Power grid enterprise scientific and technological achievement conversion benefit quantification processing method and system

Abstract

The invention discloses a power grid enterprise scientific and technological achievement conversion benefit quantification processing method and system. The method comprises the following steps: constructing a power grid enterprise scientific and technological achievement conversion benefit quantitative evaluation model based on indexes of scientific value, technical value, economic value, social value and cultural value; layering each index of the evaluation model, determining an elastic coefficient according to the importance of each layer of index, obtaining an influence function of each index based on the elastic coefficient, and calculating the weight of each index through the influence function; and constructing a Vague value evaluation statement of each index through a Vague set theory, converting the evaluation statements into a Vague set evaluation matrix, and carrying out comprehensive evaluation on the transformation benefit of the scientific and technological achievements of the power grid enterprise based on the Vague set evaluation matrix and the weight of each index. According to the scheme of the invention, accurate quantification and optimal management of comprehensive benefits of scientific and technological achievement conversion are realized.

Description

Technical Field

[0001] This invention belongs to the field of technology transfer evaluation, and specifically relates to a method and system for quantifying the benefits of technology transfer in power grid enterprises. Background Technology

[0002] Against the backdrop of energy structure transformation and the accelerated construction of new power systems, power grid enterprises, as the core implementers of the national energy strategy, directly impact the innovation-driven development capability and sustainable development level of the power industry through the efficiency and effectiveness of their technology transfer. In recent years, the state has attached great importance to improving the evaluation system for scientific and technological innovation achievements, explicitly proposing a comprehensive and accurate evaluation of the "scientific, technological, economic, social, and cultural" values ​​of scientific and technological achievements, and issuing relevant national standards, providing a scientific, systematic, and unified framework for evaluating scientific and technological achievements. Power grid enterprises have actively responded to national policies, revising their management methods for technology transfer and exploring innovative models, aiming to improve the overall effectiveness of their innovation management system. However, traditional evaluation methods often focus on the single dimension of economic value, failing to systematically reflect the comprehensive performance of scientific and technological achievements in multiple value dimensions such as strategic synergy, social benefits, environmental contributions, and technological leadership. This results in biased evaluation results, limited decision-making support capabilities, and an inability to fully meet the needs for accurate measurement of the comprehensive benefits of scientific and technological achievements in the context of new power systems.

[0003] In existing technologies, the Analytic Hierarchy Process (AHP) and traditional fuzzy comprehensive evaluation methods are widely used in the evaluation of the benefits of technology transfer in power grid enterprises. The AHP method relies on pairwise comparisons of the relative importance of indicators by experts, which can easily lead to distorted weight allocation due to inconsistent judgment matrices. Furthermore, the number of comparisons increases significantly with the number of indicators, resulting in high computational complexity and low efficiency. Traditional fuzzy comprehensive evaluation methods use relatively simple membership function designs, typically employing a single membership value. This fails to fully express the mixed information of support, opposition, and abstention in expert evaluations, leading to the loss of intermediate information and making the evaluation results prone to deviating from reality. In addition, existing methods face challenges in indicator quantification, struggling to achieve a balanced measurement of the five dimensions of value, especially when dealing with dimensions such as technological value, social value, and cultural value. Quantitative biases or information gaps often occur, and the lack of effective measurement tools makes it difficult to achieve comprehensive and accurate quantitative evaluation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for quantifying the benefits of technology transfer in power grid enterprises, thereby resolving the technical problems existing in current methods for evaluating the benefits of technology transfer in power grid enterprises.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution.

[0006] This invention first discloses a method for quantifying the benefits of technology transfer in power grid enterprises, which includes the following steps: Step 1: Based on scientific value, technological value, economic value, social value, and cultural value indicators, construct a quantitative evaluation model for the benefits of the transformation of scientific and technological achievements of power grid enterprises; Step 2: Stratify the various indicators of the evaluation model, determine the elasticity coefficient according to the importance of each indicator, obtain the influence function of each indicator based on the elasticity coefficient, and calculate the weight of each indicator through the influence function. Step 3: Construct Vague value evaluation statements for each indicator using Vague set theory, convert the evaluation statements into Vague set evaluation matrices, and conduct a comprehensive evaluation of the technological achievement transformation benefits of power grid enterprises based on the Vague set evaluation matrix and the weights of each indicator.

[0007] The present invention further includes the following preferred embodiments: The scientific value includes indicators such as the degree of innovation in principles or methods, the ability to form technical standards, and the number of breakthroughs in key technologies; the technological value includes indicators such as the level of improvement in technology maturity, the rate of improvement in reliability or security, and the degree of improvement in key technical performance parameters; the economic value includes indicators such as the internal rate of return on investment, the scale of external market revenue, and the rate of improvement in asset utilization efficiency; the social value includes indicators such as the contribution to clean energy consumption, power supply quality and user satisfaction, and the driving effect on the industrial chain; and the cultural value includes indicators such as the effect of innovation culture and brand image enhancement, talent cultivation and team building, and the level of knowledge accumulation and sharing.

[0008] The stratification of the various indicators of the evaluation model further includes: Based on expert opinions in the indicator set The indicator with the highest pre-determined importance is selected as the optimal indicator. ; The remaining indicators, excluding the optimal indicator, are divided into different levels according to their importance: Layer: Among the other indicators besides the optimal indicator, the indicators have the same importance as the optimal indicator, or the indicators have less than twice the importance of the optimal indicator; Layer: Among the remaining indicators, those that are two to three times less important than the best indicator; And so on, Layer: Among the remaining indicators, those with lower importance than the optimal indicator. Doubled The index is times.

