Power dispatch-oriented digital asset value evaluation method and system

Abstract

The application discloses a kind of digital asset value evaluation method and system for power dispatching.The method first obtains the digital asset set under target dispatching scene, then determines its intrinsic attribute based on power system physical topology and operation rules respectively, and determines its external value characteristics based on power market environment and dispatching decision demand.Next, the model is used to calculate the value of each digital asset in combination with real-time and historical dispatching operation data, and output the basic value score.Further, the comprehensive value evaluation result and value label are generated according to the score in combination with preset rules and adjustment factor.Finally, the evaluation result and label are applied to the power dispatching auxiliary decision support system, providing quantitative basis for dispatching plan optimization, data asset internal pricing and external transaction.The application realizes accurate, dynamic and business value evaluation of power dispatching digital assets, effectively supports the value release and efficient use of data elements.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching, and more specifically, to a method and system for assessing the value of digital assets for power dispatching. Background Technology

[0002] With the deepening of the digital transformation of the power system, the dispatching business has generated massive amounts of data, models, and documents, constituting valuable digital assets. These assets cover all dimensions of information, from real-time telemetry and historical statistics to simulation parameters and dispatching procedures, and are core elements supporting the safe, economical, and efficient operation of the power grid. However, current management of these digital resources is mostly focused on storage, backup, and basic quality control, while their intrinsic value and external utility have long remained in a "black box" state, failing to be effectively quantified and made explicit.

[0003] Existing data value assessment methods are mostly general frameworks, lacking deep integration with the highly reliable, real-time, and complex business coupled field of power dispatch. General methods often ignore the unique physical constraints, operating rules, and market mechanisms of the power grid, failing to accurately characterize core value dimensions such as the correlation of key grid sections, the timeliness requirements of dispatch decisions, and the differences in data reuse between security verification and market clearing. This results in assessment results that are disconnected from actual dispatch business needs and are difficult to guide practice.

[0004] Meanwhile, existing assessment models are mostly static, offline analysis tools that cannot be integrated into real-time dispatch and operation environments. The power grid state changes rapidly, and the value of digital assets fluctuates dynamically with changes in system load, renewable energy output, network topology, and market prices. Static assessments cannot capture this dynamic correlation. For example, during power grid failures or periods of severe renewable energy fluctuations, the value of relevant monitoring data and predictive models can increase dramatically, and conventional assessment methods cannot reflect this value shift in real time.

[0005] Furthermore, there are gaps in the current value assessment and application processes. Assessment results are often presented as independent reports, failing to form a closed loop with scheduling support decision-making systems, internal resource management processes, and potential data element marketization mechanisms. This leads to a separation between the value discovery process and the value realization process, making it difficult to directly translate assessment work into a basis for optimizing scheduling decisions, conducting cost-benefit accounting, or supporting data product transactions, thus limiting the release of the potential of digital assets as a factor of production.

[0006] In summary, existing technologies have significant shortcomings in three key areas: how to achieve accurate value assessment deeply integrated with power dispatching operations, how to achieve dynamic and real-time perception of digital asset value, and how to establish a closed loop for value transformation from assessment to business applications. Therefore, there is an urgent need for a digital asset value assessment method and system for power dispatching to address these problems. Summary of the Invention

[0007] This application provides a digital asset valuation method and system for power dispatching, which at least addresses the problems existing in the prior art.

[0008] A first aspect of this application provides a method for assessing the value of digital assets for power dispatching, comprising the following steps:

[0009] S1. Obtain the set of digital assets under the target power dispatching business scenario;

[0010] S2. Based on the physical topology and operating rules of the power system, determine the intrinsic attributes of each digital asset in the digital asset set;

[0011] S3. Based on the power market environment and dispatch decision-making needs, determine the external value characteristics of each digital asset in the digital asset set;

[0012] S4. Based on intrinsic attributes and external value characteristics, construct a multi-dimensional value assessment model for the power dispatching field;

[0013] S5. Using a multi-dimensional value assessment model, combined with real-time and historical scheduling and operation data, calculate the value of each digital asset in the digital asset set and output a basic value score.

[0014] S6. Based on the basic value score, combined with the preset value level classification rules and adjustment factors, generate a comprehensive value assessment result and value tag for each digital asset.

[0015] S7. Apply the comprehensive value assessment results and value tags to the power dispatch auxiliary decision support system to provide quantitative basis for power dispatch plan optimization, internal pricing of data assets, and external transactions.

[0016] This application establishes, for the first time, a systematic digital asset valuation system in the field of power dispatching by constructing a closed-loop process encompassing intrinsic attribute analysis, external feature identification, model building, dynamic calculation, comprehensive grading, and business application. Its beneficial effect lies in fundamentally solving the problems of ambiguity and difficulty in quantifying the value of digital assets, shifting the valuation from a general framework to a professional practice deeply coupled with the physical characteristics, operating rules, and market mechanisms of the power grid, and laying a methodological foundation for realizing the asset-based management and value-driven application of data.

[0017] In some embodiments of this application, determining the intrinsic properties in step S2 further includes:

[0018] S21. Correlation Analysis: Analyze the metadata and content of digital assets and establish a mapping relationship between them and the physical components, topology, and operating status of the power grid;

[0019] S22. Data quality assessment: Evaluate structured operational data from four dimensions: completeness, accuracy, timeliness, and consistency.

[0020] Among them, integrity is calculated based on the data missing rate; accuracy is measured by the deviation rate verified by reliable data sources or physical laws; timeliness is determined based on the delay from the data generation time to the evaluation time and its attenuation function on the current scheduling application; consistency is determined by the logical contradiction rate of cross-system or cross-time period data.

[0021] S23. Cost Accounting: Track and calculate the computing resources, storage resources, human resources costs, and management costs consumed in the generation, collection, storage, cleaning, and maintenance of digital assets throughout their entire lifecycle, and convert them into quantifiable cost values.

[0022] This application transforms abstract intrinsic value into calculable technical indicators by precisely defining three intrinsic attributes—relevance, data quality, and cost—and their quantification methods. This ensures that the starting point of value assessment (intrinsic attributes) is closely anchored to the inherent laws of the power system. In particular, by using correlation analysis to directly link asset value with the core elements of power grid safety and stability, it solves the problem that general assessment methods cannot reflect the unique fundamental value of power data, and significantly improves the industry professionalism and objectivity of the assessment results.

[0023] In some embodiments of this application, determining the external value characteristics in step S3 further includes:

[0024] S31. Demand Intensity Analysis: Investigate and analyze the degree of dependence of various scheduling decision-making tasks on specific digital assets at different time scales, including day-ahead scheduling planning, real-time scheduling control, and post-event analysis and evaluation.

[0025] S32. Reuse Potential Assessment: Assess the universality and adaptability of digital assets in different dispatching business scenarios such as power generation planning, load forecasting, renewable energy consumption, safety verification, and ancillary service market clearing.

[0026] S33. Compliance and Sensitivity Rating: Based on the Data Security Law, the Power Industry Data Classification and Grading Guidelines, and the company's internal data governance standards, digital assets are classified into sensitivity levels. Sensitive information involving key grid coordinates, real-time operation control commands, and undisclosed markets is identified. The possibility and potential risks of using such information for value exchange after desensitization within a controllable range are assessed.

[0027] This application characterizes external value features from three dimensions: demand intensity, reuse potential, and compliance. It links asset value with the dynamically changing business environment and market constraints, enabling value assessment to be flexibly adjusted according to scheduling decision-making scenarios, time scales, and data governance requirements. This not only reflects the immediate utility and potential scalability of assets in the business, but also avoids security and compliance risks in data circulation in advance, thereby solving the problem that static assessment cannot adapt to business dynamics and compliance requirements.

