Data asset price evaluation method and system based on multi-dimensional coefficient adjustment

Abstract

The invention relates to a data asset price evaluation method and system based on multi-dimensional coefficient adjustment. The method comprises the following steps: S1, constructing an evaluation formula; s2, determining a data scarcity adjustment coefficient S; s3, determining a data credibility adjustment coefficient C; s4, calculating a data timeliness degree adjustment coefficient T; s5, determining a report integrity adjustment coefficient R; s6, the comparable instance transaction price is corrected; and S7, comprehensively calculating an evaluation value, substituting the parameters determined in the steps S1 to S6 into an evaluation formula, and outputting a final technical report evaluation value. The method has the advantages that through multi-dimensional dynamic modeling, intelligent algorithm driving and full-process transparent design, the core pain points of single dimension, static stiffness, non-verifiability and the like in traditional data asset estimation are solved, technical innovation and commercial practicability are achieved, and key infrastructure support is provided for data element marketization.

Description

Technical Field

[0001] The present invention relates to a data asset price evaluation method and system based on multi-dimensional coefficient adjustment, belonging to the technical field of data asset price evaluation, and involving aspects of data processing, data analysis, and price evaluation. Background Art

[0002] With the rapid development of the digital economy, data assets have become an important part of an enterprise's core competitiveness. However, there are significant defects in existing data asset price evaluation methods, mainly reflected in the neglect of multi-dimensional influencing factors, resulting in evaluation results deviating from the true market value. The specific deficiencies are as follows:

[0003] 1. Single-dimensional evaluation, ignoring data scarcity

[0004] Traditional methods usually perform linear valuation based on data costs or historical transaction prices. For example, the cost method (data acquisition and storage costs) or the market method (prices of comparable transaction cases) is adopted. However, such methods do not quantify the amplifying effect of data scarcity on value. For example, credit data that is exclusively circulated in a certain industry is highly irreplaceable, and its value should be significantly higher than that of similar data that can be obtained through public channels. However, existing models do not introduce a scarcity adjustment coefficient, resulting in such high-value data being underestimated.

[0005] 2. Credibility differences are not quantified

[0006] The reliability of data sources directly affects their application value. For example, data provided by government agencies has authority and legal endorsement, while data crawled by web crawlers may have risks of integrity and authenticity. However, existing technologies lack a hierarchical evaluation mechanism for data credibility, and only use "availability" as the judgment standard, without incorporating source credibility (such as government, enterprise, open platform) into value calculation, resulting in evaluation results that cannot reflect the true risk premium.

[0007] 3. The timeliness adjustment mechanism is rigid

[0008] The value of data decays non-linearly over time. For example, real-time transaction data has the highest value in the financial risk control scenario, but its value may drop by more than 80% after more than 24 hours. Existing methods mostly adopt a fixed depreciation rate (such as an annual depreciation of 20%), and cannot dynamically respond to the impact of different time differences (such as 1 day vs. 1 year) on value, especially without distinguishing the timeliness differences between active data and historically archived data, resulting in evaluation results being out of touch with the actual market demand.

[0009] 4. The synergistic effect of multiple factors is not modeled

[0010] Current methods usually consider a single factor (such as cost or benefit) in isolation and lack a comprehensive modeling of scarcity, credibility, timeliness, and report integrity. For example, if a highly scarce piece of data has a questionable source or poor timeliness, its true value should be significantly lower than the theoretical value. However, the existing evaluation system does not establish a linkage calculation framework for multi-dimensional coefficients, resulting in overly idealized evaluation results that cannot support actual transaction decisions. Summary of the Invention

[0011] To overcome the defects of the prior art, the present invention proposes a data asset price evaluation method based on dynamic adjustment of multi-dimensional coefficients. By quantifying the synergistic effects of scarcity, credibility, timeliness, and integrity, a non-linear evaluation model is constructed to solve the technical limitations of traditional methods and improve the scientificity and practicality of evaluation results.

[0012] The present invention provides a data asset price evaluation method based on multi-dimensional coefficient adjustment. The technical solution of the present invention is as follows:

[0013] A data asset price evaluation method based on multi-dimensional coefficient adjustment includes:

[0014] Step S1: Construct an evaluation formula; Step S2: Determine the data scarcity adjustment coefficient S;

[0015] Step S3: Determine the data credibility adjustment coefficient C;

[0016] Step S4: Calculate the data timeliness adjustment coefficient T;

[0017] Step S5: Determine the report integrity adjustment coefficient R;

[0018] Step S6: Correct the transaction price of the comparable instance;

[0019] Step S7: Comprehensively calculate the evaluation value, substitute the parameters determined in Steps S1 to S6 into the evaluation formula, and output the evaluation value of the final technical report.