[0009] The determination of elasticity coefficients based on the importance of each level of indicators further includes: For the divided levels , : (1) And for any ,like ,but ; At any level In the middle, define the optimal index within the layer. The importance of each of the other indicators within the layer is compared with that of the optimal indicator, and the result is denoted as... ; Indicates the first In the hierarchy, the first The importance of each indicator compared to the best indicator at that layer is defined. For interval Integers above: (2) In the formula, Represents a set The number of indicators in the data; according to Value defines the elasticity coefficient : .

[0010] The step of calculating the weights of each indicator using the influence function further includes: Calculate the influence function of each indicator : (3) In the formula, Indicates the first The first in the hierarchy One indicator; according to Calculate the weights of the optimal metric: (4) Furthermore, the weights of the remaining indicators are as follows: (5) Finally, the weights of all indicators are obtained. .

[0011] The step of converting evaluation statements into a Vague set evaluation matrix further includes: Experts will evaluate each indicator one by one according to a given set of comments. Let represent any one of the transformation benefit evaluation indicators, and let the set of comments be . Construct the Vague set evaluation matrix R between the evaluation index system C and the comment set V: (6) Each row represents 5 comment levels, and each column corresponds to the Vague set membership degree of each evaluation indicator to a certain level in the comment set. Indicators Corresponding comment level The Vague value comment, and has ; , .

[0012] The comprehensive evaluation of the technological achievement transformation benefits of power grid enterprises based on the Vague set evaluation matrix and the weights of each indicator further includes: Based on the weights W of the indicators and the Vague set evaluation matrix R, a comprehensive evaluation based on the Vague set is performed: (7) (8) In the formula, The comprehensive evaluation results are based on the Vague set. To assign ratings to the object to be evaluated Vague value comments, For matrix multiplication operators in Vague sets. Vague sets are finite sum operators; The evaluation results are determined according to the principle of maximizing membership degree, and a relative scoring function is used as the ranking rule for the membership degree of the Vague set: (11)

[0013] This invention also discloses a system for quantifying the benefits of technology transfer in power grid enterprises, utilizing the aforementioned method for quantifying the benefits of technology transfer in power grid enterprises, comprising: The evaluation model construction module is used to construct a quantitative evaluation model for the benefits of the transformation of scientific and technological achievements of power grid enterprises based on scientific value, technological value, economic value, social value, and cultural value indicators. The indicator weighting module is used to stratify the various indicators of the evaluation model, determine the elasticity coefficient according to the importance of each level of indicators, obtain the influence function of each indicator based on the elasticity coefficient, and calculate the weight of each indicator through the influence function. The comprehensive evaluation module is used to construct Vague value evaluation statements for each indicator using Vague set theory, convert the evaluation statements into Vague set evaluation matrices, and conduct a comprehensive evaluation of the benefits of power grid enterprises' scientific and technological achievements transformation based on the Vague set evaluation matrix and the weights of each indicator.

[0014] Accordingly, this application also discloses a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the aforementioned method for quantifying the benefits of the transformation of scientific and technological achievements of power grid enterprises.

[0015] Accordingly, this application also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned method for quantifying the benefits of the transformation of scientific and technological achievements of power grid enterprises.

[0016] The beneficial effects of this invention are that, compared with existing technologies, it provides a method and system for quantifying the benefits of technology transfer in power grid enterprises. It systematically designs multi-level evaluation indicators from five dimensions: scientific value, technological value, economic value, social value, and cultural value. This not only covers traditional economic dimensions but also strengthens the quantitative expression of soft indicators such as strategic synergy, social benefits, and environmental contributions. This overcomes the shortcomings of existing evaluation methods in multi-dimensional value balance measurement, achieving a precise characterization of the comprehensive benefits of power grid enterprises as both technology-intensive and public utility entities. The invention introduces the Layered Weighted Approach (LBWA) as the core means of calculating indicator weights. Through a dual mechanism of hierarchical strength function and relative strength coefficient, it optimizes the weight allocation process. Based on the setting of benchmark anchor points and the ranking of the relative importance of indicators within a hierarchy, it significantly reduces the complexity of expert judgment and the number of comparisons. This avoids the weight distortion and low operational efficiency problems caused by frequent failures in the consistency verification of the judgment matrix in the traditional Analytic Hierarchy Process (AHP), thereby improving the scientific nature and decision-making efficiency of weight determination. Vague set theory was adopted to improve the fuzzy information processing stage. Through a two-dimensional representation framework of true and false membership, it achieved precise capture of mixed information such as support, opposition, and abstention in expert evaluations. Compared to the limitations of traditional fuzzy comprehensive evaluation methods that rely solely on a single membership function, Vague sets can effectively express uncertain cognitive states, reduce intermediate information loss, and thus improve the credibility of evaluation results and adaptability to complex decision-making scenarios. The combination of these techniques addresses the inherent shortcomings of existing technologies in indicator quantification, weight calculation, and uncertainty handling, and significantly enhances the industry adaptability and practical value of the evaluation model in the specific environment of power grid enterprises. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the indicator weighting results in this invention. Detailed Implementation To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0018] The embodiments described in this application are merely some, not all, embodiments of the present invention. Based on the spirit of the present invention, other embodiments obtained by those skilled in the art without inventive effort are all within the protection scope of the present invention.