[0028] In some embodiments of this application, the construction of a multidimensional value assessment model for the power dispatching field in step S4 specifically involves: using the analytic hierarchy process combined with the entropy weight method to determine the weights of each dimension;

[0029] Among them, the analytic hierarchy process (AHP) is used to synthesize the subjective importance judgments of domain experts on the relevance of assets and the intensity of demand, construct a judgment matrix and calculate subjective weights; the entropy weight method calculates objective weights based on the dispersion of the indicator values ​​of each dimension in historical evaluation data; finally, the subjective weights and objective weights are linearly combined and optimized, and the comprehensive weight coefficient of each value dimension is obtained through a weighted fusion algorithm.

[0030] This application employs a combined subjective and objective weighting strategy, integrating the analytic hierarchy process (AHP) and entropy weighting, effectively blending domain expert experience with objective statistical patterns from historical data. This avoids the arbitrariness of purely subjective weighting while preventing purely objective weighting from deviating from business priorities. This ensures that the weight settings of the constructed multidimensional value assessment model are both business-oriented and mathematically rigorous, providing crucial support for the scientific validity and credibility of the core assessment algorithm.

[0031] In some embodiments of this application, step S4 further includes: introducing fuzzy comprehensive evaluation logic to process qualitative indicators in the weighted fusion algorithm; for the characteristics of data quality standardization and compliance sensitivity rating, using membership function to transform them into distributions on different evaluation levels, and then synthesizing them with the evaluation values ​​of quantitative indicators under a unified fuzzy operation framework, and finally obtaining quantitative value factors through defuzzification to handle the inherent uncertainty and fuzziness in the evaluation process.

[0032] This application introduces fuzzy comprehensive evaluation logic to process qualitative indicators. Through membership functions and fuzzy operations, it technically handles fuzzy concepts in the evaluation that are difficult to quantify precisely, such as normativity and sensitivity. It rationally integrates these concepts into the overall quantitative evaluation framework, which enhances the model's ability to accommodate uncertainties in the real world, makes the evaluation results more consistent with the complex judgments in actual business, and improves the practicality and robustness of the evaluation system.

[0033] In some embodiments of this application, step S5, which utilizes a multidimensional value assessment model and combines real-time and historical scheduling and operation data to calculate the value of each digital asset in the digital asset set and output a basic value score, further includes:

[0034] S51. Based on historical scheduling and operation data, analyze the value contribution pattern of each digital asset under similar working conditions, and establish the historical benchmark interval of its basic value score and the historical pattern calculation results of the dynamic adjustment coefficient.

[0035] S52. Access real-time dispatch and operation data stream to perceive the current operating status, safety margin, and market price of the power grid in real time;

[0036] S53. When the power grid is identified to be in a specific state, the value dimension that is strongly correlated with the specific state is dynamically activated and the weight is calculated using a multi-dimensional value assessment model to obtain the real-time state perception calculation result.

[0037] S54. Integrate the historical pattern calculation results with the real-time status perception calculation results to output a basic value score that reflects both the value pattern of the asset and meets the current immediate needs of the power grid.

[0038] Among them, the specific state includes at least one of the following: the transient and recovery state after a grid fault or disturbance, the ramp-up state of drastic fluctuations in renewable energy power, the operation state of heavy load on key transmission sections or equipment, the abnormal state of system frequency or voltage, and the anticipated state of recovery and dispatch after the execution of automatic restart or power outage contingency plans.

[0039] This application introduces a dynamic value calculation method that integrates historical patterns and real-time state perception. By sensing specific states of the power grid in real time and dynamically activating relevant value dimensions, the asset value scoring can respond in real time to emergency changes in the power grid's operating conditions. This solves the key problem of the lag in static model assessment, ensuring that the value of key data assets is reflected most sensitively when the power grid needs it most, and greatly improving the timeliness and context relevance of the assessment.

[0040] In some embodiments of this application, the preset value level classification rules and adjustment factors in step S6 include: classifying assets into four levels—strategic assets, business assets, support assets, and archived assets—based on the range of basic value scores; the adjustment factors include a time decay factor, a scenario gain factor, and a scarcity factor; wherein, the time decay factor is set according to the time sensitivity of scheduling business, so that the value of real-time data decays over time; the scenario gain factor is activated when an asset is applied to a specific emergency scheduling scenario with high security risk or high economic benefit, temporarily increasing its value weight; the scarcity factor is adjusted according to the uniqueness of the source of similar substitutable assets.

[0041] This application achieves refined classification management and dynamic adjustment of asset value by establishing clear four-level classification rules and three types of dynamic adjustment factors: time, scenario, and scarcity. This not only provides a basis for asset management strategies, but more importantly, through the adjustment factor mechanism, it enables asset value to automatically adjust according to the passage of time, the urgency of the scenario, and the scarcity of alternative resources. This achieves lifecycle management and scenario-based flexible assessment of asset value, supporting differentiated resource allocation strategies.

[0042] In some embodiments of this application, step S7, which provides a quantitative basis for power dispatching plan optimization, internal pricing of data assets, and external transactions, specifically includes: dynamically associating value tags as metadata with digital assets, and constructing a data asset value map based on comprehensive value assessment results; in power dispatching plan optimization, presenting dispatchers with a priority view of assets of different value levels, and automatically recommending or prioritizing high-value digital assets as decision input when formulating or adjusting dispatching plans; based on the value map and preset internal costs, realizing cross-departmental data asset usage cost allocation and benefit accounting to complete the internal pricing of data assets; generating a standardized data asset value report containing value assessment details, compliance certificates, and potential application scenarios, providing a reliable pricing benchmark and description of the transaction target for data product transactions or cooperation with external institutions in a controlled environment.

[0043] This application specifies the application methods of the assessment results in three major scenarios: plan optimization, internal pricing, and external transactions. It completely bridges the last mile from "value assessment" to "value realization." By constructing value maps, recommending high-value assets, supporting cost accounting, and generating standard reports, it directly transforms quantitative assessment results into actionable decision support for dispatchers, executable accounting basis for management departments, and pricing benchmarks for market transactions, forming a complete value transformation closed loop and releasing the productivity of data elements.

[0044] A second aspect of this application provides a digital asset valuation system for power dispatch, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described digital asset valuation methods for power dispatch.

[0045] A third aspect of this application provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements a digital asset valuation method for power dispatch as described above.

[0046] In the above embodiments, the system claims protect the hardware architecture and software module entities that implement the above method. Through the synergistic effect of the various modules, efficient, reliable, and scalable physical support is provided for the implementation of the method. Attached Figure Description

[0047] Figure 1 A flowchart illustrating a digital asset valuation method for power dispatching provided in an embodiment of this application;

[0048] Figure 2 A flowchart illustrating the determination of intrinsic properties in step S2 of an embodiment of this application;

[0049] Figure 3 A flowchart illustrating the determination of external value in step S3 of an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0052] To make the purpose, technical solution, and advantages of this application clearer, the following will be described in conjunction with the appendix. Figure 1-3 The following is an explanation using specific examples.

[0053] Please refer to Figure 1 , Figure 1 A digital asset valuation method for power dispatching, provided in one embodiment of this application, specifically includes the following steps:

[0054] S1. Obtain the set of digital assets under the target power dispatching business scenario;

[0055] S2. Based on the physical topology and operating rules of the power system, determine the intrinsic attributes of each digital asset in the digital asset set;

[0056] S3. Based on the power market environment and dispatch decision-making needs, determine the external value characteristics of each digital asset in the digital asset set;

[0057] S4. Based on intrinsic attributes and external value characteristics, construct a multi-dimensional value assessment model for the power dispatching field;

[0058] S5. Using a multi-dimensional value assessment model, combined with real-time and historical scheduling and operation data, calculate the value of each digital asset in the digital asset set and output a basic value score.

[0059] S6. Based on the basic value score, combined with the preset value level classification rules and adjustment factors, generate a comprehensive value assessment result and value tag for each digital asset.

[0060] S7. Apply the comprehensive value assessment results and value tags to the power dispatch auxiliary decision support system to provide quantitative basis for power dispatch plan optimization, internal pricing of data assets, and external transactions.

