Power grid regulation and control data asset value evaluation method, device, equipment and medium

Abstract

The invention belongs to the technical field of power grid big data, and particularly relates to a power grid regulation and control data asset value evaluation method and device, equipment and a medium. According to the method, key factors (data integrity, data diversity, data timeliness, data scale and data relevance) influencing the value of the power grid regulation and control data assets are determined, the weight coefficient of each factor is calculated by using an analytic hierarchy process, and then the value of the power grid regulation and control data assets is calculated. According to the scheme, a unified data value evaluation system is established, and a unified value measurement standard is provided for power grid regulation and control data assets by determining the key influence factors and the weight coefficients. Through data value evaluation, the value and distribution condition of each data asset can be clearly known, and efficient management and reasonable utilization of the data assets can be realized. Meanwhile, through data value evaluation, the potential value of the data assets can be found, and possibility is provided for value increment and achievement of the data.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid big data, and specifically relates to a method, device, equipment and medium for evaluating the value of power grid control data assets. Background Art

[0002] With the construction and promotion of the control cloud platform in the field of power dispatching, the model and operation data management are standardized based on the structured model of the general data object of power dispatching, breaking the barriers between primary and secondary energy data and natural environment and power grid operation data, and collecting massive power grid operation collection data involving power generation, load, events, alarms, plan forecasts, etc. Power dispatching is an important link in the direct production, transmission and distribution of electric energy, and is one of the core components of the power system. It has the characteristics of multiple equipment types, complex data structure attributes, and high data collection density. Mining the value of derivative data such as energy characteristics, electrical characteristics, behavioral characteristics, and economic characteristics of control operation data, providing data sharing and continuous precipitation of common derivative data to various business applications, and promoting the realization of data value in "internal value-added and external efficiency" is the basis for building an efficient, interactive, intelligent and open energy Internet.

[0003] Dispatch Control Data Assets refer to data resources recorded electronically, which are formed by power generation, transmission, transformation, distribution, and electricity consumption, and are expected to bring economic benefits to the control center, and are owned, controlled, or used by power grid control agencies through various means such as collection and production. Data assets integrate, clean, and associate the original data of various business systems to bring data that improves management efficiency, facilitates services, and optimizes business processes. At present, power grid control operation data has been gradually collected in the cloud, and the historical operation data in the control cloud has reached the PB level, and data assets have begun to take shape. However, the management and operation methods of these data are relatively rough, and they are still used and managed by various business departments in a scattered manner. The maintenance involves different units, departments, and industries. Some professional data have not yet been included in unified management, lack of top-level design, weak professional adhesion, and insufficient data value-added monetization capabilities. As an intangible asset, the value of control operation data has not received enough attention and in-depth exploration, which restricts the effectiveness of analysis and application. From the perspective of the industry, the concept of data as an asset has gradually become the mainstream consensus of the industry, but the efficient management and rational use of data assets are still in the exploratory stage. In terms of data asset construction, the Internet, finance and other industries have put forward suggestions to promote data asset operation and management, mainly from the four stages of establishing data asset planning, data governance and quality control, data processing and asset operation.

[0004] In terms of data value assessment, various industries have proposed different data value assessment models based on cost method, market method and income method. However, it is difficult to directly apply them in the field of power grid control. With the advancement of control cloud construction, the types of power grid control business data are complex, and there are many data source systems. Data sharing between different departments and different business systems is frequent. The value measurement standards of control data are vague, and the value assessment results are inaccurate. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, equipment and medium for evaluating the value of power grid control data assets, so as to solve the problems existing in the prior art of fuzzy control data value measurement standards and inaccurate value evaluation results.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for evaluating the value of power grid control data assets, comprising the following steps: Determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance; The weight coefficients corresponding to each influencing factor are calculated using the hierarchical analysis method; Calculate the evaluation index scores of each influencing factor; The power grid control data asset value is calculated based on the evaluation index score and the weight coefficient corresponding to each influencing factor.

