A data evaluation analysis method and system for data asset value

Abstract

The application discloses a data evaluation analysis method and system for data asset value, and relates to the technical field of data asset value evaluation and quantitative analysis, and the method comprises the following steps: a multidimensional environment mapping field is constructed, the mapping field forms a dynamic calculation framework by fusing a demand fluctuation quantitative model, a scene adaptation correlation model and a competition response simulation model; a data asset to be evaluated is imported into the mapping field, intrinsic attributes of the data are activated by simulating environmental pressure, and a comprehensive response index is calculated; value trajectory characteristics of the data asset in the simulation process are captured; the value trajectory characteristics are mapped to a time-effectiveness, scarcity and derivation three-dimensional space to generate a value graph; and evaluation model parameters and structures are dynamically calibrated based on real market feedback.

Description

Technical Field

[0001] This invention relates to the field of data asset valuation and quantitative analysis technology, and in particular to a data valuation and analysis method and system for data assets. Background Technology

[0002] Data assets, as a core element driving digital transformation, play a crucial role in transaction pricing, financing guarantees, financial accounting, and strategic decision-making. Currently, the industry commonly uses cost-oriented methods for data asset valuation, including cost-based methods based on historical investment, analogy methods seeking similar market cases, and discounted cash flow (DCF) methods predicting future economic returns. However, these methods all show significant shortcomings when dealing with rapidly changing market environments. Cost-based methods struggle to sensitively reflect the immediate impact of market supply and demand fluctuations on data asset value; analogy methods are constrained by the scarcity of comparable transactions and the rapid changes in market conditions, posing significant challenges to case selection and parameter adjustment; discounted cash flow methods often set their core predictive variables as fixed values, making it difficult to effectively absorb and respond to real-time market feedback, resulting in slow updates to valuation results and an inability to accurately depict the intrinsic value of data assets that fluctuates in real time with market dynamics.

[0003] The main problems with the existing assessment system's weak ability to adapt to dynamic market changes lie in the following: the information basis for assessments is mostly limited to static historical records or preset constants, lacking effective access to and integration of continuously emerging real-time market dynamic data streams; the assessment models themselves are usually rigid and lack the ability to autonomously learn and adjust their internal logic and factor importance according to market changes; the assessment process is mostly driven by discrete events, making it difficult to support high-frequency or on-demand dynamic value updates. These shortcomings result in poor timeliness and limited accuracy of assessment results, making it difficult to meet the growing demand for dynamic value assessment. Summary of the Invention

[0004] In view of this, the present invention proposes a data assessment and analysis method and system for data asset value, in order to solve the problem that existing data asset value assessment schemes cannot adapt to dynamic market changes.

[0005] The specific technical solution of this invention is as follows: A data assessment and analysis method for the value of data assets includes: Based on the business ecosystem logic chain of the target industry, a dynamic environment mapping field is created, which is interwoven with demand fluctuation space, scenario adaptation channel and competitive response path. The demand fluctuation space captures the cyclical rhythm of market behavior, the scenario adaptation channel is associated with application nodes in vertical fields, and the competitive response path quantifies the erosion boundary of alternative solutions. The data assets to be evaluated are injected into the core of the mapping field, and the inherent attributes of the data are activated through the simulated pressure transmission mechanism, so that the data quality density, information entropy distribution, and knowledge extraction rate generate synergistic resonance in the multi-dimensional field. Real-time recording of the propagation path of data resonant waves in the mapping field, extracting the following value trajectory features: the value decay critical point in the demand fluctuation space, the value fission trigger threshold in the scenario adaptation channel, and the value moat construction strength between competitive response paths; The resonance trajectory is transformed into a three-dimensional value map consisting of a time-sensitive coordinate axis, a scarcity coordinate axis, and a derived coordinate axis. The time-sensitive coordinate axis quantifies the half-life of data value, the scarcity coordinate axis marks the level of non-replicability, and the derived coordinate axis measures the potential for cross-border integration. Establish a feedback loop between the map parameters and the real economy. When market environmental variables exceed the preset tolerance threshold, automatically reconstruct the mapping field topology and achieve closed-loop calibration of the value anchor point by re-triggering the resonance effect.

[0006] Specifically, synergistic resonance is manifested as the dynamic coupling between the intrinsic attributes of data and the environmental parameters of the dynamic environment mapping field. The coupling strength is determined by the matching depth between the granularity of data elements and the scene adaptation channel. The granularity of data elements refers to the fineness of the data, and the matching depth refers to the degree of matching between the granularity of data and the optimal granularity required by a specific business scenario.

[0007] Specifically, the trajectory continuity in the value trajectory characteristics reflects the life cycle resilience of data assets, while the trajectory fluctuation amplitude characterizes the value stability. The trajectory continuity is quantified by calculating the density of effective coupling strength data points or the length of continuous effective intervals in the transmission path, while the trajectory fluctuation amplitude is characterized by calculating the standard deviation or range of the coupling strength sequence.

