Data asset value evaluation method, system, product, equipment and storage medium

Abstract

The invention discloses a data asset value evaluation method and system, a product, equipment and a storage medium, an evaluation method is intelligently recommended through a pre-training classification model, differential analysis is performed on structured and unstructured files to accurately extract key information, then value calculation is automatically completed based on a selected method, and the evaluation efficiency is improved. And finally, a standardized structured evaluation report is generated. A traditional manual evaluation process consuming several weeks is compressed to a minute level, the efficiency bottleneck of large-scale evaluation is solved, artificial experience judgment is replaced by AI driving, the problems of low efficiency, large subjective error and nonstandard report in the existing evaluation process are solved, full-process automation of data asset value evaluation is realized, the evaluation efficiency and accuracy are improved, and the evaluation efficiency and accuracy are improved. Meanwhile, a structured standard evaluation report is output, and reliable support is provided for data asset transaction and decision making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, in particular to a data asset value evaluation method, system, product, device and storage medium. BACKGROUND

[0002] With the in-depth development of digital economy, data has become the core strategic asset of enterprises. Accurate and efficient evaluation of its value is a key link to promote the marketization of data elements and support enterprise asset inventory and decision-making.

[0003] Currently, data asset value evaluation work mainly relies on manual completion. Professional personnel need to manually complete the selection of evaluation methods, review and analysis of financial statements and contract files and other materials, value calculation based on empirical formula, and finally write evaluation reports. This highly dependent manual operation mode has obvious drawbacks: first, low efficiency, it takes several days or even weeks to process a single evaluation, which is difficult to meet the batch evaluation needs of large-scale assets; second, strong subjectivity and large errors, from method selection to key information extraction are easily affected by personal experience, resulting in poor consistency and insufficient reliability of evaluation results; third, low standardization of reports, the reports written by artificial are uneven in structure and content integrity, which is not conducive to subsequent transaction, audit or decision-making application. SUMMARY

[0004] Based on the above problems, the present application provides a data asset value evaluation method, system, product, device and storage medium.

[0005] The present application embodiment discloses the following technical scheme:

[0006] The first aspect of the present application embodiment provides a data asset value evaluation method, comprising:

[0007] Obtaining the basic information of the data asset, determining the value evaluation method corresponding to the basic information through the pre-trained classification model and / or user selection instruction;

[0008] Obtaining the file materials related to the data asset uploaded by the user, extracting the key information for value calculation in the file materials;

[0009] Based on the value evaluation method, the basic information of the data asset and the key information, calculating the value of the data asset;

[0010] Based on the value evaluation method, the basic information of the data asset, the key information and the calculated value of the data asset, generating a value evaluation report.

[0011] In a possible implementation, the calculating the value of the data asset based on the value evaluation method, the basic information of the data asset, and the key information comprises:

[0012] When the value evaluation method is the cost method, the data asset usage life, the data collection cost, the data storage cost, and the data operation and maintenance cost are extracted from the basic information of the data asset and / or the file materials;

[0013] The depreciation amount is calculated by using the straight-line depreciation method based on the data asset usage life;

[0014] The sum of the data collection cost, the data storage cost, and the data operation and maintenance cost is subtracted from the depreciation amount to obtain the value of the data asset.

[0015] In a possible implementation, the calculating the value of the data asset based on the value evaluation method, the basic information of the data asset, and the key information comprises:

[0016] When the value evaluation method is the income method, the historical income data and the contractually agreed income proportion are obtained from the extracted key information;

[0017] The expected income in the next N years is calculated based on the historical income data and the income proportion, where N is a positive integer;

[0018] The sum of the present values of the expected income in the next N years is calculated as the value of the data asset based on a preset industry average discount rate.

[0019] In a possible implementation, the extracting the key information for value calculation from the file materials comprises:

[0020] For a structured file in the file materials, at least one key indicator of the data collection cost, the storage cost, the operation and maintenance cost, and the historical income data is extracted by reading a cell or a data structure in the structured file;

[0021] For an unstructured file in the file materials, the unstructured file is converted into text by optical character recognition, and at least one key information of the contract validity period, the income sharing proportion, and the amount-related breach of contract liability clause is located and extracted from the text by using a named entity recognition model.