[0007] Furthermore, the power grid control data asset value is calculated based on the evaluation index scores and weights of each influencing factor, including:

[0008] in, ; Indicates the value price unit of measurement. It is the value of power grid control data assets; is the evaluation index score of data integrity, is the evaluation index score of data diversity, is the evaluation index score of data timeliness, is the evaluation index score of data scale, is the evaluation index score of data relevance, They represent the weight coefficients of the corresponding influencing factors respectively.

[0009] Furthermore, the evaluation index score of data integrity is calculated as follows: Determine each first influencing factor of data integrity; wherein each first influencing factor includes asset integrity rate, quality integrity rate, governance integrity rate and data integrity rate; Calculate the index value of each first impact factor; Calculate the average value of each first impact factor index value as the evaluation index score of data integrity.

[0010] Furthermore, the evaluation index score of data diversity is calculated as follows: Identify the different data sources for grid regulation data assets; For the data attribute information of the same power grid control data asset from different data sources, a consistent accuracy evaluation calculation is performed to obtain the evaluation index score of data diversity.

[0011] Furthermore, the evaluation index score of data timeliness is calculated as follows: Determine the time limit for submitting data as required by asset planning; Statistics on the time of uploading power grid control data at each measuring point; Calculate the timeliness rate of each measuring point based on the data reporting timeliness and upload time; The timeliness data of each measuring point is averaged to obtain the evaluation index score of data timeliness.

[0012] Furthermore, the evaluation index score of data scale is calculated as follows: Determine the various evaluation criteria for data scale, determine specific scoring indicators based on the various evaluation criteria for data scale, and formulate scoring criteria for each scoring indicator; Obtain each expert's score on the data scale of power grid control data assets based on the scoring system and scoring criteria; Summarize the scores of each expert and calculate the average score of each scoring indicator; The comprehensive score of the data scale of the regulated data assets is calculated based on the weight of each scoring indicator, and the comprehensive score is used as the evaluation indicator score of the data scale.

[0013] Furthermore, the evaluation index score of data relevance is calculated as follows: Determine the evaluation criteria for data relevance; design specific scoring indicators based on the evaluation criteria for data relevance; formulate scoring criteria for each scoring indicator; Ask the expert panel members to independently score the data relevance of the regulatory data assets according to the scoring system and scoring criteria; summarize the scores of each expert and calculate the average score of each scoring indicator; Determine the corresponding weight according to the importance of each scoring indicator; The comprehensive score of data relevance is calculated according to the average score and weight of each scoring indicator, which is used as the evaluation indicator score of data relevance.

[0014] In a second aspect of the present invention, a device for evaluating the value of power grid control data assets is provided, comprising: A factor determination module is used to determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance; The first calculation module is used to calculate the weight coefficient corresponding to each influencing factor by using the hierarchical analysis method; The second calculation module is used to calculate the evaluation index score of each influencing factor; The third calculation module is used to calculate the value of the power grid control data asset according to the evaluation index score and the weight coefficient corresponding to each influencing factor.

[0015] According to a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned method for evaluating the value of power grid regulation data assets.

[0016] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the power grid regulation data asset value assessment method as described above is implemented.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: In view of the problems existing in the background technology, the present invention proposes a method for evaluating the value of power grid control data assets. The method determines the key factors (data integrity, data diversity, data timeliness, data scale and data relevance) that affect the value of power grid control data assets, and uses the hierarchical analysis method to calculate the weight coefficients of each factor, and then calculates the value of power grid control data assets. This scheme establishes a unified data value evaluation system, and provides a unified value measurement standard for power grid control data assets by determining key influencing factors and weight coefficients. Through data value evaluation, the value and distribution of each data asset can be clearly understood, which is conducive to the efficient management and rational use of data assets. At the same time, through data value evaluation, the potential value of data assets can be discovered, which provides the possibility for the value-added realization of data, and helps to improve the economic and social benefits of power grid control business. A power grid control data asset value evaluation device, electronic device and computer-readable storage medium provided by the present invention also solve the problems raised in the background technology section.