[0008] Specifically, the surface curvature of the three-dimensional value map corresponds to the marginal value elasticity of data assets, and the surface projection area maps the commercial conversion equivalent. The surface curvature is obtained by calculating Gaussian curvature or average curvature to reflect the sensitivity to value changes, and the surface projection area is obtained by calculating the area of ​​the projection region of the map on the reference plane to characterize the total commercial potential.

[0009] Specifically, the market environment variables in the feedback loop include the actual demand fluctuation index, the competition intensity index, and the application rate of new technologies. The preset tolerance threshold is set through historical data analysis. When the variable deviation continues to exceed the allowable error range, the reconstruction is automatically triggered. The reconstruction includes adjusting the mapping field parameters or modifying the model structure to incorporate new driving factors.

[0010] A data asset valuation and analysis system includes: The environment mapping field construction module is used to create a dynamic environment mapping field based on the business ecosystem logic chain of the target industry. This mapping field is composed of demand fluctuation space, scenario adaptation channel and competitive response path to capture market rhythm, related application nodes and quantify erosion boundaries. The data activity resonance triggering module is used to inject the data asset to be evaluated into the dynamic environment mapping field, and activate the intrinsic properties of the data to generate synergistic resonance by simulating the pressure transmission mechanism. The value trajectory capture module is used to record the propagation path of data resonant waves in the mapping field in real time and extract value trajectory features, including value decay critical point, value fission trigger threshold and value moat construction strength. The 3D map generation module is used to transform value trajectory characteristics into a 3D value map consisting of time-sensitive coordinate axes, scarcity coordinate axes, and derivative coordinate axes, in order to quantify value half-life, level of non-replicability, and cross-border integration potential. The dynamic calibration module is used to establish a feedback loop between the parameters of the three-dimensional value map and the real economy, and automatically reconstructs the dynamic environment mapping field when market environmental variables exceed the preset tolerance threshold, so as to achieve closed-loop calibration.

[0011] Specifically, the environment mapping field construction module also includes a demand fluctuation quantification submodule, a scenario adaptation correlation submodule, and a competitive response simulation submodule. The demand fluctuation quantification submodule generates a demand intensity index through time series analysis technology, the scenario adaptation correlation submodule calculates the support degree through the scenario data element adaptation matrix, and the competitive response simulation submodule simulates the value performance under competitive pressure through game theory.

[0012] Specifically, the data activity resonance triggering module calculates the dynamic interaction response between data attributes and mapping field environment parameters through a coupling strength function. The function input includes quantified values ​​of data quality density, information entropy distribution, and knowledge extraction rate, and the output is a comprehensive response index to form a response spectrum.

[0013] Specifically, the value trajectory capture module analyzes the data flow of the transmission path through feature extraction algorithms. The algorithms include change point detection to identify the value fission trigger threshold, fitting curve analysis to determine the value decay critical point, and competitive pressure maintenance rate calculation to quantify the strength of the value moat construction.

[0014] Specifically, the 3D map generation module also includes a coordinate axis quantization unit, in which the time-dependent coordinate axis unit calculates the value half-life by fitting a decay model, the scarcity coordinate axis unit calculates the non-replicability level by using a multi-factor scoring card model, and the derivative coordinate axis unit measures the cross-border fusion potential by using a cross-border potential algorithm and uses surface fitting technology to generate a continuous map surface.

[0015] The beneficial effects of this invention are as follows: 1. By integrating three sub-models—demand fluctuation quantification, scenario adaptation correlation, and competitive response simulation—a dynamically updated computational framework is formed, accurately reflecting the dynamic characteristics of the industry and providing a comprehensive environmental foundation for evaluation.

[0016] 2. Import the data assets to be evaluated into the environmental mapping field, simulate environmental stress to activate and quantify the intrinsic attributes of the data, and calculate the comprehensive response index through dynamic interaction to form the data asset response spectrum.

[0017] 3. Analyze the changes in the value characteristics of data assets during the simulation process, and extract trajectory features such as the value decay critical point, the value fission trigger threshold, and the strength of the value moat construction, so as to provide core input for value quantification.

[0018] 4. Map the value trajectory characteristics onto a three-dimensional space composed of three value dimensions: timeliness, scarcity, and derivatives, to form an intuitive and quantifiable value map that visually displays the value of data assets.

[0019] 5. Establish a closed-loop feedback system to continuously calibrate the parameters and structure of the evaluation model using real market feedback information, ensuring that the evaluation results are synchronized with the real business environment and providing more timely and accurate evaluation results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, 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.