[0061] It is understandable that the core technical terms involved in this solution are closely related to the intersection of power dispatch and data asset assessment. A digital asset set refers to the aggregation of potentially valuable data, models, and documents generated during power dispatch operations, such as SCADA real-time measurements, generation plan curves, topology parameters, and dispatch procedures.

[0062] Intrinsic attributes refer to the inherent characteristics of an asset, determined by the physical laws of the power system (such as Kirchhoff's laws) and business processes. These include the degree of correlation with the physical topology of the power grid, such as the dependence and impact of power flow data on a certain line on that line and adjacent equipment, as well as data quality measured from four dimensions: completeness, accuracy, timeliness, consistency, and total lifecycle cost.

[0063] External value characteristics refer to the utility performance of an asset in a specific market and business environment, including the intensity of demand in decision-making at different scheduling stages such as pre-event, real-time, and post-event, the reuse potential in multiple scenarios such as power generation planning and safety verification, and compliance and sensitivity ratings based on regulations.

[0064] The multidimensional value assessment model uses the analytic hierarchy process (AHP) and entropy weighting method to combine subjective and objective weights, and introduces a fusion algorithm of fuzzy comprehensive evaluation.

[0065] The basic value score is the initial result of the model calculation. After dynamic correction by value level classification (such as strategic, business, support, and archived assets) and adjustment factors (such as time decay, scenario gain, and scarcity factors), the final comprehensive value assessment result and value label are formed and integrated into the auxiliary decision-making system to construct a data asset value map to support internal cost allocation and external transaction pricing.

[0066] The beneficial effects of this technical solution lie in its systematic resolution of the core challenges in the power dispatching field: the difficulty in measuring the value of digital assets, the disconnect between assessment and business operations, and the lack of a closed-loop application. First, by defining intrinsic attributes deeply coupled with the physical characteristics and operating rules of the power grid, and external features linked to market decision-making, and employing a subjective-objective combined weighting model, it achieves precise anchoring of assessment dimensions and business logic, resolving the problem of deep coupling in assessment. Second, through a dynamic calculation mechanism that integrates historical patterns and real-time state awareness, it enables asset value to respond sensitively to specific states such as power grid faults and new energy fluctuations, achieving dynamic and real-time value perception. Finally, by directly embedding the quantified results into dispatching auxiliary decision-making, cost accounting, and transaction support processes in the form of value tags and graphs, it establishes a closed-loop value transformation from value discovery to plan optimization, internal pricing, and external transactions, thereby truly transforming data into measurable, manageable, and operable production factors, improving power grid operating efficiency and the effectiveness of data elements.

[0067] See Figure 2 In some embodiments of this application, determining the intrinsic properties in step S2 further includes:

[0068] S21. Correlation Analysis: Analyze the metadata and content of digital assets and establish a mapping relationship between them and the physical components, topology, and operating status of the power grid;

[0069] S22. Data quality assessment: Evaluate structured operational data from four dimensions: completeness, accuracy, timeliness, and consistency.

[0070] Among them, integrity is calculated based on the data missing rate; accuracy is measured by the deviation rate verified by reliable data sources or physical laws; timeliness is determined based on the delay from the data generation time to the evaluation time and its attenuation function on the current scheduling application; consistency is determined by the logical contradiction rate of cross-system or cross-time period data.

[0071] S23. Cost Accounting: Track and calculate the computing resources, storage resources, human resources costs, and management costs consumed in the generation, collection, storage, cleaning, and maintenance of digital assets throughout their entire lifecycle, and convert them into quantifiable cost values.

[0072] Specifically, step S21 involves the following process: First, parsing the metadata and content (such as specific values ​​and text descriptions) of the digital assets. The metadata includes at least one of the following: data point name, equipment identifier, affiliated substation, and measurement type. The content includes at least one of the following: specific values ​​and text descriptions. Then, using pattern matching and semantic analysis techniques, the data is mapped to a pre-set or dynamically generated power grid knowledge graph. This knowledge graph contains models of the power grid's physical components, topology, and operating status. Physical components include generators, transformers, transmission lines, and circuit breakers; the topology includes bus connection relationships and electrical island divisions; and operating status includes normal, maintenance, fault, and heavy load. For example, a data point like "Active Power of 500kV XX Line" can be mapped to a specific transmission line entity in the power grid model by parsing the line name in its metadata. A document titled "XX Substation Complete Outage Accident Handling Plan" can be associated with all equipment and related protection and control strategies within the substation through keyword extraction.

[0073] Step S22, the implementation of data quality assessment, is based on the principle of constructing a multi-dimensional, quantifiable quality measurement system to address the stringent requirements of power dispatching operations for high data reliability and timeliness. The specific process unfolds across four dimensions for structured operational data:

[0074] 1) Integrity Assessment: The data loss rate is calculated by comparing the actual number of data points received with the expected number of data points within a time window (e.g., one scheduling cycle). For example, if a line is supposed to upload an active power value every 5 seconds, and there should be 100 data points per hour, and only 90 are actually received, the loss rate is (100-90) / 100. 100% = 10%.

[0075] 2) Accuracy Assessment: The deviation rate is calculated by comparing the data to be assessed with a high-reliability data source, or by using the physical laws of the power system (such as Kirchhoff's current law and power balance equations) for logical verification. For example, comparing the voltage deviation of two different acquisition channels at the same time, or checking whether the sum of the power of each incoming and outgoing line on the same bus is close to zero.

[0076] 3) Timeliness assessment: First, calculate the delay from the time the data is generated to the time the evaluation system receives and processes it. Then, based on the sensitivity of the scheduling application served by the data to the delay, define an attenuation function. The timeliness score is the result of the delay value after being mapped by the attenuation function. The longer the delay, the more severe the attenuation of the value to real-time applications, and the lower the score.

[0077] The decay function is a quantitative description of how the value (or utility) of data decreases as the time delay between its generation and use increases. In the time-constrained field of power dispatching, the value of data exhibits distinct characteristics: the greater the delay, the weaker its support for decision-making, and it may even be misleading. This decay function maps physical time delay to a timeliness score between 0 and 1, where 1 represents no delay (no loss of value) and 0 represents excessive delay (complete loss of value).

[0078] Specifically, the decay function takes the form: S(t) = e^(-λ) The normalized form is S(t) = e^(-(t / T_half)). ln2).

[0079] in,

[0080] S(t) represents the timeliness score at time t;

[0081] t represents the data latency (e.g., the number of seconds from data generation to the evaluation time).

[0082] λ is the decay constant, which determines the rate of value decay. The larger λ is, the faster the decay.

[0083] T_half is a more intuitive physical parameter called the "half-life," which represents the time required for the timeliness score to decay to half of its initial value (i.e., 0.5). Its relationship with λ is: λ = ln2 / T_half.

[0084] For example: Suppose that the PMU (Synchronization Phasor Measurement Unit) data used for real-time frequency stability monitoring has a value half-life T_half set to 5 seconds. This means:

[0085] When the delay is t = 0 seconds, S(0) = e^0 = 1.0, and the value is not lost.

[0086] At a delay of t = 5 seconds, S(5) = e^(- (5 / 5) ln2) = 0.5, the value is halved.

[0087] When the delay is t = 15 seconds, S(15) = e^(- (15 / 5) ln2) ≈ 0.125, the value has decreased to about 12.5%.

[0088] 4) Consistency Assessment: This involves checking whether copies of the same data are consistent across different business systems (such as EMS and DMS), or whether there are logical inconsistencies in the sequence data reported at different time periods (such as sudden power surges exceeding equipment limits). Consistency is determined by detecting and statistically analyzing the ratio of such inconsistencies. For unstructured documents, the assessment evaluates whether the version is the current valid version, whether the content sections are complete, and whether it conforms to the established document writing template. This series of assessments transforms the originally qualitative data quality into a series of calculable and comparable quantitative indicators.

[0089] Among them, structured operational data includes at least one of SCADA measurements, PMU phasors, and planned values.

[0090] Step S23, the cost accounting process involves tracking all stages of the digital asset's lifecycle from creation to destruction:

[0091] 1) Generation and acquisition costs: Calculate the hardware investment (such as sensors, RTUs), communication link costs, data acquisition software license and development costs required to obtain raw data, and allocate them to specific assets.