[0018] In summary, the present invention solves the problem of efficient management and value assessment of power grid control data assets by proposing a power grid control data asset value assessment method, provides high-value data knowledge for power grid operation, and improves the data management capability level of the dispatching automation system, especially the control big data platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for evaluating the value of power grid control data assets according to an embodiment of the present invention; Figure 2 A schematic diagram of a data asset value assessment model for an embodiment of the present invention; Figure 3 This is a structural block diagram of a power grid control data asset value assessment device according to an embodiment of the present invention; Figure 4 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0021] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0022] Explanation of relevant terms Dispatch Control Data Assets refer to electronically recorded data resources that are owned, controlled or used by power grid control agencies in accordance with the law through various means such as collection and production, and are generated by power generation, transmission, transformation, distribution, and power consumption, and are expected to bring economic benefits to the control center.

[0023] Data assetization is an important foundation for realizing value realization. Assetization of the massive power control data collected by the power grid will help deepen value-oriented mining and analysis and assist decision-making, and improve the ability to recognize the characteristics of large power grids, predict and anticipate, coordinate control, and manage. In each link of the power grid control operation process, this plan establishes a control data value assessment system, explores key technologies and application directions such as data asset basic support, value discovery, and asset assessment, evaluates data from different source systems, and establishes a unified control data asset value assessment model to provide a solid foundation for control data asset management and data transactions, value-added and other businesses. On the basis of ensuring data security, it improves the efficiency of data asset sharing and value-added, improves the dispatching automation system, especially the data management capability of the control big data platform, solves the problems of large amount of power grid control data, low value density, and lack of system analysis theory, and provides high-value data knowledge for power grid operation.

[0024] Example 1 In response to the current rough management and operation methods of power grid control data assets, this solution takes the control data object as the core, integrates the data asset planning method of electrical physical characteristics, time polymorphism characteristics, multi-source information characteristics and business meaning, and designs a technical method for the value assessment of power grid control data assets that considers the data ontology attributes and power grid operation mechanism. First, we conduct an in-depth analysis of the deep-level characteristics, laws and mechanisms of power grid control data, and establish an element system for the value assessment of power control data assets; secondly, we use the hierarchical analysis method and expert scoring method to construct a control data asset value assessment model; finally, we verify the feasibility and rationality of the data asset value assessment method through typical power control data application scenarios.

[0025] like Figure 1 As shown, a method for evaluating the value of power grid control data assets includes the following steps: S1. Determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance.

[0026] Specifically, the data types of control data are complex, covering both structured data and various types of unstructured data. We need to deeply analyze the deep-level characteristics, laws and mechanisms of power grid data, consider the data sample attributes and power grid operation mechanism, and analyze the factors that affect the value of data assets from multiple dimensions.

[0027] (1) Data asset scale The amount of data is one of the key factors affecting the value of data assets. The larger the amount of data, the more likely it is to discover the objective natural laws of the evolution of things, and then formulate accurate and comprehensive auxiliary decision-making analysis, so the higher the value of the data assets.

[0028] (2) Data quality. The quality of data assets is positively correlated with the value of data applications. A comprehensive and reasonable assessment of data asset quality (authenticity, accuracy, completeness, etc.) is conducive to the realization of data application value. If the data quality is poor, it will not only affect the realization of application value, but also increase data operation costs.

[0029] (3) Data multidimensionality. Data multidimensionality refers to the diversity of the coverage of data attributes. The more dimensions that describe data attributes, the wider the scope of data application, the greater the possibility of being discovered and utilized, and the greater the value of the data.

[0030] (4) Frequency of data use. The value of data assets will increase as the frequency of data use increases.

[0031] (5) Data timeliness. Data timeliness refers to data activity. Data timeliness determines whether decision analysis is reasonable and effective within a specific time period. Therefore, the value of data assets is easily affected by data timeliness and is volatile. The more realistic the data at the latest moment reflects the current system operation status, the greater the value it contains.

