecast parameter table, generate and write revenue forecast parameters, where the revenue forecast parameter table includes cost, revenue, cost profit margin, profit month-on-month growth rate, number of users, usage frequency, single-use price, revenue conversion rate, revenue generation method, and revenue generation channel parameters; When extracting the data asset income period parameters, access the income period table of the valuation resource library, select one or more parameters in the income period table, generate and write the data asset income period parameters, where the income period table includes the business information of the assessed unit, data asset authorization contract information, data asset rights restriction information, data asset utilization status information, data asset update frequency, and market demand change forecast information parameters; Profit calculation component: call and run the profit value calculation model, calculate the profit value calculation result, and synchronously cache the profit value calculation result and send it to the profit correction component; the profit value calculation formula is as follows: R t =P i ×Q i Where: R t Indicates the calculation result of the benefit value under the selected application scenario; P i Indicates that under the selected application scenario, a one-time fee is charged for data assets within the data asset income period, and the fee information comes from the received data information and the parameter information extracted by accessing the valuation resource library; Q i Indicates the number of times fees are collected within the data asset income period under the selected application scenario. The number of times fees are collected is derived from the received data information and the parameter information extracted by accessing the valuation resource library. Profit correction component: reads the application scenario and data information, extracts the correction parameter information, and synchronously compares the correction parameter information with the various correction information in the valuation resource library. According to the comparison result, the profit value calculation result is corrected, and the profit value correction result is obtained by calculation, and the profit value correction result is sent to the profit result component; The above correction parameter information is the weight correction parameter. When extracting the weight correction parameter, access the weight parameter table of the valuation resource library, select one or more parameters, generate and write the weight correction parameter, where the weight parameter table includes data quality, data capacity, data value density, market scarcity, market competitiveness, data set density, data governance capabilities, data update frequency, technology implementation capabilities, technology update speed, and data integration parameters; Profit result component: receives the profit value correction result and writes it into the profit value calculation result, and simultaneously sends the application scenario plan, profit value calculation result, data information, data assets, and data evaluation results in the form of data packets; Application scenario judgment subunit: count the received application scenario solutions and generate the number of application scenario solutions, judge the number of generated application scenario solutions, and when the judgment result is equal to 1, automatically generate an instruction to execute asset valuation operation, and send the instruction to execute asset valuation operation to the asset valuation operation subunit; when the judgment result is not equal to 1, automatically generate an instruction to execute combination income operation, and send the instruction to execute combination income operation to the combination income operation subunit; Combination income calculation subunit: when receiving the instruction to execute combination income calculation, it triggers the combination income calculation starting device, calls the combination income value model, and calculates the combination income value; writes the combination income value into the income value calculation result, generates the instruction to execute the asset valuation calculation, and sends the instruction to execute the asset valuation calculation to the asset valuation calculation subunit, and sends the application scenario plan, income value calculation result, data information, data assets, and data evaluation result to the asset valuation calculation subunit in the form of a data packet; provides a model management and configuration interface, and supports the modification, update and version control of the combination income calculation model; The formula for calculating the combined return value is as follows: Where: F t Represents the combined return value; R n Indicates the profit value calculation result obtained under the nth application scenario; ω i Indicates the application scenario realization degree parameter. When obtaining the application scenario realization degree parameter, access the organizational capability table of the valuation resource library, select one or more parameters in the organizational capability table, generate and write the application scenario realization degree parameter, where the organizational capability table includes investment amount, investment period, investment payback period, business needs, market prospects, technical capabilities, management capabilities, security capabilities, risk assessment, application risks, market feedback information, and iterative optimization information parameters; Represents the mean value of the application scenario realization parameter; i represents the number of parameter information of realization degree of selected application scenarios; n represents the number of application scenario solutions generated after the application scenario judgment subunit runs; ROI n Represents the investment rate of return, where the calculation formula is: Where T n Indicates the total investment cost required to develop the nth application scenario. The data comes from the data information input by the user. represents the mean of investment return rate; Asset valuation operation subunit: When receiving the instruction to execute asset valuation operation, it triggers the data asset valuation operation start device, calls the data asset valuation model, calculates the data asset valuation, writes the combined income value into the income value calculation result, and sends the application scenario plan, data assets, data information, data evaluation results, and data asset valuation results in the form of a data packet to the valuation result output subunit; provides a model management and configuration interface, and supports the modification, update and version control of the asset valuation operation model; The calculation formula for group data asset valuation is as follows: Where: P represents the final valuation of data assets; F t Represents the combined return value; Adj t represents the adjusted parameter factor; R f Indicates the risk-free rate of return of data asset applications. When obtaining the risk-free rate of return, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write the risk-free rate of return, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators, profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, safety risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters; risk i Represents the risk indicators of data asset applications. When obtaining risk indicators, access the investment risk table of the valuation resource library, select one or more parameters in the investment risk table, generate and write risk indicators, where the investment risk table includes China's short-term and medium- and long-term treasury bond yields, benchmark interest rates, inflation rates, corporate performance evaluation indicators, profit return indicators, risk prevention and control indicators, sustainable development indicators, asset operation indicators, realization risk rate, operating risk rate, security risk rate, industry risk rate, financial risk rate, policy risk rate, legal risk rate, technical risk rate, and risk tolerance parameters; Represents the mean value of data asset application risk indicators; n represents the remaining income period of data assets, which is derived from the data information input by users; i represents the number of risk indicators selected for data asset application; t indicates the age information is selected during data asset evaluation; Valuation result output subunit: Receive and store the results of all calculation processes, including application scenario solutions, data assets, data information, data evaluation results, and data asset valuation results; organize the received data packets according to the preset format and send the data packets to the output module. The valuation result output subunit provides data asset valuation result query and report generation functions, so that users can easily view and analyze data asset valuation results.

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