[0021] Figure 1 This is a flowchart illustrating the data asset value assessment and analysis method of the present invention. Detailed Implementation

[0022] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0023] This invention proposes a data assessment and analysis method for data assets. The core of this method lies in constructing a structured environmental model that reflects the dynamic characteristics of the target industry. By placing the data asset to be assessed within this model for simulation calculations, its response characteristics under different environmental pressures are quantified. Based on these characteristics, multi-dimensional value indicators are constructed, and finally, a feedback mechanism is used to dynamically calibrate the assessment results. This method aims to overcome the limitations of traditional static assessment methods, providing more market-relevant and forward-looking insights into the value of data assets. Figure 1 As shown, the method of the present invention specifically includes the following implementation steps: Step 1: Based on the business ecosystem logic chain of the target industry, create a dynamic environment mapping field composed of demand fluctuation space, scenario adaptation channel, and competitive response path. The demand fluctuation space captures the cyclical rhythm of market behavior, the scenario adaptation channel associates vertical application nodes, and the competitive response path quantifies the erosion boundary of alternative solutions.

[0024] The goal of constructing a multi-dimensional environment mapping field is to establish a computational model capable of digitally representing the core dynamic characteristics of a target industry. This model is not a physical field, but rather a complex computational system composed of interrelated data structures, algorithms, and rules. In practice, this requires a deep understanding of the target industry's business, collecting and integrating multi-source data, including but not limited to historical market transaction data, user behavior logs, upstream and downstream industry chain relationship data, competitor information databases, macroeconomic indicators, policy and regulatory texts, and domain-specific knowledge graphs. Using this data, the environment mapping field is formed through the construction and fusion of the following three core sub-models: The first sub-model is the demand fluctuation quantification model, which focuses on capturing the cyclical changes and random disturbances in market demand. Its construction process includes: identifying key drivers influencing demand in the target industry, such as seasonal factors, economic cycles, consumer preference trends, and unforeseen events; collecting historical time-series data of these drivers; applying time-series analysis techniques, such as autoregressive integral moving average models, seasonal decomposition, or state-space models, to fit the historical data and establish a predictive model for demand fluctuations; the model outputs a time-varying demand intensity index sequence, which comprehensively reflects the expected level of market demand and its range of uncertainty, constituting the core quantitative expression of the demand fluctuation space.

[0025] The second sub-model is the scenario adaptation and association model, which aims to establish a quantifiable relationship between data assets and specific business application scenarios. Its construction process includes: in-depth analysis of typical business scenarios in the target industry, such as risk assessment, precision marketing, and anti-fraud in the financial sector; for each key scenario, identifying its core business processes, decision-making nodes, and the required data element types, formats, timeliness, and accuracy requirements; and using graph database technology or association matrices to construct a "scenario-data element" adaptation matrix, where rows represent different business scenarios and columns represent different data element attributes, such as structure level, real-time performance, coverage, and unique identifier existence. Matrix elements are calculated through expert scoring, historical case matching analysis, or machine learning models, such as models trained based on scenario application effect feedback, representing the degree to which specific data element attributes support a specific business scenario. This adaptation matrix and its associated rule base together constitute the scenario adaptation channel, providing a computational foundation for subsequently evaluating the value potential of data assets in specific scenarios.

[0026] The third sub-model is the competitive response simulation model, which is used to quantitatively assess the competitiveness and defensive capabilities of data assets in the face of alternative solutions. Its construction process includes: identifying other data sources, algorithm models, services, or products within the target industry that may substitute for the value of the data asset being evaluated; collecting characteristic data, performance indicators, market share, or user feedback of these alternatives; constructing a simulation environment based on game theory, multi-agent simulation, or competitive benchmarking; in this environment, different competitive intensity parameters can be set, such as the price of alternatives, performance improvement, and market penetration rate, to simulate the value performance of the data asset being evaluated under different competitive pressures, especially the boundary and speed of its value erosion. Key indicators output by the competitive response simulation model include value erosion rate and market share maintenance, quantifying the intensity of confrontation in the competitive response path.

[0027] Ultimately, the multi-dimensional environment mapping field is formed by tightly coupling the three sub-models mentioned above through data interfaces and computational processes. Specifically, the coupling can be achieved as follows: the output of the demand fluctuation quantification model, i.e., the demand intensity index, is used as one of the input parameters for the scenario adaptation correlation model and the competitive response simulation model, influencing the activity level of the scenario or the intensity of competition; the adaptation score output by the scenario adaptation correlation model serves as a key input in the competitive response simulation model to evaluate the advantages of the data asset being assessed relative to alternative solutions; the three sub-models share some basic data, such as market data and competitor data, and interact through a unified data platform. This mapping field is a dynamically updated computational framework, whose parameters and structure can be adjusted with the input of new data.

[0028] Step 2 involves injecting the data assets to be evaluated into the core of the mapping field. By simulating the pressure transmission mechanism, the inherent attributes of the data are activated, enabling the data quality density, information entropy distribution, and knowledge extraction rate to resonate synergistically in the multidimensional field. This resonance is manifested as the dynamic coupling between data attributes and mapping field environmental parameters, and the coupling strength is determined by the matching depth between the granularity of data elements and the scene channel.

[0029] The core of triggering the data activity resonance effect is to import the data asset to be evaluated into the aforementioned multi-dimensional environmental mapping field calculation model, activate and quantify the intrinsic attributes of the data asset by simulating environmental stress, and observe the interactive response of these attributes in the environmental model.