[0020] The specific content of Step S1 is that the evaluation value V of the technical report is calculated by the following formula: V = P corrected × S × C × T × R; where: P corrected is the corrected transaction price of the comparable instance, S is the data scarcity adjustment coefficient, C is the data credibility adjustment coefficient, T is the data timeliness adjustment coefficient, and R is the report integrity adjustment coefficient.

[0021] The specific content of Step S2 is as follows: According to the uniqueness of the data circulation channel, coefficients are dynamically allocated through a mapping function:

[0022]

[0023] The specific content of Step S3 is as follows: Based on the authority of the data source, values are assigned through the following rules:

[0024]

[0025] The described step S4 is calculated using a piecewise linear function, specifically as follows:

[0026]

[0027] The described step S5 is assigned values according to the following rules based on the completeness of the technical report, specifically as follows:

[0028]

[0029] The described step S6 is specifically as follows:

[0030] 6.1 Calculate the weight wi based on feature similarity, where xj is the j-th feature value of the asset to be evaluated and yj is the j-th feature value of the comparable instance; 6.2 After normalizing the weights, calculate the weighted average price: In the described step S4, when the data contains multiple timestamps, the comprehensive timeliness coefficient is calculated through the following weighted formula:

[0031] T = α·T_active + (1 - α)·T_sealed; where α is the proportion of active users, and T_active and T_sealed are the timeliness coefficients corresponding to the time differences of active and sealed users respectively.

[0032] A data asset price evaluation system based on dynamic adjustment of multi-dimensional coefficients, including:

[0033] An evaluation formula construction module for constructing an evaluation formula;

[0034] A data scarcity adjustment coefficient determination module for determining the data scarcity adjustment coefficient S;

[0035] A data credibility adjustment coefficient determination module for determining the data credibility adjustment coefficient C;

[0036] A data timeliness adjustment coefficient calculation module for calculating the data timeliness adjustment coefficient T;

[0037] A report integrity adjustment coefficient determination module for determining the report integrity adjustment coefficient R; a comparable instance transaction price correction module for correcting the comparable instance transaction price;

[0038] A technical report value evaluation module for comprehensively calculating the evaluation value and outputting the final technical report evaluation value.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the data asset price evaluation method based on multi-dimensional coefficient adjustment.

[0040] The advantages of the present invention are as follows:

[0041] 1. Multi-dimensional comprehensive evaluation to improve the accuracy of valuation

[0042] The present invention creatively incorporates four core factors, namely data scarcity, credibility, timeliness, and report integrity, into a unified evaluation framework, and quantifies the non-linear impact of each dimension on value through dynamic coefficient adjustment.

[0043] 2. Driven by dynamic algorithms to enhance the flexibility of evaluation

[0044] Price correction algorithm: Calculate the similarity of comparable instances through Euclidean distance, and combine machine learning clustering analysis to eliminate abnormal transactions to ensure the scientific nature of the benchmark price;

[0045] Dynamic update of timeliness: When a new timestamp input is detected, automatically trigger the recalculation of the timeliness coefficient to respond to the change of data value in real time;

[0046] 3. Traceability throughout the process to ensure the verifiability of results

[0047] Automatically generate a standardized technical report including parameter sources, calculation logics, and adjustment processes, and output interactive charts (such as coefficient weight distribution, value decay curve);

[0048] 4. High compatibility and scalability

[0049] Modular design: The system supports independent calls to single coefficient calculation modules such as scarcity and credibility, and adapts to the needs of different industries (such as financial risk control emphasizing timeliness, and government data opening emphasizing credibility);

[0050] 5. Lower the technical threshold and promote industry standardization

[0051] Automated tool chain: Automate the entire process from data collection, coefficient calculation to report generation, reducing manual intervention errors;

[0052] Through multi-dimensional dynamic modeling, intelligent algorithm drive, and full-process transparency design, the present invention solves the core pain points in traditional data asset valuation, such as single dimension, static rigidity, and non-verifiability, and combines technical innovation and commercial practicability, providing key infrastructure support for the marketization of data elements. Brief Description of the Drawings

[0053] Figure 1 It is a schematic diagram of the system structure of the present invention. Detailed Implementation Modes

[0054] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description progresses. However, these embodiments are merely exemplary and do not impose any limitation on the scope of the present invention. Those skilled in the art should understand that without departing from the spirit and scope of the present invention, modifications or substitutions can be made to the details and forms of the technical solutions of the present invention, but such modifications and substitutions all fall within the protection scope of the present invention.