[0092] 2) Storage cost: Calculated based on the database storage space occupied by the asset, the storage duration, and the unit cost of the storage media used (such as cache, disk array, tape library).

[0093] 3) Cleaning and maintenance costs: This includes the computing resources (CPU / GPU hours) consumed in the data cleaning, verification, and fusion processes to improve data quality, as well as the human resources costs for developing and maintaining related algorithm models.

[0094] 4) Management Costs: These include the human and tool costs incurred in management activities such as asset cataloging, metadata management, security protection, and backup archiving. All resource consumption mentioned above is converted into a uniform currency unit (such as RMB) or standardized cost points using the company's internal cost accounting model or market prices. For example, the total lifecycle cost of a generator characteristic parameter requiring high-frequency data collection, dual redundant storage, and daily model calibration will be significantly higher than that of a standard report template that only requires annual updates. Cost accounting provides a cost floor perspective on asset value.

[0095] The beneficial effects of the aforementioned technical solution lie in its decomposition of abstract and general intrinsic attributes into three major technical dimensions: quantifiable correlation, data quality, and cost, which are deeply integrated with the physical and operational processes of the power grid. It also details the principles and specific processes of their quantification, establishing for the first time in the field of power dispatching an objective, transparent, and auditable measurement standard for the intrinsic value of digital assets. This not only solves the problem that general data management methods cannot accurately assess the unique technical value of power data, but more importantly, by revealing the inherent linkage between correlation, quality, and cost, it enables the assessment results to truly reflect the fundamental position and formation cost of assets within the power system. This provides reliable data support for subsequent hierarchical and classified asset management, cost-effectiveness optimization, and precise investment and maintenance of high-value assets, significantly enhancing the professionalism and persuasiveness of the entire value assessment system.

[0096] See Figure 3 In some embodiments of this application, determining the external value characteristics in step S3 further includes:

[0097] S31. Demand Intensity Analysis: Investigate and analyze the degree of dependence of various scheduling decision-making tasks on specific digital assets at different time scales, including day-ahead scheduling planning, real-time scheduling control, and post-event analysis and evaluation.

[0098] S32. Reuse Potential Assessment: Assess the universality and adaptability of digital assets in different dispatching business scenarios such as power generation planning, load forecasting, renewable energy consumption, safety verification, and ancillary service market clearing.

[0099] S33. Compliance and Sensitivity Rating: Based on the Data Security Law, the Power Industry Data Classification and Grading Guidelines, and the company's internal data governance standards, digital assets are classified into sensitivity levels. Sensitive information involving key grid coordinates, real-time operation control commands, and undisclosed markets is identified. The possibility and potential risks of using such information for value exchange after desensitization within a controllable range are assessed.

[0100] Specifically, step S31, the implementation process of demand intensity analysis, is as follows: First, a standardized scheduling decision task library is established, covering typical tasks at different time scales, such as daily planning, intraday rolling optimization, real-time scheduling control, and post-event analysis and evaluation. For each task in the library, through domain expert research, historical work order analysis, and system log mining, the set of input data assets necessary or strongly related to its execution is identified, and the impact of missing or degraded assets on the task execution effect (such as plan feasibility, control accuracy, and analysis depth) is analyzed. Next, a task-asset demand correlation matrix is ​​constructed, and a quantitative demand intensity index is assigned to each task-asset pair. The calculation of this index comprehensively considers the following factors: 1) Call frequency: the frequency with which the asset has been successfully called in similar historical tasks; 2) Role weight: the relative importance of the asset as a core parameter, auxiliary reference, or only background information in the task decision logic; 3) Timeliness coupling degree: the strictness of the task execution time on the asset data time (such as real-time control requiring millisecond-level latest data, while post-event analysis can tolerate hour-level delays). For example, for the control task of real-time voltage over-limit adjustment, the real-time measurement value of the key bus voltage has an extremely high call frequency, core parameter weight, and strict time coupling, so its demand intensity index is close to full. However, the historical voltage data of the same bus from a week ago has a demand intensity index close to zero for this real-time control task, but has a moderate demand intensity for the task of ex-post analysis of voltage fluctuation trends. This analysis closely links asset value with dynamic business activities.

[0101] Step S32, the specific process for implementing the reuse potential assessment, is as follows: First, clarify the specific data input specifications and processing logic for multiple core dispatching business scenarios, such as power generation planning, load forecasting, new energy power forecasting, static / dynamic safety verification, and ancillary service market clearing. Then, conduct an adaptability analysis on the target digital assets and assess their: 1) Standardization of data format and interface: whether it conforms to international or industry standards, or common data exchange formats (such as JSON / XMLSchema), which determines the ease with which it can be directly read by different systems; 2) Universality of business semantics: whether the information carried by the asset has a consistent or mappable interpretation in different scenarios. For example, the power grid topology model is a basic input in multiple scenarios such as safety verification, power flow calculation, and fault analysis, and has extremely high reuse potential; while a cost curve model optimized for a specific power plant bidding strategy has relatively limited reuse potential. In quantitative evaluation, a reuse potential coefficient is calculated by statistically analyzing the number of different business scenarios that the asset can directly support with little or no conversion (such as unit conversion, aggregation / deaggregation) and their business importance (weight), combined with its standardization score. Assets with high reuse potential mean that a single construction or governance investment can generate value in multiple places, and their external value characteristics are significant.

[0102] Step S33, the specific process of implementing compliance and sensitivity rating follows a hierarchical classification framework: First, based on the *Data Security Law of the People's Republic of China*, the *Personal Information Protection Law*, and the power data classification and grading guidelines issued by the National Energy Administration and the State Grid Corporation of China, the legally sensitive attributes of the asset content are determined, such as whether it contains core data (e.g., undisclosed detailed parameters reflecting the operational safety of the State Grid), important data (e.g., geographical coordinates of critical infrastructure, sensitive information on large-scale power outages), or general data. Second, in conjunction with the company's internal data governance standards, further identification is made of commercially or operationally sensitive content involving real-time grid operation control instructions, undisclosed market clearing prices and quantity information, and undisclosed dispatch plans. Based on this, the assets are classified into sensitivity levels (e.g., public, internal, confidential, and core). The rating is not only a static label but also includes a dynamic, controllable desensitization and exchange risk assessment: for non-public level assets, the balance between the residual information utility and potential leakage risk after desensitization using technical means (e.g., generalization, perturbation, differential privacy) is assessed. For example, by desensitizing precise load curves into regionally aggregated typical curves, their value for macroeconomic energy research remains, while the risk of sensitive information leakage is significantly reduced, potentially leading to a conditionally shared rating. This step ensures that the entire process of value assessment and value realization is conducted within a compliant and secure framework.

[0103] The steps S31 to S33 above form an organic whole, logically progressive and mutually constraining. Demand intensity (S31) reveals the peak and fluctuation of the asset's value on the "time axis," clarifying the key moments for value release. Reuse potential assessment (S32) reveals the breadth and extensibility of the asset's value on the "business cross-section," indicating the application path for maximizing value. Together, they depict the potential "utility map" of asset value in the spatiotemporal business dimensions. However, the actual development of this "utility map" must be constrained by the "safety boundaries" defined by compliance and sensitivity rating (S33). An asset may have high demand intensity and reuse potential (such as a real-time system topology), but its core sensitivity level is also high. This means that the realization of its high value must be carried out in a highly controlled internal environment or under strict approval and processing conditions, which in turn will affect its specific pricing and terms design on the value realization path of "external transactions."

[0104] The beneficial effect of the aforementioned technical solution lies in its systematic construction and quantification of three external characteristic dimensions: demand intensity, reuse potential, and compliance sensitivity. This dynamically expands the valuation of digital assets from static, intrinsic attribute analysis to an external space deeply interacting with business rhythm, market scenario changes, and the legal and policy environment. It not only solves the problem of how to quantify the immediate utility and long-term scalability of assets in a volatile business environment, but also innovatively transforms the non-technical constraint of data security and compliance into a risk adjustment factor that can be incorporated into the quantitative assessment model. This ensures that the assessment results can both actively guide data elements to create maximum value in business and firmly safeguard the bottom line of security and compliance, providing crucial decision-making basis for conducting refined internal management and orderly external circulation of data assets in a complex regulatory environment.