[0030] In practice, the data assets to be evaluated are first preprocessed and their features extracted. This includes data cleaning, handling missing, outlier, and duplicate values, data standardization / normalization, and extracting key metadata and statistical features, such as data size, time span, update frequency, field completeness, and primary key uniqueness. Then, the preprocessed data assets and their feature sets are used as input and injected into the environment mapping field model. This injection essentially sets the feature parameters of the data assets as input variables for the environment mapping field computational model. Next, the simulation computation engine of the environment mapping field is activated, running the simulated pressure transmission mechanism. This mechanism is implemented as follows: the environment mapping field model generates one or more sets of simulated environment parameter vectors based on its current state, which is jointly determined by the states of three sub-models: demand fluctuation, scenario adaptation, and competitive response. These vectors represent the market environment, scenario demands, and competitive landscape under different assumptions. For example, scenarios such as peak / valley periods of demand, high-frequency triggering of specific scenarios, and the emergence of highly competitive alternatives can be simulated. For each set of simulated environment parameters, the computational model drives the intrinsic attributes of the data assets to perform calculations and interactions under environmental pressure.

[0031] Here, it is necessary to clearly define and quantify the inherent attributes of data: data quality density can be quantified by calculating the proportion of valid information in the dataset, such as non-empty, compliant, and accurate records, and combining it with their business importance weights; information entropy distribution is used to measure the uncertainty and information content of data, and its Shannon entropy can be calculated for key fields or records; knowledge extraction rate measures the efficiency and potential of extracting structured knowledge from data, and can be evaluated by simulating or actually applying knowledge extraction algorithms such as entity recognition, relation extraction, event detection accuracy, recall, or F1 score, or approximated by calculating the density of potential knowledge points in the data, such as the number of identifiable entities or relations per unit of data volume.

[0032] In the environment mapping field model, these quantified data attributes, namely mass density, entropy distribution characteristic value, and knowledge extraction rate, do not exist in isolation, but interact dynamically with the environmental parameters of the mapping field, such as demand intensity, scenario adaptability requirements, and competitive pressure intensity.

[0033] By utilizing the effects of coordinated resonance or dynamic coupling, the comprehensive response index of these properties under specific environmental pressures can be calculated. For example, a coupling strength function can be defined: ; Here, f can be a weighted summation function, the output of a rule-based engine, or the output of a trained machine learning model such as a neural network. The output value of this function characterizes the overall activity or responsiveness of the data asset in the current simulated environment. In particular, when calculating the coupling strength, the fit depth between the data element granularity and the scene channel can be included as an important weighting factor or direct input variable in the calculation of the coupling strength function f. Data element granularity refers to the fineness of the data (e.g., individual record level vs. aggregate statistical level), while fit depth refers to the degree of matching between this granularity and the optimal granularity required by a specific scene as defined in the scene adaptation association model (e.g., user profiling scenarios require individual-level data, while macro trend analysis may require aggregated data).

[0034] This step involves running multiple rounds of simulations with different combinations of environmental parameters to obtain a series of coupling strength values, forming the response spectrum of the data asset in the environmental mapping field.

[0035] Step 3: Record the propagation path of the data resonant wave in the mapped field in real time, and extract trajectory features including: The value decay threshold within the demand fluctuation space, the value fission trigger threshold in the scenario adaptation channel, and the strength of the value moat construction between competitive response paths; among them, trajectory continuity reflects the life cycle resilience of data assets, and trajectory fluctuation amplitude characterizes value stability.

[0036] Capturing value resonance trajectories aims to analyze and record changes in value-related characteristics exhibited by data assets during environmental mapping field simulation.

[0037] In practical implementation, during the operation of the aforementioned simulated pressure transmission mechanism, key output data from each simulation calculation are recorded and stored in real time, particularly the instantaneous values ​​of data asset attributes (quality density, entropy, knowledge extraction rate), the calculated coupling strength values, and the environmental parameter status at that time (demand intensity, adaptability requirements, competitive pressure). These data points are arranged in chronological order or simulation round order, forming the original transmission path data stream. From this data stream, the following core value trajectory features are calculated and output using a specific feature extraction algorithm: 1) Within the space of demand fluctuations, the value decay critical point refers to the point at which, when the intensity of environmental demand declines to a certain threshold, the coupling strength representing the value response of data assets decreases sharply or becomes insignificant. Methods for extracting this critical point include: analyzing the functional relationship between coupling strength and demand intensity; finding points where the function derivative changes significantly, such as exceeding a preset gradient threshold, by fitting curves; or finding the point where, when demand intensity decreases, the coupling strength first falls below a preset effective value threshold. This critical point quantifies the value resilience of data assets during market downturns.