[0022] In a possible implementation, the value evaluation report comprises an introduction part, a data evaluation part, a value evaluation part, and a summary part; and the generating the value evaluation report based on the value evaluation method, the basic information of the data asset, the key information, and the calculated value of the data asset comprises:

[0023] filling the value evaluation method, the evaluation object extracted from the data asset basic information, and the evaluation purpose specified by the user into the introduction part;

[0024] filling the integrity judgment generated based on the file material analysis result and the key information summary parsed from the file material into the data evaluation part;

[0025] filling the calculation process explanation of the data asset value and the final evaluation result into the value evaluation part;

[0026] filling the concluding explanation including the final evaluation result and the explanation of the evaluation limitation into the summary part.

[0027] In a possible implementation, the determination manner of the value evaluation method corresponding to the basic information comprises:

[0028] inputting the text description in the data asset basic information into the pre-trained classification model, so that the classification model recommends based on whether the data asset has historical income records, to obtain a classification result output by the model, and the classification result is used to recommend the cost method for data assets without historical income records or recommend the income method for data assets with historical income records.

[0029] In a possible implementation, the method further comprises:

[0030] comparing the calculated data asset value with a preset industry value interval;

[0031] when the data asset value exceeds a threshold range of the industry value interval, generating verification warning information and attaching the verification warning information to the value evaluation report.

[0032] The second aspect of the embodiments of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the data asset value evaluation method of the first aspect is implemented.

[0033] The third aspect of the embodiments of the present application provides a computer program product, when the computer program product runs on a computer, the computer executes the data asset value evaluation method of the first aspect.

[0034] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to perform the data asset valuation method described in the first aspect above.

[0035] Compared with the prior art, this application has the following advantages:

[0036] The system automatically recommends evaluation methods through a pre-trained classification model and uses AI technology to automatically extract key information from documents, replacing the manual judgment and analysis process relied upon by professionals. This significantly improves processing efficiency, shortening the evaluation cycle from several days to minutes, and eliminates biases in evaluation method selection and omissions in key information extraction caused by human subjective experience, thus significantly improving the consistency and objectivity of the evaluation results. Secondly, the automatic value calculation based on unified logic ensures the standardization and reproducibility of the calculation process, avoiding the arbitrariness of manual calculation. Finally, by automatically generating structured evaluation reports, the system mandates the composition and content sources of the reports, ensuring the standardization and completeness of the output results. These results can be directly applied to subsequent data transaction and decision-making scenarios, thereby solving the problems of low efficiency, large subjective errors, and insufficient report standardization in existing technologies. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating a data asset valuation method provided in this application embodiment;

[0039] Figure 2 This is a schematic diagram of module interaction provided in an embodiment of this application. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0041] To facilitate understanding of the technical solutions provided in the embodiments of this application, the background technology involved in the embodiments of this application will be described below.

[0042] As mentioned earlier, existing data asset valuation methods have three significant drawbacks: First, the manual-driven valuation process is inefficient, requiring professionals to spend days to weeks manually analyzing financial contracts and other documents, which is completely unacceptable for the practical needs of the digital economy era for rapid, batch valuation of massive data assets. Second, the valuation process suffers from serious subjective bias, relying on personal experience and judgment from the selection of valuation methods to the extraction of key contract clauses, resulting in insufficient completeness of core information extraction, inconsistent valuation standards, and difficulty in guaranteeing the credibility and consistency of the final output. Finally, the output quality lacks standardized control, and manually written valuation reports exhibit significant randomness in terms of structure and content completeness, directly affecting the direct application value of the valuation results in serious scenarios such as asset transactions and financial audits.

[0043] To address the aforementioned issues, this application's embodiments achieve efficient and accurate assessment by constructing a complete intelligent processing flow. First, based on the basic description of the data assets, the system automatically recommends, or allows the user to specify, an applicable valuation method—cost-based or revenue-based—through a pre-trained classification model, ensuring the scientific rigor and adaptability of the method selection. Next, the system receives various documents uploaded by the user. For structured documents, it directly reads key data; for unstructured documents, it uses optical character recognition combined with natural language processing technology to intelligently extract core clauses and numerical information, completing the standardization of multi-source heterogeneous data. Then, according to the selected valuation method, the system automatically executes the corresponding calculation logic: if the cost-based method is used, it calculates the total investment based on parameters such as acquisition cost, storage cost, and operation and maintenance cost, and deducts depreciation; if the revenue-based method is used, it predicts future revenue based on historical revenue data and contract terms and performs discounting calculations, ultimately arriving at an accurate value result. Finally, the system automatically generates a complete valuation report, including four standard modules: introduction, data evaluation, value assessment, and conclusion, ensuring complete content and consistent format.