[0032] (6) Data application effectiveness. The ability to realize data value-added is affected by the degree of data value realization. When data assets produce influential data value mining products, they can better assist decision makers in scientific planning and precise management. When assets are fully utilized, the better the realization effect, the stronger the ability to realize asset value-added.

[0033] (7) Data richness. The more diverse the data types, the more comprehensive and detailed the data subject information can be. Rich data types increase the value of data assets.

[0034] (8) Sharing. The higher the degree of data openness and sharing, the higher the data penetration and sharing efficiency, and the greater the asset value density.

[0035] This solution comprehensively considers the characteristics of power grid regulation data assets and their operating mechanisms, and divides the value structure of power grid regulation data assets into five indicators as shown in Table 1. The value of regulation data assets is quantified from these five perspectives, which can more comprehensively reflect the value of power grid regulation data assets.

[0036] Table 1: Factors affecting the value of data assets

[0037] (1) Integrity. The integrity of data is mainly reflected in the data asset quality indicators. They mainly include asset integrity rate, quality integrity rate, governance integrity rate, data integrity rate, etc. The worse the data asset quality, the lower the data asset value.

[0038] (2) Diversity. Data diversity is reflected in the diversity of data sources and types, dynamic indicators such as data coverage, and data multi-source consistency verification. The higher the data diversity index, the higher the asset value.

[0039] (3) Timeliness: Data timeliness is reflected in indicators such as the frequency of updating the used data and the timeliness of updating. The better the timeliness of data assets, the higher the data timeliness, and the higher the value of data assets.

[0040] (4) Scale. The scale of data assets is reflected in indicators such as data quantity, data type, and coverage. The larger the data scale, the greater the value of the data assets.

[0041] (5) Relevance: Data relevance mainly evaluates the degree of mutual utilization between data.

[0042] S2. Use the hierarchical analysis method to calculate the weight coefficient corresponding to each influencing factor.

[0043] The Analytic Hierarchy Process (AHP) refers to a decision-making method that hierarchizes the influencing factors related to decision-making according to the relationship between the factors, and then conducts qualitative and quantitative analysis layer by layer on this basis.

[0044] The steps of AHP decision making are as follows: 1) Construct a hierarchical analysis model according to the hierarchical analysis model framework.

[0045] 2) Construct a judgment matrix.

[0046] The consistent matrix algorithm is used to determine the weights of the mutual influence between factors at each level in the hierarchical analysis model, that is, the relative scale is used to compare the factors affecting the target with each other to improve the accuracy. Assuming n factors The impact on target y, thereby determining the impact weight of each factor, taking two factors at a time for comparison and ,use express and The importance of the target y is measured and calculated according to the 1-9 scale method, as shown in Table 2. The matrix formed by comparing each factor with each other is the judgment matrix.

[0047] Table 2 Ratio scale table

[0048] 3) Hierarchical single sorting and consistency test: ; Necessary and sufficient conditions for a consistent matrix: ; Use the judgment matrix to calculate the weight of each factor to the target value according to the following formula. The calculation steps are as follows: ① The judgment matrix A Each column element of is normalized: (1) ②Add the normalized judgment matrix row by row: (2) ③ Vector Normalization: (3) The obtained That is the judgment matrix A Feature vector (i.e. weight coefficient).

[0049] ④Calculate the maximum eigenvalue (4) As an approximation to the largest eigenvalue.

[0050] 4) Consistency test Consistency Index CI for: (5) , indicating complete consistency, A value close to 0 indicates satisfactory consistency. The larger it is, the more serious the inconsistency is.

[0051] Consistency ratio CR : (6) in RI is a random consistency index (obtained by table lookup). When A The inconsistency is within the controllable range and has good consistency. Through the consistency test, the eigenvector calculated by formula (3) can be regarded as the weight vector. Otherwise, the comparison matrix needs to be reconstructed. A ,right Be adjusted.