[0105] In some embodiments of this application, the construction of a multidimensional value assessment model for the power dispatching field in step S4 specifically involves: using the analytic hierarchy process combined with the entropy weight method to determine the weights of each dimension;

[0106] Among them, the analytic hierarchy process (AHP) is used to synthesize the subjective importance judgments of domain experts on the relevance of assets and the intensity of demand, construct a judgment matrix and calculate subjective weights; the entropy weight method calculates objective weights based on the dispersion of the indicator values ​​of each dimension in historical evaluation data; finally, the subjective weights and objective weights are linearly combined and optimized, and the comprehensive weight coefficient of each value dimension is obtained through a weighted fusion algorithm.

[0107] Specifically, step S4 aims to construct an evaluation model that can scientifically and rationally integrate various value dimensions. Its core challenge lies in how to assign weights to the multiple intrinsic attributes and external value characteristics (such as relevance, data quality, cost, demand intensity, reuse potential, and compliance rating) established in S2 and S3. This embodiment innovatively combines the analytic hierarchy process (AHP) and the entropy weight method, and performs linear combination optimization to determine the comprehensive weight coefficients for each dimension.

[0108] First, the process of determining subjective weights using the analytic hierarchy process is as follows:

[0109] By constructing a hierarchical model and a judgment matrix, the qualitative comparisons of experts are transformed into quantitative weights.

[0110] Establish a hierarchical structure: take the value of digital assets as the target layer, take the intrinsic attributes and external value characteristics as the criteria layer, take the specific dimensions (such as relevance, data quality, demand intensity, etc.) as the sub-criteria layer, and take the assets themselves as the solution layer.

[0111] Constructing a judgment matrix: Several experts in power dispatching and data management were invited to conduct pairwise importance comparisons of various dimensions at the same level, based on their professional knowledge and experience. For example, comparing the importance of "relevance" and "demand intensity" in assessing asset value, and by how much more important. A 1-9 scale was used for quantification (e.g., 1 represents equal importance, 9 represents absolute importance), and a judgment matrix was constructed for each expert.

[0112] Calculating Subjective Weights and Consistency Testing: For each judgment matrix, calculate its largest eigenvalue and the corresponding normalized eigenvector. This eigenvector represents the weights given by the expert for each dimension. Subsequently, a consistency test must be performed, calculating the consistency ratio (CR). If CR < 0.1, the expert's judgment is considered to have satisfactory consistency, and their weighted opinions are valid; otherwise, the expert needs to readjust their judgment. The geometric or arithmetic mean of all expert weights that pass the test is then calculated to obtain the subjective weight vector W_subjective, representing the consensus of the expert group.

[0113] Secondly, the process of determining objective weights using the Entropy Weight Method (EWM) is as follows:

[0114] Entropy weighting is based on the principle that "the greater the dispersion of an indicator, the more information it provides, and the higher its weight should be," and assigns weights entirely based on data.

[0115] Data preparation and standardization: Collect assessment results data of multiple batches of digital assets across various value dimensions over a period of time (e.g., one year) to form an initial assessment matrix. Due to the different dimensions and directions (benefit-oriented, cost-oriented), standardization is required to convert all indicator values ​​to the [0,1] range and unify them into benefit-oriented indicators.

[0116] Calculate the entropy value of the indicator: For the standardized matrix, calculate the weight of the indicator value of the i-th asset in the j-th dimension. Then, calculate the information entropy of that dimension according to the information entropy formula. The information entropy value is between 0 and 1. The larger the entropy value, the more uniform the data distribution of all assets in that dimension, that is, the weaker the distinguishing ability of the indicator.

[0117] Calculate the objective weights: Calculate the difference coefficient of the j-th dimension based on information entropy (difference coefficient = 1 - entropy value). The larger the difference coefficient, the greater the amount of information provided by that dimension, and the more important it is. Finally, normalize the difference coefficients of all dimensions to obtain the objective weight vector W_objective, which is entirely based on the statistical characteristics of historical data.

[0118] Finally, a linear combination optimization of the subjective and objective weights is performed to obtain the comprehensive weight:

[0119] A single subjective weight may be influenced by expert bias, while a single entropy weight may deviate from common business sense due to data characteristics. To overcome their respective shortcomings, this embodiment adopts a linear combination approach to optimize and integrate the two.

[0120] Set the overall weight W_combined = α W_subjective + β W_objective, where α and β are combination coefficients, and α + β = 1. The determination of α and β is not a simple averaging, but rather through optimization algorithms (such as trial-and-error based on historical evaluation feedback, or solving an objective function that maximizes the discriminative power of the combined weights on the final evaluation result). For example, an objective can be set to maximize the correlation (such as the Spearman rank correlation coefficient) between the asset value ranking calculated based on different weight combinations and the benchmark ranking based on the asset's actual business influence (such as usage frequency and decision contribution). By solving this optimization problem, the optimal values ​​of α and β are obtained, and the final comprehensive weight coefficient vector W_combined is then calculated.

[0121] For example: Suppose that after the initial processing in steps S2 and S3, we focus on three core dimensions: relevance (A), data quality (B), and demand intensity (C). Through expert evaluation and calculation, the subjective weight is obtained as: W_subjective = (0.5, 0.3, 0.2). This reflects the experts' view that intrinsic attributes (especially relevance) are more crucial than external demands. Entropy weight method results: Based on historical assessment data of 1000 assets, the dispersion (variance coefficient) of the three dimensions is found to be: 0.8, 0.5, and 0.9, respectively. After normalization, the objective weight is obtained as: W_objective = (0.36, 0.23, 0.41). This shows that in historical data, demand intensity (C) exhibits the greatest variation and the highest discriminative power.

[0122] By optimizing the algorithm (e.g., setting the objective to maximize the correlation between the asset value score and the number of times the actual scheduling decision is referenced), the optimal combination coefficients α=0.6 and β=0.4 are obtained.

[0123] Overall weight: W_combined = 0.6 (0.5, 0.3, 0.2) + 0.4 (0.36, 0.23, 0.41) = (0.444, 0.272, 0.284). The final weights not only significantly reflect the experts' emphasis on relevance (A), but also reasonably reflect the actual influence of demand intensity (C) in historical data.

[0124] The beneficial effect of this technical solution lies in its creative combination and linear combinatorial optimization of the analytic hierarchy process (AHP) and entropy weight method, fundamentally solving the technical problem of subjective and arbitrary weight setting or deviation from business reality in multi-attribute decision-making. This method enables the final multi-dimensional value assessment model to deeply integrate professional knowledge and value orientation in the power dispatching field, while fully exploring and utilizing the objective laws inherent in historical assessment data, thereby ensuring the scientific, reasonable, and robust nature of weight allocation. This significantly enhances the reliability and persuasiveness of the entire value assessment model, ensuring that its assessment results are recognized by domain experts and can withstand the test of actual data, laying a solid model foundation for the subsequent accurate and fair quantification of digital asset value.

[0125] In some embodiments of this application, step S4 further includes: introducing fuzzy comprehensive evaluation logic to process qualitative indicators in the weighted fusion algorithm; for the characteristics of data quality standardization and compliance sensitivity rating, using membership function to transform them into distributions on different evaluation levels, and then synthesizing them with the evaluation values ​​of quantitative indicators under a unified fuzzy operation framework, and finally obtaining quantitative value factors through defuzzification to handle the inherent uncertainty and fuzziness in the evaluation process.