[0038] 2) In the scenario adaptation channel, the value fission trigger threshold refers to the point at which the coupling strength, i.e., the value response, exhibits a non-linear leap when the data asset's adaptation to a specific scenario reaches a certain high level. Methods for extracting this threshold include: analyzing the functional relationship between coupling strength and scenario adaptation; identifying inflection points or abrupt changes on the function curve, for example, using change point detection algorithms such as the PELT algorithm or finding points where the coupling strength growth rate significantly increases with increasing adaptation, such as exceeding a preset growth rate threshold; this threshold identifies the critical adaptation condition for the data asset to generate high value in a specific scenario.

[0039] 3) In competitive response paths, the strength of the value moat refers to the ability of a data asset to maintain stable coupling strength when facing competitive pressure. This strength is calculated by quantifying the rate of decrease or maintenance rate of coupling strength as competitive pressure increases, or by calculating the percentage of coupling strength that can still be maintained when competitive pressure reaches a preset high level. The higher this value, the stronger the competitive defense capability of the data asset.

[0040] Furthermore, trajectory continuity reflects the lifecycle resilience of data assets. This involves analyzing whether the coupling strength sequence experiences prolonged interruptions (e.g., coupling strength falling below the minimum measurable value for multiple consecutive simulation rounds) or drastic jumps throughout the simulation time span or within the range of environmental parameter changes. Continuity indicators can be quantified by calculating the density of effective coupling strength data points, the average length of continuous effective intervals, or the maximum length. Trajectory volatility characterizes value stability. Specifically, this involves calculating the standard deviation, variance, or maximum-minimum-range of the coupling strength sequence, or the range of coupling strength variation within a specific range of environmental variable changes. Smaller volatility indicates greater value stability. These extracted trajectory features, including critical points, thresholds, moat strength, continuity, and volatility, are the core inputs for subsequent value quantification.

[0041] Step 4: Transform the resonance trajectory into a three-dimensional value map consisting of a time-sensitive coordinate axis, a scarcity coordinate axis, and a derived coordinate axis: the time-sensitive coordinate axis is used to quantify the half-life of data value, the scarcity coordinate axis is used to mark the level of non-replicability, and the derived coordinate axis is used to measure the potential for cross-border integration; the curvature of the map surface corresponds to the marginal value elasticity of data assets, and the surface projection area maps the commercial conversion equivalent.

[0042] Generating a three-dimensional value map involves mapping the captured value trajectory features into a three-dimensional space composed of three key value dimensions using specific transformation rules and computational models, thus forming an intuitive and quantifiable value map. In practice, the three coordinate axes of the three-dimensional value map and their quantification methods are first defined: The time-sensitivity axis is used to quantify the half-life of data value, that is, the time required for data value to decay to half of its initial value. This value is calculated based on information such as the critical point of value decay within the demand fluctuation space and the continuity of the trajectory. Alternatively, a more complex decay model, such as an exponential decay model, can be used to fit the relationship between the decline in demand intensity and value decay, and the time required for the value to halve can be calculated, or simulations of cycles can be performed before converting the result to real-time. The larger the value of this axis, the longer the data value is retained.

[0043] The scarcity axis is used to mark the non-replicability level of data assets. This level is calculated by comprehensively considering the uniqueness of the data source, acquisition cost, compliance barriers, technological thresholds, and the strength of the value moat built in the competitive response path. In practice, a multi-factor scoring card model can be designed: for example, assessing the data source (exclusive authorization = 5 points, multiple public sources = 1 point), acquisition cost (extremely high = 5 points, extremely low = 1 point), compliance complexity (extremely high barriers = 5 points, no barriers = 1 point), and technological replicability (extremely difficult to replicate = 5 points, extremely easy to replicate = 1 point), and combining this with the strength of the value moat (normalized to 1-5 points), to calculate a weighted average score as the scarcity level. The higher the value on this axis, the more unique and difficult the data is to replace.

[0044] The derived coordinate axis is used to measure the cross-industry integration potential of data assets, that is, the likelihood of their application in non-industry fields or the generation of new business models. This potential is calculated based on the knowledge extraction rate, the diversity of adaptation scenarios identified in the scenario adaptation channel, especially cross-domain scenarios, and the structured nature and semantic richness of the data assets. A specific algorithm could be: ; Where a, b, and c are weighting coefficients, the industry span index can be calculated by the number or difference of industries belonging to different scenarios in the scenario adaptation association model, and semantic richness can be evaluated by ontology coverage or the number of entity / relationship types. The larger the value of this axis, the broader the potential for cross-industry application of the data.