[0055] See Figure 1 , the present invention relates to a method for evaluating the price of data assets based on multi-dimensional coefficient adjustment

[0056] A method for evaluating the price of data assets based on multi-dimensional coefficient adjustment, comprising:

[0057] Step S1: Construct an evaluation formula; Step S2: Determine the data scarcity adjustment coefficient S;

[0058] Step S3: Determine the data credibility adjustment coefficient C;

[0059] Step S4: Calculate the data timeliness adjustment coefficient T;

[0060] Step S5: Determine the report integrity adjustment coefficient R;

[0061] Step S6: Revise the transaction price of the comparable instance;

[0062] Step S7: Comprehensively calculate the evaluation value, substitute the parameters determined in Steps S1 to S6 into the evaluation formula, and output the evaluation value of the final technical report.

[0063] The setting of the method for evaluating the price of data assets based on multi-dimensional coefficient adjustment achieves the following advantages:

[0064] 1. Multi-dimensional comprehensive modeling to improve the scientificity and accuracy of evaluation

[0065] Step S1 (Construct an evaluation formula): Deeply integrate the four core dimensions of data scarcity, credibility, timeliness, and report integrity, breaking through the limitations of traditional single dimensions (such as cost or revenue).

[0066] Steps S2 - S5 (Determine coefficients S, C, T, R):

[0067] S (Scarcity): Distinguish the three-level weights of "unique in circulation", "specific channels", and "visible on the network" (1.5 / 1.0 / 0.5), accurately quantify the irreplaceability of data, and avoid underestimation of high-value data;

[0068] C (Credibility): Dynamically adjust according to the authority of the data source (government 1.2, web crawler 0.5), reducing the interference of low-quality data;

[0069] T (Timeliness): A piecewise linear function is used to simulate the value decay curve, differentiating the timeliness differences between active data and archived data;

[0070] R (Integrity): Graded assignment is carried out according to the report completeness (complete: 1.2, only basic: 0.5) to ensure that the evaluation results match the actual application scenarios of the data.

[0071] 2. Dynamic correction mechanism, enhancing flexibility and market adaptability, dynamically correcting market prices and eliminating market volatility interference.

[0072] 3. Full-process transparency, ensuring the verifiability and compliance of results

[0073] Step S1 (Formula transparency): The evaluation formula and parameter definitions are publicly available for inspection, supporting third-party audits;

[0074] Step S7 (Comprehensive calculation): Generate a standardized technical report including parameter sources and calculation logics, and output interactive charts (such as coefficient weight distribution diagrams, value decay curves);

[0075] Step S3 (Credibility verification): Use blockchain technology to perform hash verification on government data sources to ensure the traceability of the C coefficient assignment.

[0076] 4. Intelligence and automation, reducing the cost of manual intervention

[0077] Through the refined division of labor and collaboration in steps S1 - S7, the present invention realizes the leap from "static single dimension" to "dynamic multi-dimension" in the price evaluation of data assets:

[0078] The formula framework constructed in step S1 lays a scientific foundation; the dynamic adjustment of coefficients in steps S2 - S5 solves the rigidity problem of traditional methods; the intelligent correction and transparent output in steps S6 - S7 ensure the reliability of results.

[0079] Finally, this method is comprehensively superior to the existing technologies in terms of accuracy, flexibility, verifiability, and scalability, providing a standardized tool for scenarios such as data asset trading, financing and mortgage, and compliance auditing.

[0080] Specifically for the above-mentioned step S1, the evaluated value V of the technical report is calculated by the following formula: V = P corrected × S × C × T × R; where: P corrected is the corrected comparable instance transaction price, S is the data scarcity adjustment coefficient, C is the data credibility adjustment coefficient, T is the data timeliness adjustment coefficient, and R is the report integrity adjustment coefficient.