[0126] Specifically, step S4 aims to address the uncertainty inherent in qualitative evaluation and subjective judgment that are unavoidable in value assessment. For example, the level of data quality standardization or the strength of compliance sensitivity are essentially fuzzy concepts that are difficult to characterize with a single precise numerical value. Fuzzy comprehensive evaluation transforms these qualitative characteristics into a membership degree distribution across each evaluation level by establishing an evaluation set (e.g., {high, medium, low}) and defining a membership function. For instance, the standardization of a document can be evaluated as having a membership degree of 0.7 for the "high" level, 0.3 for the "medium" level, and 0 for the "low" level. This provides a more nuanced and reasonable description of its state in the form of a fuzzy vector, avoiding the errors caused by arbitrarily assigning it a precise value.

[0127] After obtaining the fuzzy evaluation vectors for each qualitative indicator, this scheme synthesizes them with evaluation values ​​from precisely quantifiable indicators such as correlation and cost within a unified fuzzy computation framework. This is typically achieved through fuzzy transformation and synthesis operators, ultimately yielding a comprehensive fuzzy membership vector of the evaluated object (digital asset) for each value level. To obtain a final comparable and applicable single quantitative score, defuzzification is required. Common methods include the centroid method or the maximum membership method, transforming the fuzzy vector into a clear value factor value. This process ensures that qualitative judgments and quantitative data work together within a coordinated and scientific mathematical system to contribute to the final value score.

[0128] This application creatively solves the inherent problem of accurately quantifying subjective qualitative indicators in the evaluation process by introducing fuzzy mathematics theory. By integrating fuzzy comprehensive evaluation into a weighted fusion algorithm, the evaluation of fuzzy concepts such as normativity and sensitivity is no longer a rough estimate, but is transformed into a structured, computable mathematical expression. This significantly improves the overall evaluation model's ability to characterize and accommodate the complexity and uncertainty of the real world. This not only enhances the scientific rigor and rationality of the evaluation results, but also gives the model a stronger processing capability and more realistic evaluation accuracy when facing the numerous qualitative rules and expert experiences present in power dispatching operations.

[0129] In some embodiments of this application, step S5, which utilizes a multidimensional value assessment model and combines real-time and historical scheduling and operation data to calculate the value of each digital asset in the digital asset set and output a basic value score, further includes:

[0130] S51. Based on historical scheduling and operation data, analyze the value contribution pattern of each digital asset under similar working conditions, and establish the historical benchmark interval of its basic value score and the historical pattern calculation results of the dynamic adjustment coefficient.

[0131] S52. Access real-time dispatch and operation data stream to perceive the current operating status, safety margin, and market price of the power grid in real time;

[0132] S53. When the power grid is identified to be in a specific state, the value dimension that is strongly correlated with the specific state is dynamically activated and the weight is calculated using a multi-dimensional value assessment model to obtain the real-time state perception calculation result.

[0133] S54. Integrate the historical pattern calculation results with the real-time status perception calculation results to output a basic value score that reflects both the value pattern of the asset and meets the current immediate needs of the power grid.

[0134] Among them, the specific state includes at least one of the following: the transient and recovery state after a grid fault or disturbance, the ramp-up state of drastic fluctuations in renewable energy power, the operation state of heavy load on key transmission sections or equipment, the abnormal state of system frequency or voltage, and the anticipated state of recovery and dispatch after the execution of automatic restart or power outage contingency plans.

[0135] After calculating the value of the digital asset in step S5 above, a value credibility verification step is also included: randomly sampling the input multidimensional feature parameters within their possible value range, repeatedly running the evaluation model multiple times to obtain the probability distribution of the asset value score; analyzing the variance and confidence interval of the distribution, if the variance exceeds the threshold or the confidence interval is too wide, the credibility of the evaluation result is low.

[0136] Specifically, since certain intrinsic attributes and external value characteristic parameters (such as cost estimation range, compliance sensitivity membership, and quality deviation rate estimation interval) determined in steps S2 and S3 may have a certain range or error, rather than fixed values, this solution involves performing a large number (e.g., tens of thousands) random samplings of these multidimensional characteristic parameters within their possible value ranges or probability distributions in a computer program. Each sampling constitutes a complete set of input parameters, driving the multidimensional value assessment model to run once, thereby calculating the corresponding asset value score. By repeating this process, a probability distribution (such as a histogram or density function) of the asset value score can be obtained, rather than a single fixed value.

[0137] After obtaining the probability distribution of the value scores, statistical analysis is used to calculate its variance (or standard deviation) and confidence intervals at specified confidence levels. Variance directly reflects the dispersion of multiple simulated scoring results; a larger variance indicates that the evaluation result is more sensitive to changes in input parameters and has poorer stability. The confidence interval provides the range within which the score value is most likely to fall; an excessively wide interval signifies high uncertainty and insufficient accuracy in the evaluation result. This solution presets scientifically reasonable variance and confidence interval width thresholds, comparing the actual values ​​obtained from simulations with these thresholds. If the actual variance exceeds the threshold, or the confidence interval width exceeds the threshold, the system automatically identifies the credibility level of the current evaluation result as low and prompts the user to pay attention to the accuracy and stability of the relevant input parameters.

[0138] This application innovatively quantifies and verifies the credibility of digital asset valuation results. This not only significantly enhances the rigor and scientific nature of the valuation system, providing clear quality indicators for the final value score, but also offers crucial decision-making context for users (such as dispatchers or asset managers): a "high" credibility score carries greater decision weight, while a "low" credibility score indicates the need for caution and verification of the input data quality. This greatly enhances the robustness, transparency, and practicality of the entire valuation system in the face of real-world data uncertainty and ambiguity.

[0139] In some embodiments of this application, the preset value level classification rules and adjustment factors in step S6 include: classifying assets into four levels—strategic assets, business assets, support assets, and archived assets—based on the range of basic value scores; the adjustment factors include a time decay factor, a scenario gain factor, and a scarcity factor; wherein, the time decay factor is set according to the time sensitivity of scheduling business, so that the value of real-time data decays over time; the scenario gain factor is activated when an asset is applied to a specific emergency scheduling scenario with high security risk or high economic benefit, temporarily increasing its value weight; the scarcity factor is adjusted according to the uniqueness of the source of similar substitutable assets.

[0140] The first step is to establish a value mapping mechanism directly linked to asset management strategies. Based on the preset range of the basic value score, digital assets are statically divided into four levels: strategic assets, business assets, support assets, and archived assets. This division is not a simple ranking, but rather corresponds to differentiated resource protection, access permissions, and lifecycle management strategies.

[0141] Strategic assets refer to core digital assets that have a global and decisive impact on the safe and stable operation of the power grid and major dispatch decisions, and are difficult to replace or rebuild. These assets are typically directly related to critical power grid infrastructure, core control logic, or systemic risk prevention. Their value is long-term and fundamental; any anomalies or failures could lead to significant operational risks or decision-making errors. Strategic assets mainly include: 1) Real-time state estimation and network topology models: accurate models used to calculate real-time power flow across the entire network, forming the cornerstone of power grid situational awareness; 2) Stability limits and control strategy parameters for key transmission sections: core data directly determining the power grid's transmission capacity and safety boundaries; 3) "Black start" contingency plans and recovery control procedures involving the entire or regional power grid: the highest-level emergency assets for restoring power supply after a system collapse; 4) Source code and key parameters of core dispatch algorithms (such as safety-constrained economic dispatch and automatic generation control). These assets require the highest level of protection, real-time monitoring, off-site disaster recovery, and strict access control.

[0142] Business assets refer to essential digital assets frequently used in the core daily business processes of power dispatching, directly supporting various planning and real-time operations. These assets are fundamental to ensuring the continuous and efficient operation of dispatching services, possessing high usage frequency and direct business support value. Business assets mainly include: 1) Day-ahead / intra-day generation plan curves and load forecast results: the direct basis for daily dispatch plan preparation; 2) Real-time output and adjustable capacity data of generating units (including new energy power plants): used for real-time balancing and adjustment; 3) Relay protection setting sheets and equipment commissioning / decommissioning status information: related to operational safety; 4) Market clearing prices and declaration data: supporting market-based operations. These assets require high availability, good data quality, and efficient retrieval and retrieval capabilities.