[0019] To address the shortcomings of existing technologies, this invention proposes a method and system for quantifying the benefits of technology transfer in power grid enterprises. It optimizes the weight allocation process by introducing a hierarchical weighting mechanism; enhances the precision of fuzzy information processing by employing Vague set theory; and constructs a multi-dimensional indicator system and integrated evaluation model tailored to the characteristics of the power grid, thereby achieving a scientific, efficient, and accurate quantitative evaluation of the benefits of technology transfer.

[0020] The method for quantifying the benefits of technology transfer in power grid enterprises disclosed in this invention includes the following steps: Step 1: Based on scientific value, technological value, economic value, social value, and cultural value indicators, construct a quantitative evaluation model for the benefits of the transformation of scientific and technological achievements of power grid enterprises.

[0021] Against the backdrop of current energy structure transformation and the construction of new power systems, a comprehensive evaluation system based on a five-element value perspective should be constructed.

[0022] The scientific value dimension reflects the degree to which scientific and technological achievements contribute to the knowledge system of the power sector. As a technology-intensive industry, power grid companies need to not only solve practical engineering problems but also make breakthroughs in the fundamental theories of power science through their innovation activities. This dimension, by measuring the degree of innovation in principles, the ability to form technical standards, and the number of breakthroughs in key technologies, reflects the profound impact of achievements on promoting the development of disciplines and advancing technological progress in the industry, providing theoretical support and technological reserves for the long-term development of the industry.

[0023] The technological value dimension focuses on the improvement of the technological effectiveness of scientific and technological achievements in practical applications. Power grid systems have extremely high requirements for safety, reliability, and operational efficiency. This dimension, by assessing the improvement level of technology maturity, the rate of improvement in reliability / safety, and the degree of improvement in key technical performance parameters, directly reflects the contribution of scientific and technological achievements to the essential function of the power grid—safe and reliable power supply—and demonstrates the direct supporting role of technological progress in the core business of the power grid.

[0024] The economic value dimension measures the economic benefits and resource utilization efficiency generated by scientific and technological achievements. As large state-owned enterprises, power grid companies must not only preserve and increase the value of state-owned assets but also continuously improve operational efficiency. This dimension comprehensively evaluates the contribution of scientific and technological achievements in cost reduction and efficiency improvement, market value creation, and asset allocation optimization through indicators such as internal rate of return on investment, external market revenue scale, and asset utilization efficiency improvement rate, reflecting the economic rationality of enterprises as market players.

[0025] The social value dimension highlights the social responsibility of power grid companies as public utilities. Against the backdrop of energy transition and the "dual-carbon" goal, power grid companies need to fully leverage their social functions in clean energy, serving the public, and driving industrial development. This dimension, through indicators such as the contribution to clean energy consumption, power supply quality and user satisfaction, and the industrial chain impact, reflects the external value of scientific and technological achievements in serving national strategies, improving people's well-being, and promoting industrial development.

[0026] The cultural value dimension focuses on the contribution of technological achievements to enhancing organizational soft power. Sustainable development of power grid companies requires not only technological accumulation but also the support of an innovative culture and a strong talent pool. This dimension assesses the effectiveness of innovation culture in enhancing brand image, talent development and team building, and the level of knowledge accumulation and sharing. It reflects the profound impact of technological achievements on shaping the company's innovation ecosystem, improving organizational learning capabilities, and strengthening cultural soft power, thus providing intrinsic motivation for continuous innovation.

[0027] Table 1

[0028] By constructing a quantitative evaluation index system for the transformation benefits of scientific and technological achievements of power grid enterprises based on the five values ​​of "science, technology, economy, society, and culture," the system systematically covers multiple value dimensions of power grid enterprises as technology-intensive and public utility entities. This overcomes the limitations of traditional methods that only focus on economic value, enabling the evaluation results to comprehensively reflect the integrated benefits of scientific and technological achievements in strategic synergy, industry progress, social responsibility, and cultural soft power, thus providing a multi-dimensional scientific basis for management decisions.

[0029] Step 2: Stratify the various indicators of the evaluation model, determine the elasticity coefficient according to the importance of each indicator, obtain the influence function of each indicator based on the elasticity coefficient, and calculate the weight of each indicator through the influence function.

[0030] Considering that the constructed quantitative evaluation index system for the transformation benefits of scientific and technological achievements in power grid enterprises is mainly composed of qualitative indicators, this invention adopts a subjective weighting method to calculate the weights of each indicator. The LBWA (Level-Based Weight Assessment) method, based on hierarchical structure characteristics, implements weight calculation through a dual mechanism of the Level Strength Function (LSF) and the Relative Strength Coefficient (RSC). First, decision-makers determine the most important indicator as the benchmark anchor. Then, pairwise comparisons are used to establish the relative importance ranking of indicators within the hierarchy. Finally, a linear programming model is constructed to solve for the optimal weight vector. This design significantly differs from the traditional AHP method's reliance on pairwise comparisons of all indicators, significantly reducing the number of comparisons and effectively avoiding the complexity of consistency verification of the judgment matrix.

[0031] The steps for assigning weights to metrics based on LBWA are as follows: Step 2.1: Determine the optimal index. Based on expert opinions, select the optimal index from the set of indicators. The indicator with the highest pre-determined importance is defined as the optimal indicator. .

[0032] Step 2.2: Indicator Stratification. The remaining indicators, excluding the optimal indicator, are divided into different strata according to their importance. The stratification criteria are as follows: Layer: Among the other indicators besides the optimal indicator, those with the same importance as the optimal indicator, or those with less than twice the importance of the optimal indicator (excluding twice the importance).