[0044] Specifically, it compresses the traditional manual assessment process, which takes several days to several weeks, to minutes, improves the accuracy of key information extraction to over 95%, and increases the consistency of assessment results by over 80%. It effectively solves the three core problems of low efficiency, large subjective error, and insufficient report standardization in existing technologies, and provides efficient, reliable and standardized assessment support for scenarios such as data asset trading, pledge financing and financial auditing.

[0045] It should be noted that the data asset valuation methods, systems, products, equipment, and media provided in this application can be applied to the field of computer technology. The above are merely examples and do not limit the application areas of the data asset valuation methods, systems, products, equipment, and media provided in this application. Furthermore, the embodiments of this application may not limit the entity performing the data asset valuation. For example, the data asset valuation method of this application can be applied to data processing devices such as terminal devices or servers. The terminal device can be an electronic device such as a computer or a personal digital assistant (PDA). The server can be a standalone server, a cloud server, or a cluster server composed of multiple servers.

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0047] The following embodiment illustrates a data asset valuation method provided in this application. See also... Figure 1 ,Should Figure 1 A flowchart of a data asset valuation method provided in this application embodiment, the method including:

[0048] S101. Obtain basic information about the data assets, and determine the value assessment method corresponding to the basic information through a pre-trained classification model and / or user selection instructions.

[0049] Basic information about data assets refers to a set of key metadata that describes the core attributes, status, and application context of data assets. This information serves as the foundational input for initiating an automated assessment process, primarily serving two core purposes: 1) providing a basis for AI to intelligently recommend appropriate value assessment methods; and 2) serving as important descriptive content in the final assessment report.

[0050] Specifically, this basic information is filled in by the user during the initial assessment phase through the system's data and materials input module in the form of a structured form or text description. Its core components include, but are not limited to, one or more of the following:

[0051] Data content and source: A substantive description of the data asset itself, such as "e-commerce user behavior data" or "road traffic flow data", and an explanation of its generation or collection source.

[0052] Time attribute: The key time point in the generation, collection, or updating of data assets, such as "collected in 2023". This information is crucial for subsequent depreciation calculations and determining data timeliness.

[0053] Scale and Coverage: This describes the size and scope of influence of data assets, such as "covering 1 million active users" or "containing 100 million transaction records." This is an important indicator for measuring the potential value and influence of data assets.

[0054] Application scenarios: The business areas or purposes in which the data asset is currently or is expected to be used, such as "for internal operational analysis" or "for user profiling and precision marketing." This information is a key characteristic for AI to determine its profitability.

[0055] Usage status and lifespan: Specify the number of years the data asset has been used (e.g., "used for 1 year") or its preset lifespan (e.g., "usespan of 5 years"). This is a necessary parameter for calculating depreciation when using the cost method.

[0056] Specifically, in order to determine the value assessment method corresponding to the basic information, this application provides three decision paths.

[0057] The first mode is manual selection. In this mode, the system provides users with a clear interface showcasing the two core evaluation methods: the "cost approach" and the "income approach." Users can rely entirely on their own professional judgment, without system assistance, to directly select the target method based on their understanding of the data asset type (e.g., whether it is "basic data" formed by a one-time investment or "derived data" that can generate continuous cash flow). This mode ensures the integrity of the expert user's decision-making power.

[0058] The second approach is the AI-powered intelligent recommendation mode. The system receives a text description of the "basic information about the data asset" from the user (such as a description of the data source and application scenarios) and inputs it into a pre-trained AI classification model. This model makes judgments based on a large amount of prior knowledge and automatically outputs the most suitable evaluation method recommendation. For example, when the model identifies features such as "no clear revenue records" or "raw data" in the basic information description, it recommends the cost approach based on input cost accounting; while when it identifies features such as "historical revenue data" or "authorized operation," it recommends the revenue approach based on future revenue forecasts. This approach effectively lowers the professional threshold for users and ensures the objectivity and consistency of method selection.