[0143] Supporting assets refer to digital assets used for auxiliary analysis, management, reporting, and general reference. These assets do not directly impact core scheduling decisions but help improve work efficiency, support analytical reports, or meet management compliance requirements. Supporting assets mainly include: 1) Equipment ledgers and historical maintenance records: reference information for equipment status assessment; 2) Long-term historical statistical reports for non-real-time analysis (such as monthly electricity consumption statistics); 3) General scheduling procedure document templates and meeting minutes; 4) Simulation case data for training. The management of these assets focuses on orderly organization, convenient querying, and cost control, typically employing standardized storage and archiving strategies.

[0144] Archived assets refer to digital assets that have exceeded their primary operational utility period and only possess potential value for historical retrieval, audit traceability, or regulatory compliance. These assets are no longer involved in daily operations, but their content may need to be accessed under specific circumstances (such as incident tracing, long-term trend research, or compliance audits). Archived assets mainly include: 1) detailed historical operational logs exceeding the statutory or company-mandated retention period (such as SCADA sampling data at the second-by-second level from five years ago); 2) obsolete old scheduling procedures and obsolete technical solutions; 3) completed and closed project process documents; and 4) old simulation model parameters that have been replaced by new models.

[0145] The time decay factor quantifies the natural loss of data value over time through a decay function that matches the rhythm of dispatching operations. For example, the value of real-time monitoring data may decline rapidly according to an exponential law. The scenario gain factor is a value amplification mechanism. When the system identifies a specific emergency state such as a grid fault or overload, it automatically increases the value weight of assets strongly related to that state (such as real-time power flow data of relevant lines and emergency plans) to reflect their immediate criticality. The scarcity factor, from a supply perspective, adjusts the value of assets (such as the measured characteristic parameters of a specific generation unit) if their source is unique and irreplaceable, reflecting the market law of resource scarcity.

[0146] This application achieves refined classification management and dynamic adjustment of asset value by establishing clear four-level classification rules and three types of dynamic adjustment factors: time, scenario, and scarcity. This not only provides a basis for asset management strategies, but more importantly, through the adjustment factor mechanism, it enables asset value to automatically adjust according to the passage of time, the urgency of the scenario, and the scarcity of alternative resources. This achieves lifecycle management and scenario-based flexible assessment of asset value, supporting differentiated resource allocation strategies.

[0147] In some embodiments of this application, step S7, which provides a quantitative basis for power dispatching plan optimization, internal pricing of data assets, and external transactions, specifically includes: dynamically associating value tags as metadata with digital assets, and constructing a data asset value map based on comprehensive value assessment results; in power dispatching plan optimization, presenting dispatchers with a priority view of assets of different value levels, and automatically recommending or prioritizing high-value digital assets as decision input when formulating or adjusting dispatching plans; based on the value map and preset internal costs, realizing cross-departmental data asset usage cost allocation and benefit accounting to complete the internal pricing of data assets; generating a standardized data asset value report containing value assessment details, compliance certificates, and potential application scenarios, providing a reliable pricing benchmark and description of the transaction target for data product transactions or cooperation with external institutions in a controlled environment.

[0148] Specifically, the implementation principle and process of step S7 aim to deeply integrate the quantitative evaluation results generated in the aforementioned steps into the actual business operation and management process of power dispatch, thereby achieving a closed loop from value discovery to value realization. This process uses technical means to transform abstract value scores into operable and executable business rules and market signals.

[0149] First, dynamic association of value tags and construction of a data asset value graph are achieved. The principle is to bind and dynamically update the evaluation results (value tags and comprehensive scores) as a set of standardized, machine-readable metadata with the corresponding digital assets. Specifically, using an enterprise-level metadata management platform or dedicated interface, the value tags of each asset generated in step S6 (e.g., {Asset ID: 0001, Grade: Strategic Asset, Comprehensive Score: 92.5, High-Value Dimension: [Relevance, Timeliness]}) are written into the asset's metadata record. Based on these value metadata of all assets and their inherent relationships in the business (e.g., belonging to the same power grid equipment, serving the same scheduling process), a data asset value graph is constructed. This graph is a graph data structure where nodes represent digital assets, and node attributes include their value tags; edges represent relationships between assets, such as "input-output" relationships, "belonging to the same equipment" relationships, or "coexistence of business scenarios" relationships. The construction of the graph transforms asset value from a discrete list into an interconnected network view, intuitively revealing high-value asset clusters, key value flow paths, and weak links.

[0150] Secondly, the value map is deeply integrated with the dispatching auxiliary decision-making system to achieve data-driven resource optimization. Specifically, the dispatching auxiliary decision-making system accesses the value map in real time via API. When dispatchers are creating or adjusting plans, the system interface provides a data asset priority view. This view highlights high-value data assets (especially strategic and operational assets) and their quality status related to the current dispatching task (such as cross-section control and renewable energy consumption) in a visual manner (e.g., lists, topology overlays). Furthermore, the system can integrate an intelligent recommendation module: when a dispatcher intends to use a certain model or query a certain type of data, the system automatically analyzes the value map and recommends higher-scoring, higher-quality alternatives or preferred assets as decision input. For example, when formulating a power supply guarantee plan, the system prioritizes and automatically associates real-time power flow data from high-precision measurement units that has been recently verified, rather than general monitoring data. This essentially uses data value as one of the decision factors, improving the data foundation quality of the dispatching plan.

[0151] Furthermore, based on value mapping, internal pricing and cost-benefit accounting for data assets are achieved, establishing an internal accounting mechanism based on value and cost. This transforms the consumption of data assets from a technical cost center into manageable internal service costs. The specific process involves: combining the asset's value level and usage frequency (obtainable from system logs) in the value map with the full lifecycle cost calculated in step S2 to establish an internal pricing model. This model can set differentiated internal service unit prices or cost allocation coefficients for assets of different levels. For example, each time a strategic asset-level network status estimation result is accessed, the calling department incurs a higher internal cost point, while accessing historical statistical reports for supporting assets incurs a lower cost. By integrating this model into the enterprise's resource management and financial systems, cross-departmental and cross-project data asset usage cost allocation and benefit accounting can be automated. High-frequency use of high-value assets will generate significant internal costs, driving departments to utilize data more prudently and efficiently, while also providing value proof and a channel for capital recovery for the data management department's continued investment.

[0152] Finally, a standardized value report is generated to support external transactions. The specific process is as follows: the system automatically generates a structured "Data Asset Value Report" based on the value map, assessment details, compliance rating, and asset examples (after anonymization). This report includes at least: 1) Asset Overview and Value Summary: Core value score, level, and radar chart of key dimensions; 2) Assessment Details and Methodology: Showing the specific scores for each dimension and a brief introduction to the assessment model used, ensuring the process is auditable; 3) Compliance Proof: Clearly indicating the asset's sensitivity level, implemented anonymization techniques (such as differential privacy, generalization processing), and compliant legal and regulatory provisions; 4) Potential Application Scenarios Analysis: Based on the reuse potential assessment, listing scenarios where the asset may be applicable in external cooperation (such as regional power grid collaborative planning, new energy power generation capacity assessment). This report provides an objective pricing benchmark and a clear description of the transaction target for data product transactions, sharing, or cooperation in controlled environments (such as data exchanges, consortium blockchains), enabling buyers or partners to clearly understand the value connotation and usage boundaries of the purchased data.

[0153] The aforementioned steps are interconnected, forming a complete value realization chain from internal management to the external market. The value map is the core carrier, containing the assessment results and depicting asset relationships. Planned optimization and application ensures the immediate realization of value in core production processes, guaranteeing that high-value assets play a role in key decision-making. Internal pricing explores the monetization of value in operations management, establishing economic constraints and incentive mechanisms for data use. Standardized reporting vouches for value in factor markets, providing a technological foundation of trust for data to move beyond the enterprise and participate in circulation. This entire process transforms the valuation of digital assets from an offline, post-hoc analytical activity into an online, ongoing process that actively drives actual business operations and resource allocation.