[0081] Specifically for the above-mentioned step S2: According to the uniqueness of the data circulation channels, coefficients are dynamically allocated through a mapping function:

[0082]

[0083] The specific steps of step S3 are as follows: Based on the authority of the data source, assign values according to the following rules:

[0084]

[0085] The step S4 is calculated using a piecewise linear function, specifically:

[0086]

[0087] The step S5 assigns values according to the following rules based on the integrity of the technical report, specifically:

[0088]

[0089] The specific steps of step S6 are as follows:

[0090] 6.1 Calculate the weight wi based on feature similarity, where xj is the j-th feature value of the asset to be evaluated, and yj is the j-th feature value of the comparable instance; 6.2 After normalizing the weights, calculate the weighted average price: In the step S4, when the data contains multiple timestamps, calculate the comprehensive timeliness coefficient through the following weighted formula:

[0091] T = α·T_active + (1 - α)·T_sealed; where α is the proportion of active users, and T_active and T_sealed are the timeliness coefficients corresponding to the time differences of active and sealed users respectively.

[0092] The present invention constructs a set of scientific and flexible data asset price evaluation systems through multi-dimensional coefficient dynamic adjustment and intelligent correction algorithms. The specific process is as follows:

[0093] 1. Multi-dimensional coefficient dynamic modeling (steps S1 - S5)

[0094] Incorporate the four core dimensions of data scarcity (S), credibility (C), timeliness (T), and report integrity (R) into a unified calculation model to achieve the synergistic effect of multiple factors.

[0095] Step S2 (scarcity quantification): Dynamically allocate coefficients according to the uniqueness of the data circulation channels:

[0096] Unique circulation (S = 1.5): Exclusive data, value multiplied;

[0097] Specific channel (S = 1.0): Limited circulation, standard weight;

[0098] Network visible (S = 0.5): Public data, value reduced.

[0099] Step S3 (credibility grading): Assigning values based on the authority of the data source:

[0100] Government data (C = 1.2): authoritative and credible, with low risk premium;

[0101] The source is clear but not verified (C = 1.0): default weight;

[0102] Web crawler data (C=0.5): low credibility and reduced value.

[0103] Step S4 (dynamic calculation of timeliness):

[0104] Piecewise linear function: dynamically adjusts the coefficients according to the time difference (Δt), for example:

[0105] Δt<1 year: the coefficient increases linearly from 81% to 100%, reflecting the high value of recent data;

[0106] Δt≥4 years: coefficient is Attenuation, simulating a sudden drop in the value of long-term data. Multi-timestamp weighting: When the data contains active and archived users, calculate the comprehensive timeliness coefficient to accurately distinguish the value of dynamic data from static data.

[0107] Step S5 (integrity classification):

[0108] Complete report (R=1.2): covers all core fields and maximizes value;

[0109] Partially missing (R = 1.0): missing non-critical fields, standard weight;

[0110] Basic content only (R=0.5): missing core fields, value halved.

[0111] 2. Intelligent correction and comprehensive calculation (steps S6-S7)

[0112] Step S6 (price correction):

[0113] Similarity calculation to eliminate abnormal transactions that deviate from the cluster;

[0114] Weighted average: After normalizing the weights, correct the prices and eliminate market noise.

[0115] Step S7 (comprehensive calculation): Substitute each coefficient into the formula and output the final evaluation value.

[0116] This invention achieves three major breakthroughs in data asset price evaluation through dynamic adjustment of multi-dimensional coefficients, intelligent correction algorithm and full-process transparent design:

[0117] 1. From static to dynamic: respond to changes in data value in real time and avoid the rigidity of traditional fixed depreciation rates;

[0118] 2. From single to comprehensive: Quantify the synergistic effects of scarcity, credibility, timeliness, and integrity to improve valuation accuracy;

[0119] 3. From black box to transparent: The formula, parameters, and calculation process are traceable throughout, meeting the requirements of compliance audits.

[0120] Finally, this method provides a standardized and highly credible evaluation tool for scenarios such as data asset trading, financing and mortgage, and risk management, promoting the marketization process of data elements.

[0121] The present invention also relates to a data asset price evaluation system based on dynamic adjustment of multi-dimensional coefficients, including:

[0122] An evaluation formula construction module 1 for constructing an evaluation formula;

[0123] A data scarcity adjustment coefficient determination module 2 for determining a data scarcity adjustment coefficient S;

[0124] A data credibility adjustment coefficient determination module 3 for determining a data credibility adjustment coefficient C;

[0125] A data timeliness adjustment coefficient calculation module 4 for calculating a data timeliness adjustment coefficient T;

[0126] A report integrity adjustment coefficient determination module 5 for determining a report integrity adjustment coefficient R;

[0127] A comparable instance transaction price correction module 6 for correcting the transaction price of comparable instances;

[0128] A technical report value evaluation module 7 for comprehensively calculating the evaluation value and outputting the final technical report evaluation value.