[0052] S3. Calculate the evaluation index score of each influencing factor.

[0053] Specifically, the evaluation index score of data integrity is calculated as follows: Determine each first influencing factor of data integrity; wherein each first influencing factor includes asset integrity rate, quality integrity rate, governance integrity rate and data integrity rate; calculate the index value of each first influencing factor; calculate the average value of the index value of each first influencing factor as the evaluation index score of data integrity.

[0054] Specifically, the evaluation index score of data diversity is calculated as follows: Determine the different data sources of the power grid control data assets; perform consistent accuracy evaluation calculations on the data attribute information at different data sources of the same power grid control data assets to obtain the evaluation index score of data diversity.

[0055] Specifically, the evaluation index score of data timeliness is calculated as follows: Determine the data submission timeliness specified in the asset planning; count the submission time of the power grid control data at each measuring point; calculate the timeliness rate of each measuring point based on the data submission timeliness and submission time; average the timeliness rate data of each measuring point to obtain the evaluation index score of data timeliness.

[0056] Specifically, the data scale evaluation index score is calculated as follows: Determine the various evaluation criteria for data scale, determine specific scoring indicators based on the various evaluation criteria for data scale, and formulate scoring standards for each scoring indicator; obtain each expert's score on the data scale of power grid control data assets based on the scoring system and scoring standards; summarize each expert's score and calculate the average score for each scoring indicator; calculate the comprehensive score of the data scale of the control data assets based on the weight of each scoring indicator, and use the comprehensive score as the evaluation indicator score for data scale.

[0057] Specifically, the evaluation index score of data relevance is calculated as follows: Determine the evaluation criteria for data relevance; design specific scoring indicators based on the evaluation criteria for data relevance; formulate scoring criteria for each scoring indicator; have expert panel members independently score the data relevance of regulatory data assets based on the scoring system and scoring criteria; summarize the scores of each expert and calculate the average score for each scoring indicator; determine the corresponding weight based on the importance of each scoring indicator; calculate the comprehensive score of data relevance based on the average score and weight of each scoring indicator as the evaluation indicator score for data relevance.

[0058] Optionally, the evaluation index scores of data scale and data relevance are determined by expert scoring method. Expert scoring method refers to formulating reasonable and comprehensive evaluation criteria according to the specific requirements of the evaluation subject, firstly, experts score according to the given evaluation criteria, and then comprehensively analyze and summarize the expert scoring results, objectively integrate the experience of most experts and subjective cognitive judgment, make reasonable evaluation and calculation for a large number of factors that are difficult to qualitatively analyze by technical means, and obtain the evaluation results. This method is suitable for business scenarios with many uncertain factors that cannot be quantitatively analyzed by other methods.

[0059] In an optional embodiment, the data integrity assessment is mainly an assessment of the quality of power grid control data. The assessment of data quality is mainly reflected in four indicators: asset integrity rate, quality integrity rate, governance integrity rate, and data integrity rate. The calculation of the four indicators and then taking their average value is the data integrity index. G .

[0060] 1) Asset integrity rate Asset Integrity per Asset (AIA) for a single asset.

[0061] (7) 0—indicates that the asset data has never been sent, 1—indicates that the asset data has been sent and the number of days with data meets the planning requirements, (0, 1)—indicates that data has been sent but there is no data on some days.

[0062] 2) Quality completeness rate Quality integrity rate of a single asset (Qsset Integrity per Asset, QIA).

[0063] (8) 0—data for this asset has never been sent; 1—data has been sent and has been sent every day since the earliest access date; (0, 1)—data has been available since the earliest access date, but no data is available on some days.

[0064] 3) Governance completeness rate Governance Integrity per Asset GIA ).

[0065] (9) Result: 0—the asset data has never been submitted, or the data was submitted but the daily completeness rate was less than 100%; 1—the asset data was submitted, and the number of days with a daily completeness rate of 100% met the planning and governance requirements; (0, 1)—the asset data was submitted, and the number of days with a daily completeness rate of 100% did not meet the planning and governance requirements.