[0045] Using the quantified values ​​of the data asset to be evaluated on the three coordinate axes—namely, its validity value, scarcity level, and cross-border potential value—as coordinates, a point can be determined in three-dimensional space. To form a continuous graph surface, it is usually necessary to evaluate the value coordinates of the same data asset at different points in time or under different environmental assumptions. This is obtained by repeatedly running the aforementioned steps, or by evaluating the value coordinates of a set of related data assets and then using surface fitting techniques, such as polynomial surface fitting, spline interpolation, or Kriging, to generate a smooth surface that passes through or approximates these points. The curvature of the graph surface corresponds to the marginal value elasticity of the data asset; that is, the curvature of the surface at a certain point (which can be obtained by calculating the Gaussian curvature or average curvature of that point) reflects the sensitivity (i.e., elasticity) of the data value (reflected by the overall coordinate position) to changes when there are small changes in inputs (such as data maintenance costs and application development investment) near that point. High curvature areas mean rapid changes in marginal value and high elasticity; low curvature areas mean gradual changes and low elasticity. The projected area of ​​a data graph surface onto a reference plane, such as the timeliness-scarcity plane, maps to its commercial conversion equivalent. In other words, the size of the projected area, obtained by calculating the area of ​​the two-dimensional projection region of the surface, can approximately represent the total potential commercial value or scale of the data asset under a specific combination of value dimensions, such as timeliness and scarcity. The larger the area, the greater the total commercial potential.

[0046] Step 5: Establish a feedback loop between the map parameters and the real economy. When market environmental variables exceed the preset tolerance threshold, automatically reconstruct the mapping field topology and re-trigger the resonance effect to achieve closed-loop calibration of the value anchor point, ensuring that the evaluation results continue to conform to real business rules.

[0047] The core of dynamically calibrating value anchors is to establish a closed-loop feedback system that uses real-world market feedback information to continuously calibrate the parameters and structure of the aforementioned value assessment model (especially the environmental mapping field and value map generation rules) to ensure that the assessment results are synchronized with the real business environment.

[0048] In practical implementation, the first step is to establish a feedback loop between the graph parameters and the real economy. This includes defining a set of key, observable real economy indicators as feedback signals. These indicators should be closely related to the value of the data assets. For example, in the financial sector, these could be the Sharpe ratio improvement or risk loss reduction rate of trading strategies using the data assets; in the marketing sector, they could be the ad click-through rate improvement, conversion rate improvement, or customer acquisition cost reduction rate; and in the operations sector, they could be the percentage efficiency improvement or cost savings. A data collection pipeline should be established to continuously collect the actual performance data of these feedback indicators. A feedback comparison mechanism should be set up: the value enhancement potential predicted based on the value graph, such as the incremental increase in a certain business conversion equivalent predicted through graph parameters, should be compared with the actual observed feedback indicator values ​​(i.e., the actual business conversion effect) during the same period to calculate the prediction deviation. When market environmental variables, such as the observed demand fluctuation index, competition intensity index, and new technology application rate, exceed the preset tolerance threshold (e.g., the actual demand fluctuation exceeds ±15% of the model's prediction range), or a new, highly competitive alternative not included in the model emerges, or when the aforementioned prediction deviations consistently exceed the allowable error range (e.g., deviations exceeding 10% for three consecutive calibration cycles), the calibration process is triggered. Automatic reconstruction of the mapping field topology includes: parameter calibration: using optimization algorithms such as gradient descent, genetic algorithms, and Bayesian optimization to adjust the internal parameters of the sub-models (demand fluctuation, scenario adaptation, competitive response) in the environmental mapping field, such as model coefficients, weights, and thresholds, so that the model re-achieves optimal fit on new market data or feedback data. If parameter calibration is insufficient to compensate for the deviation, it may be necessary to modify the model structure. For example, introducing new driving factor variables in the demand fluctuation model; adding new business scenario nodes or modifying adaptation rules in the scenario adaptation model; adding newly identified alternatives and their features in the competitive response model. This involves model version iteration. New market data and feedback data are incorporated into the model's training set or real-time input stream to ensure the model learns the latest patterns. After reconstruction, the system automatically re-runs the aforementioned steps, starting from step 2 which triggers the data activity resonance effect. It then uses the updated environmental mapping field model to reassess the value of data assets and generate a new three-dimensional value map. This process achieves closed-loop calibration of the value anchor, i.e., the core benchmark of the assessment results. The system can be set up for regular, such as monthly, quarterly, or event-triggered automatic calibration processes to ensure that the assessment method continuously adapts to market changes. Through this dynamic calibration mechanism, the method of this invention can effectively overcome the rigidity of static assessment models, providing more timely and accurate data asset value assessment results.

[0049] This invention also proposes a data assessment and analysis system for data asset value, comprising: The environment mapping field construction module is used to create a dynamic environment mapping field based on the business ecosystem logic chain of the target industry. This mapping field is composed of demand fluctuation space, scenario adaptation channel and competitive response path to capture market rhythm, related application nodes and quantify erosion boundaries. The data activity resonance triggering module is used to inject the data asset to be evaluated into the dynamic environment mapping field, and activate the intrinsic properties of the data to generate synergistic resonance by simulating the pressure transmission mechanism. The value trajectory capture module is used to record the propagation path of data resonant waves in the mapping field in real time and extract value trajectory features, including value decay critical point, value fission trigger threshold and value moat construction strength. The 3D map generation module is used to transform value trajectory characteristics into a 3D value map consisting of time-sensitive coordinate axes, scarcity coordinate axes, and derivative coordinate axes, in order to quantify value half-life, level of non-replicability, and cross-border integration potential. The dynamic calibration module is used to establish a feedback loop between the parameters of the three-dimensional value map and the real economy, and automatically reconstructs the dynamic environment mapping field when market environmental variables exceed the preset tolerance threshold, so as to achieve closed-loop calibration.