[0059] The third type is a hybrid human-computer interaction mode. This mode combines the two mentioned above, presenting users with both a manual selection interface and AI-recommended results. Users can use the AI ​​recommendations as a key decision-making reference, and then, based on their own deeper business insights, make a final confirmation or rejection. This "AI suggestion, human decision-making" approach utilizes the efficiency and standardization of AI while retaining the user's ultimate control, achieving both high efficiency and reliability in practical applications.

[0060] S102. Obtain the file materials related to the data asset uploaded by the user, and extract the key information used for value calculation from the file materials.

[0061] This involves automatically and accurately extracting all key information for value calculation from multi-source heterogeneous materials provided by users, achieving intelligent transformation from raw materials to standardized data. This step first receives two types of structured information through a unified input interface: the first type is a user-generated basic description of the data assets, including but not limited to core characteristics such as data collection time, number of users covered, and application areas; the second type is various supporting files uploaded by users, compatible with common formats such as PDF, Excel, and Word, specifically covering financial documents (such as data collection cost details and system maintenance expense reports) and legal contract documents (such as data licensing agreements and revenue sharing agreements), thus constructing a complete foundation for the assessment data.

[0062] In the information extraction stage, the system employs differentiated intelligent parsing strategies based on file type characteristics. For structured files (such as financial statements in Excel format), the system directly reads the numerical information of specific cells by parsing the underlying data structure of the file, accurately extracting key quantitative indicators such as data collection costs, annual maintenance expenses, and contractual revenue ratios. For unstructured files (such as commercial contracts in PDF format), a fusion technology approach is adopted: first, the image or scanned document is converted into processable text content through an optical character recognition (OCR) engine; then, a deep learning-based named entity recognition (NER) model is used to accurately locate and extract core commercial terms such as contract validity period, revenue sharing ratio, and breach of contract liability amounts from the text stream. At the same time, redundant content such as standard clauses and irrelevant attachments is intelligently filtered to ensure the accuracy and effectiveness of information extraction.

[0063] All key information obtained from the analysis, including feature parameters extracted from text descriptions and quantitative indicators extracted from documents, is standardized and stored in a dedicated system database. This establishes a complete data mapping relationship, providing reliable and comprehensive data support for the automated value calculation in the subsequent S103 step. This process completely changes the traditional manual material review workflow, achieving deep fusion of multi-source data and accurate capture of key information through intelligent technology, providing a solid data foundation for the evaluation work.

[0064] S103. Calculate the value of the data asset based on the valuation method, the basic information of the data asset, and the key information.

[0065] Based on the valuation methods established in the preceding steps, the basic information of the data assets acquired, and the key information extracted from the documents, the system automatically calculates the value of the data assets. Through standardized calculation logic and parameterized processing, it automates and standardizes the calculation of data asset value, effectively eliminating subjective errors from manual calculations, ensuring the objectivity and consistency of the assessment results, and providing accurate value basis for the final generation of a standardized assessment report.

[0066] In one possible implementation, when the valuation method is the cost approach, the data asset's useful life, data acquisition cost, data storage cost, and data operation and maintenance cost are extracted from the basic information of the data asset and / or from the document materials; based on the data asset's useful life, the depreciation amount is calculated using the straight-line depreciation method; the value of the data asset is obtained by summing the data acquisition cost, data storage cost, and data operation and maintenance cost and subtracting the depreciation amount from the sum.

[0067] When the system determines that the cost approach should be used for valuation, it will initiate the cost accounting logic. This method is based on the principle of asset replacement cost. First, it extracts four key parameters from the basic information of the data asset or the parsed documents: the data asset's useful life, data acquisition cost, data storage cost, and data operation and maintenance cost. Then, based on the data asset's useful life, the system automatically calculates the depreciation amount using the straight-line depreciation method (the formula is: Depreciation Amount = Total Cost × (Used Years / Total Useful Life)). Finally, the data asset value is obtained by aggregating all costs and deducting depreciation, specifically calculated as: Data Asset Value = Data Acquisition Cost + Data Storage Cost + Data Operation and Maintenance Cost - Depreciation Amount. For example, when the acquisition cost is 800,000 yuan, the storage cost is 200,000 yuan, the operation and maintenance cost is 500,000 yuan, and the depreciation amount is 300,000 yuan, the system will automatically output an assessed value of 1,200,000 yuan.

[0068] In one possible implementation, when the valuation method is the income approach, historical revenue data and contractually agreed revenue ratios are obtained from the extracted key information; based on the historical revenue data and the revenue ratios, the expected revenue for the next N years is calculated, where N is a positive integer; based on a preset industry average discount rate, the sum of the present values ​​of the expected revenue for the next N years is calculated as the value of the data asset.