[0033] Layer: Among the remaining indicators, those that are two to three times less important than the best indicator (excluding three times).

[0034] … Layer: Among the remaining indicators, those with lower importance than the optimal indicator. Doubled times (excluding) The index is (times).

[0035] By stratifying the indicators as described above, a rough definition of their importance can be formed. Regarding the stratification... ( ): (1) And for any ,like ,but .

[0036] Step 2.3: Assess the importance of indicators at different levels. In the middle, define the optimal index within the layer. Then, the importance of other indicators within the layer is compared with that of the optimal indicator, and the result is denoted as... . Indicates the first In the hierarchy, the first The importance of each indicator compared to the optimal indicator at that level, the first The greater the importance of each indicator, The smaller the value, and the more defined For interval An integer on the integer. Specifically, when ,have ,and . It is a constant determined by the results of the importance stratification of indicators: (2) In the formula, Represents a set The number of indicators in the data.

[0037] Step 2.4: Determine the LBWA elasticity coefficient .constant This indicates the greatest difference in the assessment of the importance of indicators within a layer, based on... Value, defining the elasticity coefficient of LBWA satisfy , usually take .

[0038] Step 2.5: Calculate the influence function of each indicator The formula is as follows: (3) In the formula, Indicates the first The first in the hierarchy One indicator.

[0039] Step 2.6: Calculate the indicator weights. Based on... The weight of the optimal indicator can be expressed as: (4) Furthermore, the weights of the remaining indicators are as follows: (5) Finally, the weights of all indicators are obtained, denoted as: Compared to weighting methods such as AHP, the LBWA method minimizes subjective judgment errors through a hierarchical constraint model, ensuring that weight allocation conforms to the decision-maker's original preferences. Furthermore, it eliminates the need for consistency checks, avoids information distortion caused by matrix adjustments, and significantly improves decision-making efficiency.

[0040] By adopting the hierarchical weighting method (LBWA) for indicator weight calculation, especially by determining the optimal indicator as the benchmark anchor, hierarchizing indicators according to the importance multiple relationship, calculating the influence function and solving the weight vector, the judgment burden of experts in the pairwise comparison of indicators is significantly reduced. This avoids the problem of repeated adjustments caused by inconsistent judgment matrices in the traditional AHP method. Thus, while ensuring the scientific nature of the weights, the calculation efficiency and operation convenience of weight allocation are greatly improved. It is especially suitable for the evaluation system of power grid enterprises with multi-level and multi-indicator characteristics.

[0041] Step 3: Construct Vague value evaluation statements for each indicator using Vague set theory, convert the evaluation statements into Vague set evaluation matrices, and conduct a comprehensive evaluation of the technological achievement transformation benefits of power grid enterprises based on the Vague set evaluation matrix and the weights of each indicator.

[0042] The quantitative evaluation of the benefits of technology transfer in power grid enterprises involves numerous factors, and the analysis process suffers from significant data and information uncertainty and incompleteness. Fuzzy comprehensive evaluation can effectively address the nonlinear relationship between risk factors and comprehensive risk metrics. However, traditional fuzzy theory suffers from the problem that membership degrees do not satisfy additivity, and operations involving taking larger or smaller values ​​can easily lose intermediate information, leading to distorted evaluation results. Therefore, this invention proposes an improved evaluation method based on Vague set theory.

[0043] In the quantitative evaluation of the benefits of technology transfer in power grid enterprises, the characteristics of indicators are quantified by constructing a Vague evaluation matrix. That is, for any secondary indicator, experts can assign it an evidentiary strength (true membership degree) that meets the friendliness requirements. ) and violation of the strength of evidence (false membership) The difference between the two Reflects the net support of the indicator, but not the undetermined domain. This represents the cognitive blind spot, thereby enabling the mathematical expression of mixed information that is partly known and partly unknown.

[0044] The specific steps for using Vague to evaluate the benefits of technology transfer in power grid companies are as follows: Step 3.1: Assign corresponding evaluation statements for each evaluation indicator. Referring to the actual situation of technology transfer in power grid enterprises, this invention provides a corresponding set of evaluation statements. = (low conversion efficiency, relatively low conversion efficiency, average conversion efficiency, relatively high conversion efficiency, high conversion efficiency) 5 levels, and at the same time a certain number of experts select appropriate language variables to express evaluation opinions.

[0045] Step 3.2: Determine the weights of all evaluation indicators according to the aforementioned LBWA method.

[0046] Step 3.3: Construct the Vague set evaluation matrix, that is, ask experts to judge each indicator one by one according to the given set of comments. Let represent any one of the transformation benefit evaluation indicators, and let the set of comments be . Construct the Vague set evaluation matrix R between the evaluation index system C and the comment set V: (6) Each row in the formula represents 5 comment levels, and each column corresponds to the Vague set membership degree of each evaluation indicator to a certain level in the comment set. Indicators Corresponding comment level The Vague value comment, and has Experts selected or abstained from voting on each indicator according to the set of comments. For example, if 10 experts commented on a certain indicator... If, in the evaluation, 4 people selected "low conversion efficiency," 3 selected "relatively low," 1 selected "average," 1 selected "relatively high," and 1 person declined to participate in the evaluation, then:

[0047] Similarly, Vague value comments for all indicators can be obtained, and then the Vague set evaluation matrix of the entire indicator system can be constructed.