[0050] Specifically, the environment mapping field construction module also includes a demand fluctuation quantification submodule, a scenario adaptation correlation submodule, and a competitive response simulation submodule. The demand fluctuation quantification submodule generates a demand intensity index through time series analysis technology, the scenario adaptation correlation submodule calculates the support degree through the scenario data element adaptation matrix, and the competitive response simulation submodule simulates the value performance under competitive pressure through game theory.

[0051] Specifically, the data activity resonance triggering module calculates the dynamic interaction response between data attributes and mapping field environment parameters through a coupling strength function. The function input includes quantified values ​​of data quality density, information entropy distribution, and knowledge extraction rate, and the output is a comprehensive response index to form a response spectrum.

[0052] Specifically, the value trajectory capture module analyzes the data flow of the transmission path through feature extraction algorithms. The algorithms include change point detection to identify the value fission trigger threshold, fitting curve analysis to determine the value decay critical point, and competitive pressure maintenance rate calculation to quantify the strength of the value moat construction.

[0053] Specifically, the 3D map generation module also includes a coordinate axis quantization unit, in which the time-dependent coordinate axis unit calculates the value half-life by fitting a decay model, the scarcity coordinate axis unit calculates the non-replicability level by using a multi-factor scoring card model, and the derivative coordinate axis unit measures the cross-border fusion potential by using a cross-border potential algorithm and uses surface fitting technology to generate a continuous map surface.

[0054] The beneficial effects of this invention are as follows: 1. By integrating three key sub-models—demand fluctuation quantification, scenario adaptation correlation, and competitive response simulation—a multi-dimensional environmental mapping field computation framework capable of dynamic updates was constructed. This framework can more comprehensively and accurately capture the complex dynamic characteristics of the target industry, laying a solid and realistic environmental foundation for subsequent data asset value assessment, and effectively overcoming the limitations of traditional methods' single and static environmental modeling.

[0055] 2. This invention places the data asset to be evaluated within a constructed environmental mapping field, simulating various environmental stresses to activate and quantify the data's inherent key attributes (such as quality density, information entropy distribution, and knowledge extraction rate). By calculating the dynamic interaction between these attributes and environmental parameters, a comprehensive response index is derived, ultimately forming a response set reflecting the data asset's performance characteristics under different environmental stresses. This process reveals the true activity and adaptability of data assets in complex environments.

[0056] 3. Through in-depth analysis of changes in data asset value-related characteristics during the simulation process, this method can capture and extract key value trajectory features. These features include the value decay threshold during market downturns, the value fission trigger threshold in specific scenarios, and the strength of the value moat built to resist competition. These features provide core and reliable input for subsequent accurate and multi-dimensional value quantification.

[0057] 4. This invention innovatively maps the extracted value trajectory features onto a visualized value model composed of three key dimensions: timeliness, scarcity, and derivative potential, generating an intuitive and quantifiable value map. This map clearly displays the core value dimensions of data assets and their interrelationships, making the evaluation results more comprehensive and easier to understand and use for decision-making.

[0058] 5. By establishing a closed-loop calibration mechanism based on real market feedback, this method can continuously and dynamically adjust the parameters and structure of the evaluation model (especially the environmental mapping field and value map generation rules). This ensures that the evaluation results keep pace with market changes, significantly improves the timeliness and long-term accuracy of the evaluation, and effectively solves the problem of static evaluation models becoming ineffective with environmental changes.

[0059] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data assessment and analysis method for the value of data assets, characterized in that, include: Based on the business ecosystem logic chain of the target industry, a dynamic environment mapping field is created, which is interwoven with demand fluctuation space, scenario adaptation channel and competitive response path. The demand fluctuation space captures the cyclical rhythm of market behavior, the scenario adaptation channel is associated with application nodes in vertical fields, and the competitive response path quantifies the erosion boundary of alternative solutions. The data assets to be evaluated are injected into the core of the mapping field, and the inherent attributes of the data are activated through the simulated pressure transmission mechanism, so that the data quality density, information entropy distribution, and knowledge extraction rate generate synergistic resonance in the multi-dimensional field. Real-time recording of the propagation path of data resonant waves in the mapping field, extracting the following value trajectory features: the value decay critical point in the demand fluctuation space, the value fission trigger threshold in the scenario adaptation channel, and the value moat construction strength between competitive response paths; The resonance trajectory is transformed into a three-dimensional value map consisting of a time-sensitive coordinate axis, a scarcity coordinate axis, and a derived coordinate axis. The time-sensitive coordinate axis quantifies the half-life of data value, the scarcity coordinate axis marks the level of non-replicability, and the derived coordinate axis measures the potential for cross-border integration. Establish a feedback loop between the map parameters and the real economy. When market environmental variables exceed the preset tolerance threshold, automatically reconstruct the mapping field topology and achieve closed-loop calibration of the value anchor point by re-triggering the resonance effect.