[0069] When the system determines that the income approach should be used for valuation, it initiates the future income discounting logic. This method, based on the asset's future profitability, first obtains historical income data and contractually agreed income ratios from the analyzed key information, and then calculates the expected income for the next N years (N being a positive integer). Subsequently, the system calls a preset industry average discount rate and calculates the present value using the formula: Present Value = Σ [Expected Income in Year t / (1 + Discount Rate)^t], where t = 1 to N, to determine the present value of the future income stream. For example, if the expected income for the next three years is 600,000, 700,000, and 800,000 yuan respectively, and the discount rate is 8%, the system will automatically calculate the total present value to be approximately 1,836,700 yuan, and use this as the valuation of the data asset.

[0070] Through these two rigorously designed calculation paths, the system can automatically complete the accurate calculation from basic parameters to final value without human intervention, ensuring both the standardization of the evaluation process and the accuracy and reliability of the evaluation results.

[0071] S104. Based on the value assessment method, the basic information of the data asset, the key information, and the calculated value of the data asset, generate a value assessment report.

[0072] Based on all the outputs from the preceding steps, including the valuation methods used, basic information about the data assets, key information extracted from the documents, and the calculated value of the data assets, a complete valuation report is automatically generated. This report uses a standardized structured template to ensure the standardization and professionalism of the output content.

[0073] The assessment reports generated through this standardized process not only significantly improve report writing efficiency, reducing the workload of several days in the traditional manual mode to minutes, but also effectively solve problems such as inconsistent formatting and missing content caused by manual writing, providing standardized professional documents that can be used directly for application scenarios such as data asset trading, pledge financing, and financial auditing.

[0074] In one possible implementation, the valuation report employs a structured template design, comprising four logically rigorous core parts: an introduction, a data evaluation section, a valuation section, and a conclusion. The system uses intelligent fill technology to automatically assign various types of information obtained from pre-processing to their corresponding modules: In the introduction, the system integrates and fills in the valuation methods used, the description of the valuation object's characteristics extracted from the basic information of the data assets, and the user-specified valuation purpose; in the data evaluation section, the system generates a judgment on the completeness of the input materials based on the document parsing results and summarizes the key information elements extracted from various documents; in the valuation section, the system provides a detailed explanation of the derivation process for calculating the data asset value and the precise final valuation results; in the conclusion, the system generates a comprehensive statement containing the final value conclusion and explains the limitations of this valuation.

[0075] In one possible implementation, the valuation report is output in PDF format; and / or, a secondary editing interface for the valuation report is provided.

[0076] This means that the system generates downloadable PDF documents that conform to industry standards based on the assessment report. While ensuring the standardization of the report structure and the professionalism of the content, it also provides user interaction functions, allowing users to make necessary secondary edits and supplementary notes to the generated report content. Thus, the editable mechanism preserves the space for professional judgment, so that the output of the system can not only meet the standardization requirements, but also adapt to the special needs of specific application scenarios, significantly improving the applicability and practical value of the assessment results in actual business scenarios.

[0077] In one possible implementation, the value assessment section displays the mathematical expressions used in the calculation process in formula form, and the specific numerical values ​​obtained from the key information are substituted into the formula for display.

[0078] In one possible implementation, after extracting the key information for value calculation from the document material, the method further includes: storing the extracted key information in a structured database and attaching a data source tag to each key information; when calculating the value of the data asset, retrieving the key information with the data source tag from the structured database.

[0079] In one possible implementation, the method further includes a batch evaluation mode, which simultaneously acquires basic information and related documents of multiple data assets; performs the steps of determining the valuation method, extracting key information, calculating value, and generating an evaluation report in parallel for each data asset; and outputs a summary report containing the evaluation results of all data assets.

[0080] In one possible implementation, the method further includes an evaluation result verification step: comparing the calculated data asset value with a preset industry value range; when the data asset value exceeds the threshold range of the industry value range, generating a verification warning message and attaching it to the value assessment report.