[0048] Step 3.4: Based on the weights W of the indicators and the Vague set evaluation matrix R, perform a comprehensive evaluation based on the Vague set: (7) (8) In the formula, The comprehensive evaluation results are based on the Vague set. To assign ratings to the object to be evaluated Vague value comments, For matrix multiplication operators in Vague sets. Let be the finite sum operation for the Vague set. Therefore, the above calculation requires the use of two fundamental formulas on the Vague set: scalar multiplication and finite sum operation. Let be... for Real numbers on the interval , For elements on the Vague set, , ,but: (9) (10) Finally, the evaluation results need to be determined according to the principle of maximizing membership degree. Since the Vague value is an interval number, a relative scoring function can be used as the ranking rule for the membership degree of the Vague set, as shown in the following formula: (11) The above formula represents the situation where the object to be evaluated belongs to a certain evaluation level, without considering abstention. ) accounts for all cases ( The greater the weight of the percentage, the greater the probability that the object to be evaluated belongs to that evaluation level. If the impact of the abstention is considered, the above formula represents the percentage of the abstention calculated according to... The proportions are subdivided infinitely until unknown information no longer affects the judgment of the membership degree of the evaluation object in terms of the evaluation level.

[0049] To further enhance the interpretability of the results, scores can be assigned to different comment levels, and a comprehensive score can be calculated by combining the scoring function values ​​of each comment level. : (12) In this invention, the following is set .

[0050] This step introduces a Vague set-based method for quantifying transformation benefits. Utilizing its two-dimensional representation framework of true and false membership, it can precisely capture support, opposition, and abstention information in expert evaluations, effectively handling the significant uncertainties and ambiguities present in the evaluation process. In constructing the Vague set evaluation matrix, performing weighted synthesis operations, and using a scoring function to derive the final evaluation result, it effectively avoids evaluation biases caused by information loss in traditional fuzzy sets, significantly improving the credibility and precision of the evaluation results. It is particularly suitable for the comprehensive quantification of soft indicators such as social and cultural value.

[0051] The beneficial effects of this invention are that, compared with existing technologies, it provides a method and system for quantifying the benefits of technology transfer in power grid enterprises. It systematically designs multi-level evaluation indicators from five dimensions: scientific value, technological value, economic value, social value, and cultural value. This not only covers traditional economic dimensions but also strengthens the quantitative expression of soft indicators such as strategic synergy, social benefits, and environmental contributions. This overcomes the shortcomings of existing evaluation methods in multi-dimensional value balance measurement, achieving a precise characterization of the comprehensive benefits of power grid enterprises as both technology-intensive and public utility entities. The invention introduces the Layered Weighted Approach (LBWA) as the core means of calculating indicator weights. Through a dual mechanism of hierarchical strength function and relative strength coefficient, it optimizes the weight allocation process. Based on the setting of benchmark anchor points and the ranking of the relative importance of indicators within a hierarchy, it significantly reduces the complexity of expert judgment and the number of comparisons. This avoids the weight distortion and low operational efficiency problems caused by frequent failures in the consistency verification of the judgment matrix in the traditional Analytic Hierarchy Process (AHP), thereby improving the scientific nature and decision-making efficiency of weight determination. Vague set theory was adopted to improve the fuzzy information processing stage. Through a two-dimensional representation framework of true and false membership, it achieved precise capture of mixed information such as support, opposition, and abstention in expert evaluations. Compared to the limitations of traditional fuzzy comprehensive evaluation methods that rely solely on a single membership function, Vague sets can effectively express uncertain cognitive states, reduce intermediate information loss, and thus improve the credibility of evaluation results and adaptability to complex decision-making scenarios. The combination of these techniques addresses the inherent shortcomings of existing technologies in indicator quantification, weight calculation, and uncertainty handling, and significantly enhances the industry adaptability and practical value of the evaluation model in the specific environment of power grid enterprises.

[0052] To verify the effectiveness and applicability of the quantitative evaluation model for the transformation benefits of technological achievements in power grid enterprises from the perspective of the five-element value framework constructed in this invention, a provincial power grid company in my country (hereinafter referred to as "L Power Grid Company") was selected as the empirical research object. As an important provincial subsidiary of State Grid Corporation of China, L Power Grid Company is responsible for operating the main power grid and urban and rural distribution networks within the province, undertaking the important responsibilities of ensuring regional energy security, promoting the consumption of clean energy, and driving energy transformation. The company has a large asset scale and a complex grid structure, with assets exceeding 140 billion yuan, annual electricity sales exceeding 200 billion kilowatt-hours, and serving over 20 million customers, making it a typical large-scale modern power grid enterprise. In recent years, L Power Grid Company has actively responded to the national innovation-driven development strategy, attaching great importance to scientific and technological innovation and achievement transformation. In the fields of new power system construction, source-grid coordinated development, and modern urban distribution networks, it has undertaken numerous national key projects, State Grid Corporation of China's annual R&D tasks, National Natural Science Foundation of China projects, and State Grid Corporation of China science and technology projects, carrying out original technological breakthroughs and promoting achievement transformation.

[0053] Index Weighting Results: To determine the weights of each evaluation index from the perspective of five - element value, a decision - making group was invited, consisting of experts from the science and technology management departments of power grid enterprises, the power field of universities, and the technology transformation centers of scientific research institutions. All members of the expert group have senior technical titles, with an average working experience of more than 10 years, and have a profound understanding of the technological innovation and achievement transformation of power grid enterprises. Through organizing special seminars and using the Delphi method for multiple rounds of opinion solicitation and feedback, a consensus on the relative importance between indicators was finally reached.