[0129] This system realizes three major breakthroughs in data asset price evaluation through modular division of labor, intelligent algorithm drive, and full-link transparency design:

[0130] 1. From decentralized to integrated: 7 modules work together to cover the entire evaluation process;

[0131] 2. From manual to intelligent: Automatic correction, real-time update, and machine learning greatly reduce human errors;

[0132] 3. From closed to open: The modules are scalable, the algorithms are replaceable, and the reports are auditable, meeting diverse business needs.

[0133] Finally, this system provides an efficient, accurate, and credible standardized tool for scenarios such as data asset trading, financing and mortgage, and risk management, promoting the marketization process of data elements.

[0134] The present invention also relates to a computer-readable storage medium storing a computer program which, when executed by a processor, implements the data asset price evaluation method based on multi-dimensional coefficient adjustment.

[0135] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention should cover within the protection scope of the present invention any equivalent replacement or change made according to the technical solution of the present invention and its inventive concept.

Claims

1. A data asset price evaluation method based on multi-dimensional coefficient adjustment, characterized in that Including: Step S1, constructing an evaluation formula; Step S2, determining the data scarcity adjustment coefficient S; Step S3, determining the data credibility adjustment coefficient C; Step S4, calculating the data timeliness adjustment coefficient T; Step S5, determining the report integrity adjustment coefficient R; Step S6: correcting the transaction price of the comparable instance; Step S7, comprehensively calculating the evaluation value, substituting the parameters determined in Steps S1 to S6 into the evaluation formula, and outputting the evaluation value of the final technical report.

2. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 1, wherein Specifically, the step S1 is as follows. The evaluation value V of the technical report is calculated by the following formula: V = P 修正 × S × C × T × R; where: P is the corrected comparable instance transaction price, S is the data scarcity adjustment coefficient, C is the data credibility adjustment coefficient, T is the data timeliness adjustment coefficient, and R is the report integrity adjustment coefficient.

3. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 1 or 2, characterized in that The specific content of Step S2 is as follows: According to the uniqueness of the data circulation channel, dynamically allocate coefficients through a mapping function:

4. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 3, characterized in that The specific step S3 is as follows: Based on the authority of the data source, assign values according to the following rules:

5. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 4, characterized in that, The calculation in Step S4 adopts a piecewise linear function, specifically:

6. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 5, characterized in that Step S5 is assigned values according to the completeness of the technical report according to the following rules, specifically:

7. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 6, characterized in that, The specific content of Step S6 is: 6.1 Calculate the weight w based on feature similarity i , where x j is the j-th eigenvalue of the asset to be evaluated, and y j is the j-th eigenvalue of the comparable instance; 6.2 After normalizing the weights, calculate the weighted average price:

8. The data asset price evaluation method based on multi-dimensional coefficient adjustment according to claim 7, characterized in that In Step S4, when the data contains multiple timestamps, calculate the comprehensive timeliness coefficient through the following weighted formula: T = α·T 活跃 +(1 - α)·T 封存 ; where α is the proportion of active users, and T 活跃 and T 封存 are the timeliness coefficient corresponding to the time difference between active and archived users respectively.

9. A data asset price evaluation system for dynamically adjusting multi-dimensional coefficients based on any one of claims 1 to 8, characterized in that, Including: An evaluation formula construction module for constructing an evaluation formula; A data scarcity adjustment coefficient determination module for determining the data scarcity adjustment coefficient S; A data credibility adjustment coefficient determination module for determining the data credibility adjustment coefficient C; A data timeliness adjustment coefficient calculation module for calculating the data timeliness adjustment coefficient T; A report integrity adjustment coefficient determination module for determining the report integrity adjustment coefficient R; A comparable instance transaction price correction module for correcting the transaction price of the comparable instance; A technical report value evaluation module for comprehensively calculating the evaluation value and outputting the evaluation value of the final technical report.

10. A computer-readable storage medium, characterized in that, Stored with a computer program, when the program is executed by a processor, the method described in any one of claims 1 to 8 is implemented.

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