[0081] The system automatically compares the calculated value of data assets with a pre-defined industry value range database. This database contains dynamic value reference ranges based on multi-dimensional characteristics such as industry classification, asset type, and data scale. When the system detects that the current assessed value exceeds the preset threshold range of the corresponding industry value range, it automatically generates specific verification warning information. This generated warning information will be automatically added to the final value assessment report as important information. This information not only clearly points out potential anomalies in the assessment results but also analyzes and prompts key factors that may lead to deviations (such as insufficient input data integrity, abnormal values ​​of key parameters, or problems with the applicability of the assessment method), providing report users with professional risk warnings and decision-making references. This effectively enhances the self-verification capability of the assessment system. By introducing industry benchmark data as an external reference, it significantly improves the objectivity and reliability of the assessment results, providing additional security for data asset transactions, financing, and other application scenarios.

[0082] Compared with the prior art, the present invention has the following core advantages:

[0083] First, it achieves fully automated assessment, resulting in a significant improvement in efficiency. By building a complete automated processing chain, the assessment process, which traditionally relied on manual operation and took days or even weeks, is compressed to minutes (the assessment time for a single data asset does not exceed 10 minutes). The system further supports batch concurrent processing capabilities, capable of processing no fewer than 100 data assets simultaneously, fundamentally solving the efficiency bottleneck problem of large-scale data asset assessment.

[0084] Secondly, driven by AI technology, the accuracy and objectivity of the evaluation process have been significantly improved. The system comprehensively utilizes natural language processing and optical character recognition technologies to intelligently extract key information from multi-source documents with an accuracy rate of no less than 95%. By recommending evaluation methods and performing automatic calculations through AI models, subjective biases and random errors caused by reliance on human experience are effectively avoided, improving the consistency of results across different evaluation projects by more than 80%.

[0085] Third, it outputs highly structured standard reports with excellent adaptability to various scenarios. The evaluation reports generated by the system strictly follow a fixed structure, fully including four core modules: "Introduction, Data Evaluation, Value Assessment, and Conclusion." This standardized output format ensures the integrity of the content and the uniformity of the format. The generated reports can be directly applied to various business scenarios such as data asset transactions, rights pledge financing, and corporate financial auditing without secondary processing.

[0086] Fourth, the system architecture is flexible, with good scalability and environmental adaptability. The current system supports two core evaluation methods: the cost approach and the income approach. Its modular design facilitates the addition of other evaluation methods such as the market approach in the future. Furthermore, the system is compatible with multiple file formats and supports various deployment modes, including cloud and local deployments, enabling it to flexibly adapt to the existing technical architectures and differentiated business needs of different enterprises.

[0087] The above are some specific implementations of the data asset valuation method provided in the embodiments of this application. Based on this, this application also provides a corresponding data asset valuation system. The system provided in the embodiments of this application will be described below from the perspective of functional modularization. The device of this invention mainly includes 5 functional modules, each module operating independently and linked sequentially, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of module interaction provided in an embodiment of this application.

[0088] The evaluation method selection module serves as the starting point of the process. This module provides a selection mechanism for two evaluation methods: cost approach and income approach. It supports users to manually specify or intelligently recommend applicable methods based on the description of data assets using a pre-trained AI model, ensuring a scientific match between the evaluation method and the characteristics of the assets.

[0089] The data and materials input module is used to receive the information submitted by users for the assessment, including basic descriptive text of data assets (such as collection time, coverage, application scenarios, etc.) and related documents and materials (such as financial statements, authorization contracts, etc.), providing a data foundation for subsequent analysis and calculation.

[0090] The AI ​​file parsing module is used to intelligently parse files uploaded by the input module: for structured files (such as Excel), it directly reads key data; for unstructured files (such as PDF contracts), it uses OCR and NER technologies to extract core clauses and numerical information, realizing standardized processing of multi-source heterogeneous data.

[0091] The AI-powered valuation and calculation module, as the core processing unit, automatically performs value calculations based on the selected valuation method, basic asset information, and key parameters obtained from analysis. It supports both the cost approach (based on cost and depreciation) and the income approach (based on discounted cash flow), outputting accurate value results.

[0092] The assessment report generation module serves as the output terminal, automatically generating a complete assessment report based on the processing results of the aforementioned modules. The report includes standardized sections such as an introduction, data evaluation, value assessment, and conclusion, supports PDF export and secondary content editing, and meets the format and content requirements of different application scenarios.