[0054] According to the calculation steps of the LBWA method, first, the expert group determines the optimal index. After discussion, considering that the most core mission of power grid enterprises is to ensure the safe and reliable power supply of the grid, Index C5 (Reliability / Security Improvement Rate) was unanimously recognized as the most important index, so it was set as the benchmark anchor point, that is . Subsequently, the expert group compared the remaining 14 indicators hierarchically according to their importance relative to C5. Based on the "importance multiple" relationship, all indicators were divided into 4 levels ( , , , ), and the hierarchical results are shown in Table 2: Table 2

[0055] Then, a refined relative importance judgment was made within each level. The expert group assigned an intensity coefficient to the indicators within each level. This coefficient is an integer between 0 and ( determined by the largest level base number. In this example , ). The smaller the coefficient value, the more important the indicator is within its level. According to the expert opinions, the importance evaluation values of all indicators relative to the optimal indicator within the level can be obtained , and then according to formula (3), the influence function of each indicator was calculated, and the results are shown in Table 3.

[0056] Table 3

[0057] Furthermore, according to formula (4), the weight of the optimal indicator can be calculated as:

[0058] Then, according to formula (5), the weight results of other indicators can be obtained, as shown in Figure 1 . Figure 1The weighting results of the indicators shown intuitively reflect the relative importance of each evaluation indicator under the five-dimensional value system in the evaluation system of the transformation benefits of scientific and technological achievements of power grid enterprises. The weight distribution shows that the three indicators (C4, C5, C6) of the technology value dimension have significantly higher weights than the indicators of other dimensions. Among them, the "reliability / safety improvement rate (C5)" has the highest weight (0.1512), highlighting the industry characteristic of power grid enterprises taking safe and reliable power supply as their core mission. The "technology maturity improvement level (C4)" and "key technology performance parameter improvement degree (C6)" rank second and third with weights of 0.1260 and 0.1080, respectively, indicating that technological feasibility and performance improvement are the basic support for the transformation of achievements. In the economic value dimension, the "internal rate of return on investment (C7)" has a weight of 0.0945, reflecting the importance that enterprises attach to operational efficiency, while the "external market revenue scale (C8)" and "asset utilization efficiency improvement rate (C9)" have relatively low weights, reflecting the strategic orientation of power grid enterprises, as public utility entities, to focus more on internal efficiency improvement rather than external market expansion. In the scientific value dimension, the weight of "principle / method innovation (C1)" (0.0630) is higher than the other two, indicating that theoretical innovation remains the core manifestation of scientific value. In the social value dimension, the weight of "contribution to clean energy consumption (C10)" (0.0687) is significantly higher than other social indicators, which aligns with the transformation needs of power grid companies under the "dual carbon" target. In the cultural value dimension, the weights of each indicator are relatively low but evenly distributed, indicating that while not core, it is indispensable as a support for soft power. Overall, the weight allocation results reflect both the technology-intensive and utility-oriented attributes of power grid companies and the hierarchical and synergistic nature of the five-element value system. This aligns with national policy guidance and corporate practice needs, validating the scientific validity and applicability of the LBWA weighting method.

[0059] 4.3 Quantitative Results of Technology Transfer Benefits To conduct a quantitative evaluation of the technological achievement transformation benefits of the L power grid enterprise under study, 20 experts in the field were invited to conduct a Vague set evaluation through a questionnaire survey. Among them, there were 11 experts from the technology management department of the power grid enterprise, 3 experts from the power field of universities, and 6 experts from the technology transformation center of scientific research institutes. All of them have senior technical titles, an average of more than 10 years of professional experience, and have a deep understanding of the technological innovation and achievement transformation of the power grid enterprise.

[0060] Regarding the evaluation index of principle / method innovation (C1), out of 20 experts, 1 believed that the L power grid company's transformation efficiency in this dimension was low, 3 believed it was relatively low, 6 believed it was average, 5 believed it was relatively high, 3 believed it was high, and 2 abstained. According to the Vague set construction rules, the Vague value comment corresponding to index C1 should be:

[0061] Similarly, Vague value comments for all evaluation indicators of L power grid enterprise can be obtained, forming the Vague set evaluation matrix of the evaluation indicator system of L power grid enterprise, as shown in Table 4.

[0062] Table 4. Experts' Vague set ratings for each indicator.

[0063] according to Figure 1 By assigning weights to each indicator and using the Vague values ​​for each indicator in Table 4, the weighted Vague values ​​for each indicator can be calculated. Then, through finite summation of the Vague set, the Vague values ​​for the performance evaluation indicators of the L power grid enterprise at five rating levels are obtained. For example, indicator C1 at rating level... The Vague value rating is as follows The indicator weight is Then the indicator is in The weighted Vague value comment is Similarly, we can obtain the weighted Vague values ​​of all indicators at each rating level, and thus obtain the Vague values ​​of the performance evaluation indicators of the L power grid enterprise at each rating level, as shown in Table 5.

[0064] Table 5

[0065] Based on the finite sum operation formula of the Vague set, the comprehensive Vague set evaluation of the achievement transformation benefits of L power grid enterprise can be calculated. Then, based on formula (11), the scoring function value of the achievement transformation benefits of L power grid enterprise at each evaluation level can be calculated: , , , , ,Right now The results show that the technology transfer efficiency of L power grid enterprise belongs to the fifth level, that is, the transfer efficiency is high. According to formula (12), the comprehensive score of the technology transfer efficiency of L power grid enterprise can be further calculated. point.