[0066] 4) Data integrity rate Daily Data Integrity per Asset, DDIA ).

[0067] (10) Result: 0—the data for the day is not sent; 1—the data for the day is sent and is complete; (0, 1)—the data for the day is sent but has breakpoints.

[0068] The evaluation calculation for data integrity assessment is: (11) In an optional embodiment, multivariate consistency refers to the degree of consistency of data values ​​from different data sources. Whether the data attribute information at different data sources of the same data asset is consistent requires an evaluation of the degree of consistency accuracy. If the deviation of data values ​​at corresponding moments from different data sources exceeds the threshold range, there may be errors in the data of a certain data source. During the consistency detection process, it is often possible to calculate quantitative indicators of deviations between data sources through data statistical methods such as root mean square, normalized root mean square, data association analysis, and time series invariance analysis, thereby effectively reflecting the differences between multi-source data. The multivariate consistency index reflects the authenticity and reliability of the data, and its calculation formula is as follows: (12) Where N is 1440 (measurement data) or 96 (prediction data). If the number of data sources is 2, then is the calculated root mean square error value. If the number of data sources is greater than 2, the root mean square error value between each two data sources is calculated .

[0069] In an optional embodiment, data timeliness is to evaluate the timeliness of the data. The evaluation of data timeliness requires measuring the timeliness of the data. Combined with the data reporting timeliness stipulated in the asset planning, whether the statistical data is submitted according to the prescribed time is used to evaluate the degree of failure of the value of the regulatory data assets. The formula is as follows: (13) (14) The larger the value, the better the data timeliness and the stronger the activity.

[0070] In an optional embodiment, data scale is reflected in the amount of data accessed and stored. This article measures and calculates the amount of data collected and accessed by the control cloud big data platform. The evaluation of the data scale of the control data asset is completed by introducing the expert scoring method.

[0071] Specifically, the steps for evaluating the scale of data assets by the expert scoring method are as follows: (1) Determine the evaluation objectives and criteria Objective: Assess and regulate the data scalability of data assets.

[0072] Standards: The evaluation criteria for data scale may include the total amount, type, coverage, growth rate, etc. of data.

[0073] (2) Establishing an expert team Select experts: Invite experts from the fields of power grid control, data analysis, and data asset management to form an evaluation team.

[0074] Determine the number of experts: Based on the complexity of the assessment and the amount of data, determine the appropriate number of experts, at least 5.

[0075] (3) Design scoring system Scoring indicators: Based on the evaluation criteria for data scale, design specific scoring indicators, such as total data volume score, data type score, coverage score, etc.

[0076] Scoring criteria: Develop detailed scoring criteria for each scoring indicator. For example, the total amount of data can be graded and scored based on the actual amount of data, and the types of data can be scored based on the number and quality of the types, etc.

[0077] (4) Expert rating Provide background information: Provide the expert group with background information on the regulatory data assets, including the source, type, and quantity of the data.

[0078] Independent scoring: Have expert panel members independently score the data scalability of regulatory data assets based on a scoring system and scoring criteria.

[0079] (5) Summarize the scoring results Calculate the average score: Summarize the scores of each expert and calculate the average score of each scoring indicator.

[0080] Determine the comprehensive score: Based on the weight of each scoring indicator (which can be determined based on actual conditions), calculate the comprehensive score of the data scale of the regulatory data assets.

[0081] In an optional embodiment, the control data connectivity is used to measure the ease with which different data assets can be integrated and utilized. The data value is reflected in the information expansion and value-added realization that can be generated after being associated with other business system data. The evaluation of the data connectivity of the control data assets is completed by introducing the expert scoring method.

[0082] Specifically, the steps of the expert scoring method to evaluate the data relevance of regulatory data assets are as follows: (1) Clarify evaluation objectives and standards Objective: Assess data relevance of governance data assets.