[0093] Furthermore, it should be noted that this system adopts a highly flexible deployment architecture, supporting stable operation in the cloud (such as mainstream cloud platforms like AWS and Alibaba Cloud) or local server environments. Through the application of distributed computing technology, the system has the ability to horizontally scale to handle massive data asset assessment tasks, effectively meeting the performance requirements of large-scale enterprise-level assessment scenarios.

[0094] In terms of system scalability, a modular design concept is adopted, with each core functional module supporting independent upgrades and functional expansions. Taking the addition of a new evaluation method as an example: when it is necessary to add a "market approach" evaluation function in the future, only the evaluation method selection module and the AI ​​evaluation calculation module need to be updated to achieve functional expansion, without the need for system-level reconstruction, which significantly reduces the subsequent maintenance and iteration costs.

[0095] Furthermore, the system provides standardized data interfaces that can seamlessly connect to mainstream data visualization tools such as Tableau, displaying evaluation results intuitively through rich chart formats. This effectively enhances the readability and decision support capabilities of the evaluation results, meeting the diverse data consumption needs of users at different levels. This open architecture design ensures the stability of the system's core functions while reserving ample space for future functional expansion and ecosystem integration.

[0096] The following example illustrates the data asset valuation provided in this application and the coordination of various modules in a practical application scenario. This example uses a cloud-deployed device and activates all five modules: "Assessment Method Selection Module," "Data and Material Input Module," "AI File Parsing Module," "AI Assessment Calculation Module," and "Assessment Report Generation Module." Data interaction between modules is achieved through cloud APIs.

[0097] S201. Select a valuation calculation method.

[0098] Users input a basic summary of the data asset ("e-commerce user behavior data collected in 2023, with no clear historical revenue records") through the "Evaluation Method Selection Module." The module's AI recommends the "cost method." After the user confirms their selection, the method result is synchronized to the AI ​​evaluation calculation module (see reference). Figure 2 (Linkage between the "Evaluation Method Selection Module" and the "AI Evaluation Calculation Module").

[0099] S202. Input data, asset information, and documents.

[0100] Users fill in a complete basic introduction through the "Data and Materials Input Module," such as "Data covers 1 million active users, collected for internal operational analysis, with a usage period of 5 years," and upload 3 files: "2023 Data Collection Service Contract.xlsx" (structured file, including collection cost of 800,000 yuan); "2023-2024 Data Storage Fee Report.pdf" (unstructured file, including annual storage cost of 100,000 yuan); and "2023-2024 Data Operation and Maintenance Personnel Fee Report.xlsx" (structured file, including annual operation and maintenance cost of 250,000 yuan).

[0101] S203, AI analyzes the content of the document materials.

[0102] After the "AI File Parsing Module" is activated, it directly extracts indicators from structured files: "Collection cost 800,000 yuan", "Total storage cost 200,000 yuan in 2023-2024", and "Total maintenance cost 500,000 yuan in 2023-2024". For unstructured PDFs containing storage costs, it extracts "Annual storage cost 100,000 yuan" after recognizing the text using OCR. Finally, all parsing indicators are stored in the cloud database (parsing accuracy 100%).

[0103] S204, AI automatically calculates the value of data assets

[0104] The "AI Evaluation Calculation Module" calls the following information:

[0105] Method selection: cost method; Analysis indicators: data acquisition cost 800,000, storage cost 200,000, operation and maintenance cost 500,000; Basic introduction: extracted information: service life of 5 years, depreciation amount is calculated using the straight-line depreciation method (annual depreciation rate of 20%).

[0106] The calculation process based on the above-mentioned content is as follows: Total investment cost = 80 + 20 + 50 = 1.5 million yuan; Depreciation amount = 1.5 million × 20% × 1 (used for 1 year) = 300,000 yuan; Data asset value = 1.5 million - 300,000 = 1.2 million yuan.

[0107] S205, Output a value assessment report.

[0108] The "Assessment Report Generation Module" automatically generates a PDF report, the core content of which is as follows:

[0109] Introduction: The evaluation object is "2023 e-commerce user behavior data", the evaluation purpose is "internal asset inventory", and the evaluation method is "cost method".

[0110] Data evaluation section: Document integrity "no missing parts", key information summary "collection cost 800,000 yuan, usage period 5 years";

[0111] Valuation section: Calculation process: "80 + 20 + 50 - 30 = 1.2 million yuan", valuation result: "1.2 million yuan";

[0112] Summary: Conclusion: "The current value of this data asset is 1.2 million yuan." Limitation: "The risk of data devaluation due to timeliness was not considered."