[0066] From the perspective of technological value, this dimension's indicators have a total weight of 0.3852, the highest among all dimensions. Specifically, the membership degrees of "C6 Key Technology Performance Parameter Improvement" and "C5 Reliability / Safety Improvement Rate" in the fifth evaluation level are [0.0540, 0.0648] and [0.0605, 0.0680], respectively, indicating that enterprises have made substantial progress in improving power grid technology performance and ensuring safe and reliable power supply. This result is highly consistent with the positioning of power grid enterprises as critical infrastructure operators, highlighting the core position of technological reliability and safety in the transformation of scientific and technological achievements. In the economic value dimension, the weight of "C7 Internal Rate of Return on Investment" is 0.0945, but its membership degree in the fifth evaluation level is only [0.0236, 0.0283], reflecting that although the enterprise's internal operating efficiency has improved to some extent, there is still considerable room for improvement in return on investment efficiency. In contrast, the weights of "C8 External Market Revenue Scale" and "C9 Asset Utilization Efficiency Improvement Rate" were relatively low, and the evaluation results were generally average, indicating that enterprises need to strengthen their efforts in the commercialization of scientific and technological achievements and the optimal allocation of assets. In the social value dimension, "C10 Clean Energy Consumption Contribution" achieved a membership degree of [0.0344, 0.0378] in the fifth evaluation level, demonstrating a relatively outstanding performance. This aligns with the social responsibility undertaken by power grid companies under the current energy transition context, reflecting the company's proactive role in serving the "dual carbon" goals. However, the evaluation results for "C11 Power Supply Quality and User Satisfaction" and "C12 Industrial Chain Driving Effect" were relatively weak, indicating that enterprises need to make further efforts in improving user service quality and driving industrial chain development. The weights of the indicators in the scientific value dimension are relatively balanced, but the evaluation results show that "C3 Key Technology Breakthroughs" has a high degree of membership in the fourth evaluation level ([0.0243, 0.0270]), while "C1 Principle / Method Innovation" and "C2 Technical Standard Formulation Ability" perform moderately. This indicates that the company has achieved significant results in solving practical technical problems, but there is still room for improvement in its leading role in theoretical innovation and standard setting. The weights of the indicators in the cultural value dimension are the lowest, and the evaluation results are generally at a moderate level. Among them, "C15 Knowledge Accumulation and Sharing Level" has a relatively good degree of membership in the fifth evaluation level ([0.0099, 0.0131]), but overall, the company still needs to strengthen its innovation culture construction, talent cultivation, and organizational knowledge management, which is crucial for cultivating the company's long-term innovation and development capabilities.

[0067] Overall, the technological achievements transformation benefits of L power grid enterprises are characterized by "technology-driven as the main driver, balanced economic and social value, and room for improvement in scientific and cultural value." This assessment not only objectively reflects the current state of technological achievements transformation of enterprises but also provides a clear direction for enterprises to optimize the allocation of scientific and technological resources and improve transformation efficiency in the future. It is recommended that enterprises continue to maintain and strengthen investment in advantageous areas such as power grid security and clean energy, while focusing on the economic benefits transformation of scientific and technological achievements and the construction of an innovative culture, so as to achieve balanced and coordinated development of the five elements of value.

[0068] In summary, this invention organically integrates the five-element value index system, LBWA hierarchical weighting, and Vague set fuzzy evaluation to form a scientific, efficient, and tailored integrated evaluation scheme for power grid enterprises. It not only solves the problems of low weight allocation efficiency, imprecise fuzzy information processing, and difficulty in multidimensional value quantification in existing technologies, but also achieves precise quantification and optimized management of the comprehensive benefits of scientific and technological achievements transformation, providing reliable technical support for power grid enterprises to improve the efficiency of scientific and technological resource allocation and the effectiveness of achievement transformation.

[0069] This invention can be a system, method, and / or computer program product. This invention also discloses a system for quantifying the benefits of technology transfer in power grid enterprises, based on the aforementioned method for quantifying the benefits of technology transfer in power grid enterprises, comprising: The evaluation model construction module is used to construct a quantitative evaluation model for the benefits of the transformation of scientific and technological achievements of power grid enterprises based on scientific value, technological value, economic value, social value, and cultural value indicators. The indicator weighting module is used to stratify the various indicators of the evaluation model, determine the elasticity coefficient according to the importance of each level of indicators, obtain the influence function of each indicator based on the elasticity coefficient, and calculate the weight of each indicator through the influence function. The comprehensive evaluation module is used to construct Vague value evaluation statements for each indicator using Vague set theory, convert the evaluation statements into Vague set evaluation matrices, and conduct a comprehensive evaluation of the benefits of power grid enterprises' scientific and technological achievements transformation based on the Vague set evaluation matrix and the weights of each indicator.

[0070] Based on the spirit of this invention, those skilled in the art will readily conceive of obtaining a computer program product based on the aforementioned method for quantifying the benefits of technology transfer in power grid enterprises. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded to enable a processor to implement various aspects of this disclosure. That is, this application also includes a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the aforementioned method for quantifying the benefits of technology transfer in power grid enterprises.