2. The data assessment and analysis method for data asset value as described in claim 1, characterized in that, The aforementioned synergistic resonance is manifested as the dynamic coupling between the intrinsic attributes of data and the environmental parameters of the dynamic environment mapping field. The coupling strength is determined by the matching depth between the granularity of data elements and the scene adaptation channel. Here, the granularity of data elements refers to the fineness of the data, and the matching depth refers to the degree of matching between the granularity of data and the optimal granularity required by a specific business scenario.

3. The data assessment and analysis method for data asset value as described in claim 1, characterized in that, The trajectory continuity in the value trajectory characteristics reflects the life cycle resilience of data assets, and the trajectory fluctuation amplitude characterizes the value stability. The trajectory continuity is quantified by calculating the density of effective coupling strength data points or the length of continuous effective intervals in the transmission path, and the trajectory fluctuation amplitude is characterized by calculating the standard deviation or range of the coupling strength sequence.

4. The data assessment and analysis method for data asset value as described in claim 1, characterized in that, The surface curvature of the three-dimensional value map corresponds to the marginal value elasticity of the data asset, and the surface projection area maps the commercial conversion equivalent. The surface curvature is obtained by calculating the Gaussian curvature or the average curvature to reflect the sensitivity to value changes, and the surface projection area is obtained by calculating the area of ​​the projection region of the map on the reference plane to characterize the total commercial potential.

5. The data assessment and analysis method for data asset value as described in claim 1, characterized in that, The market environment variables in the feedback loop include the actual demand fluctuation index, the competition intensity index, and the application rate of new technologies. The preset tolerance threshold is set through historical data analysis. When the variable deviation continues to exceed the allowable error range, reconstruction is automatically triggered. Reconstruction includes adjusting the mapping field parameters or modifying the model structure to incorporate new driving factors.

6. A data assessment and analysis system for data asset value, characterized in that, include: The environment mapping field construction module is used to create a dynamic environment mapping field based on the business ecosystem logic chain of the target industry. This mapping field is composed of demand fluctuation space, scenario adaptation channel and competitive response path to capture market rhythm, related application nodes and quantify erosion boundaries. The data activity resonance triggering module is used to inject the data asset to be evaluated into the dynamic environment mapping field, and activate the intrinsic properties of the data to generate synergistic resonance by simulating the pressure transmission mechanism. The value trajectory capture module is used to record the propagation path of data resonant waves in the mapping field in real time and extract value trajectory features, including value decay critical point, value fission trigger threshold and value moat construction strength. The 3D map generation module is used to transform value trajectory characteristics into a 3D value map consisting of time-sensitive coordinate axes, scarcity coordinate axes, and derivative coordinate axes, in order to quantify value half-life, level of non-replicability, and cross-border integration potential. The dynamic calibration module is used to establish a feedback loop between the parameters of the three-dimensional value map and the real economy, and automatically reconstructs the dynamic environment mapping field when market environmental variables exceed the preset tolerance threshold, so as to achieve closed-loop calibration.

7. The data asset value assessment and analysis system as described in claim 6, characterized in that, The environment mapping field construction module also includes a demand fluctuation quantification submodule, a scenario adaptation association submodule, and a competitive response simulation submodule. The demand fluctuation quantification submodule generates a demand intensity index through time series analysis technology, the scenario adaptation association submodule calculates the support degree through the scenario data element adaptation matrix, and the competitive response simulation submodule simulates the value performance under competitive pressure through game theory.

8. The data asset value assessment and analysis system as described in claim 6, characterized in that, The data activity resonance triggering module calculates the dynamic interaction response between data attributes and mapping field environment parameters through a coupling strength function. The function input includes quantified values ​​of data quality density, information entropy distribution, and knowledge extraction rate, and the output is a comprehensive response index to form a response spectrum.

9. The data asset value assessment and analysis system as described in claim 6, characterized in that, The value trajectory capture module analyzes the transmission path data flow through feature extraction algorithms. The algorithms include change point detection to identify the value fission trigger threshold, fitting curve analysis to determine the value decay critical point, and competitive pressure maintenance rate calculation to quantify the strength of the value moat construction.

10. The data asset value assessment and analysis system as described in claim 6, characterized in that, The three-dimensional map generation module also includes a coordinate axis quantization unit, wherein the time-dependent coordinate axis unit calculates the value half-life by fitting a decay model, the scarcity coordinate axis unit calculates the non-replicability level by a multi-factor scoring card model, and the derivative coordinate axis unit measures the cross-border fusion potential by a cross-border potential algorithm and generates a continuous map surface using surface fitting technology.

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