[0154] This technological solution creatively designs and implements a path that directly, automatically, and seamlessly injects the quantified value of digital assets into the entire chain of power dispatching, production, management, and operation. By constructing a dynamic value map, deeply integrating it into decision support, implementing internal cost accounting, and generating standardized transaction reports, it completely solves the problem of the disconnect between assessment and application, and establishes a closed loop for value transformation from value assessment to plan optimization, internal resource allocation, and external market transactions. This not only greatly enhances the actual effectiveness of high-value data in ensuring grid security and improving economic dispatch levels, but also, for the first time in the power industry, provides a complete technical solution and feasible implementation model for the internal refined management and external compliant and orderly circulation of data assets, powerfully promoting the transformation of power data elements from resources to assets and then to capital.

[0155] In another embodiment of this application, a digital asset valuation system for power dispatch is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described digital asset valuation methods for power dispatch.

[0156] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to execute the aforementioned digital asset valuation method for power dispatching.

[0157] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0158] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0159] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0160] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in any embodiment of the digital asset value assessment method for power dispatch provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0161] In another embodiment of this application, an electronic device is provided. The electronic device stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the digital asset value assessment method for power dispatch described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in an electronic device, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0162] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0163] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0164] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0167] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the value of digital assets for power dispatching, characterized in that, Includes the following steps: S1. Obtain the set of digital assets under the target power dispatching business scenario; S2. Based on the physical topology and operating rules of the power system, determine the intrinsic attributes of each digital asset in the digital asset set; S3. Based on the power market environment and dispatch decision-making needs, determine the external value characteristics of each digital asset in the digital asset set; S4. Based on the intrinsic attributes and the external value characteristics, construct a multi-dimensional value assessment model for the power dispatching field; S5. Using the multi-dimensional value assessment model and combining real-time and historical scheduling and operation data, calculate the value of each digital asset in the digital asset set and output a basic value score. The step of using the multi-dimensional value assessment model, combined with real-time and historical scheduling and operation data, to calculate the value of each digital asset in the digital asset set and output a basic value score further includes: S51. Based on historical scheduling and operation data, analyze the value contribution pattern of each digital asset under similar working conditions, and establish the historical benchmark interval of its basic value score and the historical pattern calculation results of the dynamic adjustment coefficient. S52. Access real-time dispatch and operation data stream to perceive the current operating status, safety margin, and market price of the power grid in real time; S53. When the power grid is identified to be in a specific state, the value dimension that is strongly correlated with the specific state is dynamically activated and the weight is calculated using the multi-dimensional value assessment model to obtain the real-time state perception calculation result. S54. The historical pattern calculation results are fused with the real-time state perception calculation results to output a basic value score that reflects both the value pattern of the asset and meets the current immediate needs of the power grid. The specific state includes at least one of the following: the transient and recovery state after a power grid failure or disturbance, the ramp-up state of drastic fluctuations in renewable energy power, the operation state of heavy load on key transmission sections or equipment, the abnormal state of system frequency or voltage, and the anticipated state of recovery and dispatch after the execution of automatic restart or power outage plans. S6. Based on the basic value score, combined with the preset value level classification rules and adjustment factors, generate a comprehensive value assessment result and value tag for each digital asset. S7. Apply the comprehensive value assessment results and value tags to the power dispatch auxiliary decision support system to provide quantitative basis for power dispatch plan optimization, internal pricing of data assets, and external transactions.

2. The method according to claim 1, characterized in that, Determining the intrinsic properties in step S2 further includes: S21. Correlation Analysis: Analyze the metadata and content of digital assets and establish a mapping relationship between them and the physical components, topology, and operating status of the power grid; S22. Data quality assessment: Evaluate structured operational data from four dimensions: completeness, accuracy, timeliness, and consistency. Among them, integrity is calculated based on the data missing rate; accuracy is measured by the deviation rate verified by reliable data sources or physical laws; timeliness is determined based on the delay from the data generation time to the evaluation time and its attenuation function on the current scheduling application; consistency is determined by the logical contradiction rate of cross-system or cross-time period data. S23. Cost Accounting: Track and calculate the computing resources, storage resources, human resources costs, and management costs consumed in the generation, collection, storage, cleaning, and maintenance of digital assets throughout their entire lifecycle, and convert them into quantifiable cost values.

3. The method according to claim 1, characterized in that, Determining the external value characteristics in step S3 further includes: S31. Demand Intensity Analysis: Investigate and analyze the degree of dependence of various scheduling decision-making tasks on specific digital assets at different time scales, including day-ahead scheduling planning, real-time scheduling control, and post-event analysis and evaluation. S32. Reuse Potential Assessment: Assess the universality and adaptability of digital assets in different dispatching business scenarios such as power generation planning, load forecasting, renewable energy consumption, safety verification, and ancillary service market clearing. S33. Compliance and Sensitivity Rating: Based on the Data Security Law, the Power Industry Data Classification and Grading Guidelines, and the company's internal data governance standards, digital assets are classified into sensitivity levels. Sensitive information involving key grid coordinates, real-time operation control commands, and undisclosed markets is identified. The possibility and potential risks of using such information for value exchange after desensitization within a controllable range are assessed.

4. The method according to claim 1, characterized in that, The specific steps in step S4 of constructing a multi-dimensional value assessment model for the power dispatching field are as follows: The weights of each dimension are determined by combining the analytic hierarchy process (AHP) with the entropy weight method. Among them, the analytic hierarchy process (AHP) is used to synthesize the subjective importance judgments of domain experts on the relevance of assets and the intensity of demand, construct a judgment matrix and calculate subjective weights; the entropy weight method calculates objective weights based on the dispersion of the indicator values ​​of each dimension in historical evaluation data; finally, the subjective weights and objective weights are linearly combined and optimized, and the comprehensive weight coefficient of each value dimension is obtained through a weighted fusion algorithm.

5. The method according to claim 4, characterized in that, Step S4 further includes: In the weighted fusion algorithm, fuzzy comprehensive evaluation logic is introduced to process qualitative indicators; For the characteristics of data quality standardization and compliance sensitivity rating, a membership function is used to transform them into distributions on different evaluation levels. Then, they are synthesized with the evaluation values ​​of quantitative indicators under a unified fuzzy computation framework. Finally, the quantitative value factors are obtained through defuzzification to deal with the inherent uncertainty and fuzziness in the evaluation process.

6. The method according to claim 1, characterized in that, The preset value level classification rules and adjustment factors in step S6 include: Based on the range of basic value scores, assets are divided into four levels: strategic assets, business assets, support assets, and archived assets. The adjustment factors include time decay factors, scenario gain factors, and scarcity factors. Among them, the time decay factor is set according to the time sensitivity of scheduling business, so that the value of real-time data decays over time. The scenario gain factor is activated when the asset is applied to a specific emergency scheduling scenario with high security risk or high economic benefit, temporarily increasing its value weight. The scarcity factor is adjusted according to the uniqueness of the source of similar substitutable assets.

7. The method according to claim 1, characterized in that, The step S7, which provides quantitative basis for optimizing the power dispatch plan, internal pricing of data assets, and external transactions, specifically includes: The value tags are dynamically associated with the digital assets as metadata, and a data asset value map is constructed based on the comprehensive value assessment results. In the power dispatch plan optimization, a priority view of assets with different value levels is presented to the dispatcher, and high-value digital assets are automatically recommended or prioritized as decision input when formulating or adjusting the dispatch plan. Based on the value map and preset internal costs, cross-departmental data asset usage cost allocation and benefit accounting are realized to complete the internal pricing of data assets. A standardized data asset value report containing value assessment details, compliance certificates, and potential application scenarios is generated, providing a reliable pricing benchmark and description of the transaction target for data product transactions or cooperation with external institutions in a controlled environment.

8. A digital asset valuation system for power dispatching, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the digital asset valuation method for power dispatch as described in any one of claims 1 to 7.

9. An electronic device having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the digital asset valuation method for power dispatch as described in any one of claims 1 to 7.

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