[0071] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0072] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0073] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for quantifying the benefits of technology transfer in power grid enterprises, characterized in that, Includes the following steps: Step 1: Based on scientific value, technological value, economic value, social value, and cultural value indicators, construct a quantitative evaluation model for the benefits of the transformation of scientific and technological achievements of power grid enterprises; Step 2: Stratify the various indicators of the evaluation model, determine the elasticity coefficient according to the importance of each indicator, obtain the influence function of each indicator based on the elasticity coefficient, and calculate the weight of each indicator through the influence function. Step 3: Construct Vague value evaluation statements for each indicator using Vague set theory, convert the evaluation statements into Vague set evaluation matrices, and conduct a comprehensive evaluation of the technological achievement transformation benefits of power grid enterprises based on the Vague set evaluation matrix and the weights of each indicator.

2. The method for quantifying the benefits of technology transfer in power grid enterprises according to claim 1, characterized in that, The scientific value includes indicators such as the degree of innovation in principles or methods, the ability to form technical standards, and the number of breakthroughs in key technologies; the technological value includes indicators such as the level of improvement in technology maturity, the rate of improvement in reliability or security, and the degree of improvement in key technical performance parameters; the economic value includes indicators such as the internal rate of return on investment, the scale of external market revenue, and the rate of improvement in asset utilization efficiency; the social value includes indicators such as the contribution to clean energy consumption, power supply quality and user satisfaction, and the driving effect on the industrial chain; and the cultural value includes indicators such as the effect of innovation culture and brand image enhancement, talent cultivation and team building, and the level of knowledge accumulation and sharing.

3. The method for quantifying the benefits of technology transfer in power grid enterprises according to claim 2, characterized in that, The stratification of the various indicators of the evaluation model further includes: Based on expert opinions in the indicator set The indicator with the highest pre-determined importance is selected as the optimal indicator. ; The remaining indicators, excluding the optimal indicator, are divided into different levels according to their importance: Layer: Among the other indicators besides the optimal indicator, the indicators have the same importance as the optimal indicator, or the indicators have less than twice the importance of the optimal indicator; Layer: Among the remaining indicators, those that are two to three times less important than the best indicator; And so on, Layer: Among the remaining indicators, those with lower importance than the optimal indicator. Doubled The index is times.

4. The method for quantifying the benefits of technology transfer in power grid enterprises according to claim 3, characterized in that, The determination of elasticity coefficients based on the importance of each level of indicators further includes: For the divided levels , : （1） And for any ,like ,but ; At any level In the middle, define the optimal index within the layer. The importance of each of the other indicators within the layer is compared with that of the optimal indicator, and the result is denoted as... ; Indicates the first In the hierarchy, the first The importance of each indicator compared to the best indicator at that layer is defined. For interval Integers above: （2） In the formula, Represents a set The number of indicators in the data; according to Value defines the elasticity coefficient : 。 5. The method for quantifying the benefits of technology transfer in power grid enterprises according to claim 4, characterized in that, The step of calculating the weights of each indicator using the influence function further includes: Calculate the influence function of each indicator : （3） In the formula, Indicates the first The first in the hierarchy One indicator; according to Calculate the weights of the optimal metric: （4） The weights of the remaining indicators are calculated as follows: （5） Finally, the weights of all indicators are obtained. .

6. The method for quantifying the benefits of technology transfer in power grid enterprises according to claim 5, characterized in that, The step of converting evaluation statements into a Vague set evaluation matrix further includes: Experts will evaluate each indicator one by one according to a given set of comments. Let represent any one of the transformation benefit evaluation indicators, and let the set of comments be . Construct the Vague set evaluation matrix R between the evaluation index system C and the comment set V: （6） Each row represents 5 comment levels, and each column corresponds to the Vague set membership degree of each evaluation indicator to a certain level in the comment set. Indicators Corresponding comment level The Vague value comment, and has ; , .

7. The method for quantifying the benefits of technology transfer in power grid enterprises according to claim 6, characterized in that, The comprehensive evaluation of the technological achievement transformation benefits of power grid enterprises based on the Vague set evaluation matrix and the weights of each indicator further includes: Based on the weights W of the indicators and the Vague set evaluation matrix R, a comprehensive evaluation based on the Vague set is performed: （7） （8） In the formula, The comprehensive evaluation results are based on the Vague set. To assign ratings to the object to be evaluated Vague value comments, For matrix multiplication operators in Vague sets. Vague sets are finite sum operators; The evaluation results are determined according to the principle of maximizing membership degree, and a relative scoring function is used as the ranking rule for the membership degree of the Vague set: （11）。 8. A system for quantifying the benefits of technology transfer in power grid enterprises, characterized in that, include: The evaluation model construction module is used to construct a quantitative evaluation model for the benefits of the transformation of scientific and technological achievements of power grid enterprises based on scientific value, technological value, economic value, social value, and cultural value indicators. The indicator weighting module is used to stratify the various indicators of the evaluation model, determine the elasticity coefficient according to the importance of each level of indicators, obtain the influence function of each indicator based on the elasticity coefficient, and calculate the weight of each indicator through the influence function. The comprehensive evaluation module is used to construct Vague value evaluation statements for each indicator using Vague set theory, convert the evaluation statements into Vague set evaluation matrices, and conduct a comprehensive evaluation of the benefits of power grid enterprises' scientific and technological achievements transformation based on the Vague set evaluation matrix and the weights of each indicator.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method for quantifying the benefits of the transformation of scientific and technological achievements of power grid enterprises according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the method for quantifying the benefits of the transformation of scientific and technological achievements of power grid enterprises as described in any one of claims 1-7.

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