[0083] Standards: The evaluation criteria for data relevance may include the difficulty of data integration, the convenience of data sharing, the logical consistency between data, and the complementarity of data with other business systems.

[0084] (2) Establishing an expert team Select experts: Invite experts from fields such as power grid regulation, data analysis, and data asset management to form an evaluation team.

[0085] Determine the number of experts: Based on the complexity of the assessment and the amount of data, determine the appropriate number of experts, at least 5.

[0086] (3) Design scoring system Scoring indicators: Based on the evaluation criteria for data relevance, design specific scoring indicators, such as data integration difficulty score, data sharing convenience score, data logic consistency score, etc.

[0087] Scoring criteria: Develop detailed scoring criteria for each scoring indicator. For example, the difficulty of data integration can be evaluated based on the time and resources required for integration, and the convenience of data sharing can be evaluated based on the process and efficiency of data sharing.

[0088] (4) Provide background information Provide the expert team with background information on regulatory data assets, including the source, type, volume and relationship with other business systems.

[0089] (5) Expert rating Independent scoring: Have expert panel members independently score the data relevance of regulatory data assets based on the scoring system and scoring criteria.

[0090] Rating summary: Summarize the scores of each expert and calculate the average score for each rating indicator.

[0091] (6) Determine the weight According to the importance of each scoring indicator, their weights are determined. The weights can be determined through expert discussion, questionnaire survey or statistical methods.

[0092] (7) Calculation of comprehensive score Based on the average score and weight of each scoring indicator, the comprehensive score of the data relevance of the regulatory data assets is calculated.

[0093] S4. Calculate the value of the power grid control data asset based on the evaluation index scores and weight coefficients corresponding to the various influencing factors.

[0094] This solution uses the five indicators of data asset value mentioned above to construct a hierarchical analysis model for regulating data asset value assessment, and the corresponding weight values ​​are calculated according to the formula of the hierarchical analysis method.

[0095] Specifically, the power grid control data asset value is calculated based on the evaluation index scores and weights of each influencing factor, including: (15) in, ; Indicates the value price unit of measurement. It is the value of power grid control data assets; is the evaluation index score of data integrity, is the evaluation index score of data diversity, is the evaluation index score of data timeliness, is the evaluation index score of data scale, is the evaluation index score of data relevance, They represent the weight coefficients of the corresponding influencing factors respectively.

[0096] This solution takes the control data object as the core, integrates the data asset planning method of electrical physical characteristics, time polymorphism characteristics, multi-source information characteristics and business meaning, and designs a technical method for the value assessment of power grid control data assets that takes into account the data ontology attributes and power grid operation mechanism. First, the deep-level characteristics, laws and mechanisms of power grid control data are analyzed in all aspects to establish an element system for the value assessment of power control data assets; secondly, the hierarchical analysis method and expert scoring method are used to construct a control data asset value assessment model. The control data asset assessment method designed in this solution can quantify the value assessment of control data assets, promote the value realization of data in "internal value-added and external efficiency-enhancing", and improve the data management capabilities of the control big data platform and the level of lean dispatching management.

[0097] Example 2 like Figure 3 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a power grid control data asset value assessment device, comprising: A factor determination module is used to determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance; The first calculation module is used to calculate the weight coefficient corresponding to each influencing factor by using the hierarchical analysis method; The second calculation module is used to calculate the evaluation index score of each influencing factor; The third calculation module is used to calculate the value of the power grid control data asset according to the evaluation index score and the weight coefficient corresponding to each influencing factor.

[0098] Example 3 like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a method for evaluating the value of power grid control data assets; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0099] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a power grid regulation data asset value assessment method in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0100] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0101] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0102] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for evaluating the value of power grid regulation data assets, and the processor 102 can execute the plurality of instructions to implement: Determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance; The weight coefficients corresponding to each influencing factor are calculated using the hierarchical analysis method; Calculate the evaluation index scores of each influencing factor; The power grid control data asset value is calculated based on the evaluation index score and the weight coefficient corresponding to each influencing factor.