[0113] This application also provides corresponding devices and computer storage media for implementing the data asset valuation scheme provided in this application.

[0114] The device includes a memory and a processor. The memory stores instructions or code, and the processor executes the instructions or code to enable the device to perform the data asset valuation method described in any embodiment of this application.

[0115] The computer storage medium stores code, and when the code is run, the device running the code implements the data asset valuation method described in any embodiment of this application.

[0116] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0117] It should be understood that in this application, "at least one" refers to one or more items, and "more" refers to two or more items. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one" of a, b, or c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0118] It should be understood that the terms center, longitudinal, transverse, up, down, front, back, left, right, vertical, horizontal, top, bottom, inside, outside, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0119] It should be noted that, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0120] It should also be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the statement "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0121] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing the value of data assets, characterized in that, include: Obtain basic information about data assets, and determine the value assessment method corresponding to the basic information through a pre-trained classification model and / or user selection instructions; Obtain files and materials uploaded by users that are related to the data assets, and extract key information from the files and materials used for value calculation; Based on the valuation method, the basic information of the data asset, and the key information, calculate the value of the data asset; A value assessment report is generated based on the aforementioned value assessment method, the basic information of the data asset, the key information, and the calculated value of the data asset.

2. The method according to claim 1, characterized in that, The calculation of the value of the data asset based on the valuation method, the basic information of the data asset, and the key information includes: When the valuation method is the cost approach, it is based on extracting the data asset's useful life, data acquisition cost, data storage cost, and data operation and maintenance cost from the basic information of the data asset and / or the document materials. Based on the useful life of the data assets, the depreciation amount is calculated using the straight-line depreciation method. The value of the data asset is obtained by summing the data acquisition cost, data storage cost, and data operation and maintenance cost and subtracting the depreciation amount.

3. The method according to claim 1, characterized in that, The calculation of the value of the data asset based on the valuation method, the basic information of the data asset, and the key information includes: When the valuation method is the income approach, historical income data and the income ratio stipulated in the contract are obtained from the extracted key information. Based on the historical return data and the return ratio, the expected return for the next N years is calculated, where N is a positive integer; Based on a pre-set industry average discount rate, the sum of the present values ​​of expected returns over the next N years is calculated as the value of the data asset.

4. The method according to claim 1, characterized in that, The extraction of key information from the document material used for value calculation includes: For the structured files in the document materials, at least one key indicator from data acquisition cost, storage cost, operation and maintenance cost, and historical revenue data is extracted by reading cells or data structures in the structured files. For unstructured documents in the document materials, the unstructured documents are converted into text by optical character recognition, and at least one key piece of information, including the contract validity period, profit sharing ratio, and liability for breach of contract related to the amount, is located and extracted from the text using a named entity recognition model.

5. The method according to claim 1, characterized in that, The valuation report includes an introduction, a data evaluation section, a valuation section, and a conclusion section. The valuation report, generated based on the valuation method, the basic information of the data assets, the key information, and the calculated value of the data assets, includes: Fill the introductory section with the value assessment method, the assessment object extracted from the basic information of the data assets, and the assessment purpose specified by the user. The integrity judgment generated based on the document material analysis results, and the key information summary parsed from the document material, are filled into the data evaluation section; The calculation process of the data asset value and the final evaluation results shall be filled into the value evaluation section; The summary section should include a conclusive statement of the final evaluation results and a description of the limitations of the evaluation.

6. The method according to claim 1, characterized in that, The methods for determining the valuation method corresponding to the basic information include: The text description in the basic information of the data asset is input into the pre-trained classification model, so that the classification model makes recommendations based on whether the data asset has historical revenue records, and obtains the classification result output by the model. The classification result is used to recommend data assets without historical revenue records to use the cost method or to recommend data assets with historical revenue records to use the revenue method.

7. The method according to claim 1, characterized in that, The method further includes: The calculated value of the data assets is compared with a preset industry value range; When the value of the data asset exceeds the threshold range of the industry value range, a verification warning message is generated and attached to the value assessment report.

8. A computer program product, characterized in that, The computer program product stores instructions that, when executed on a terminal device, cause the terminal device to perform the data asset valuation method as described in any one of claims 1-7.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the data asset valuation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a terminal device, cause the terminal device to perform the data asset valuation method as described in any one of claims 1-7.

Images