[0103] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0104] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the value of power grid control data assets, characterized in that: The steps include: Determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance; The weight coefficients corresponding to each influencing factor are calculated using the hierarchical analysis method; Calculate the evaluation index scores of each influencing factor; The power grid control data asset value is calculated based on the evaluation index scores and weight coefficients corresponding to various influencing factors.

2. The method for evaluating the value of power grid control data assets according to claim 1, characterized in that: The power grid control data asset value is calculated based on the evaluation index scores and weights of each influencing factor, including: in, ; Indicates the value price unit of measurement. It is the value of power grid control data assets; is the evaluation index score of data integrity, is the evaluation index score of data diversity, is the evaluation index score of data timeliness, is the evaluation index score of data scale, is the evaluation index score of data relevance, They represent the weight coefficients of the corresponding influencing factors respectively.

3. The method for evaluating the value of power grid control data assets according to claim 2, characterized in that: The evaluation index score of data integrity is calculated as follows: Determine each first influencing factor of data integrity; wherein each first influencing factor includes asset integrity rate, quality integrity rate, governance integrity rate and data integrity rate; Calculate the index value of each first impact factor; Calculate the average value of each first impact factor index value as the evaluation index score of data integrity.

4. The method for evaluating the value of power grid control data assets according to claim 2, characterized in that: The evaluation index score of data diversity is calculated as follows: Identify the different data sources for grid regulation data assets; For the data attribute information of the same power grid control data asset from different data sources, a consistent accuracy evaluation calculation is performed to obtain the evaluation index score of data diversity.

5. The method for evaluating the value of power grid control data assets according to claim 2, characterized in that: The evaluation index score of data timeliness is calculated as follows: Determine the time limit for submitting data as required by asset planning; Statistics on the time of uploading power grid control data at each measuring point; Calculate the timeliness rate of each measuring point based on the data reporting timeliness and upload time; The timeliness data of each measuring point is averaged to obtain the evaluation index score of data timeliness.

6. The method for evaluating the value of power grid control data assets according to claim 2, characterized in that: The data scale evaluation index score is calculated as follows: Determine the various evaluation criteria for data scale, determine specific scoring indicators based on the various evaluation criteria for data scale, and formulate scoring criteria for each scoring indicator; Obtain each expert's score on the data scale of power grid control data assets based on the scoring system and scoring criteria; Summarize the scores of each expert and calculate the average score of each scoring indicator; The comprehensive score of the data scale of the regulated data assets is calculated based on the weight of each scoring indicator, and the comprehensive score is used as the evaluation indicator score of the data scale.

7. The method for evaluating the value of power grid control data assets according to claim 2, characterized in that: The evaluation index score of data relevance is calculated as follows: Determine the evaluation criteria for data relevance; design specific scoring indicators based on the evaluation criteria for data relevance; formulate scoring criteria for each scoring indicator; Ask the expert panel members to independently score the data relevance of the regulatory data assets according to the scoring system and scoring criteria; summarize the scores of each expert and calculate the average score of each scoring indicator; Determine the corresponding weight according to the importance of each scoring indicator; The comprehensive score of data relevance is calculated according to the average score and weight of each scoring indicator, which is used as the evaluation indicator score of data relevance.

8. A device for evaluating the value of power grid control data assets, characterized in that: include: A factor determination module is used to determine the influencing factors of the power grid control data asset value assessment; wherein the influencing factors include data integrity, data diversity, data timeliness, data scale and data relevance; The first calculation module is used to calculate the weight coefficient corresponding to each influencing factor by using the hierarchical analysis method; The second calculation module is used to calculate the evaluation index score of each influencing factor; The third calculation module is used to calculate the value of the power grid control data asset according to the evaluation index score and the weight coefficient corresponding to each influencing factor.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the power grid regulation data asset value assessment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the power grid regulation data asset value assessment method as described in any one of claims 1 to 7 is implemented.

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