Park comprehensive evaluation method and system considering efficiency scale competition

Abstract

The invention discloses a park comprehensive evaluation method and system considering # imgabs0 # efficiency scale competition, and aims to establish a set of comprehensive evaluation index system which takes # imgabs1 # efficiency as a core and combines traditional energy, environment and economic indexes, so that the real energy utilization efficiency and the overall sustainable development level of a park can be evaluated more comprehensively and deeply. Meanwhile, the method aims at introducing a dynamic scale competition mechanism, and through public and transparent comparison and sorting of key performance indicators such as # imgabs2 efficiency of all parks, mutual learning and benign competition between the parks are promoted, and improvement of the overall energy utilization efficiency of the areas is driven. Finally, the method aims at providing more refined and operable evaluation feedback, accurately positioning weak links of energy utilization through # imgabs3 efficiency analysis, providing more targeted improvement strategies and technical path selection for park managers and policy formulators, and powerfully supporting green low-carbon transformation and high-quality development of the park.

Description

Technical Field

[0001] The present invention relates to the technical field of park evaluation, and particularly relates to a comprehensive park evaluation method and system considering the competition of efficiency benchmarks. Background Art

[0002] As a key engine of economic growth, parks play a crucial role globally and have greatly promoted the economic development of regions and countries by aggregating enterprises, promoting industrial collaboration, and attracting investment. Under the goal of addressing global climate change and resource shortage challenges and achieving sustainable development, improving the energy efficiency and environmental performance of parks has become a core issue. To this end, various park evaluation guidelines and indicator systems have been developed and implemented at home and abroad, such as the "Park Energy Efficiency Evaluation Guidelines" in Beijing, the "Low-Carbon Park Evaluation Guidelines" in Shenzhen, and the international eco-industrial park (EIP) framework jointly developed by institutions such as the United Nations Industrial Development Organization (UNIDO) and the World Bank, aiming to evaluate and guide the sustainable development practices of parks from different dimensions.

[0003] However, through research, it is found that there are still several significant deficiencies in the existing technologies for comprehensive park evaluation. Although many current evaluation indicator systems cover multiple aspects such as energy consumption intensity, renewable energy utilization rate, pollutant emissions, and waste treatment, the common problem is that they mainly focus on the "quantity" of energy consumption or the apparent environmental impact, while ignoring the deep "quality" or effective utilization degree in the energy utilization process, that is, (Exergy) efficiency. This evaluation method based on energy quantity has a not fine enough analysis granularity and cannot comprehensively and deeply reflect the true efficiency and irreversible losses in the cascade utilization and conversion process of energy. This may lead to misjudgments on the true energy-saving potential and the optimization direction of energy utilization in parks. For example, a park may perform well in the energy consumption per unit output value index due to the large use of low-cost and low-grade energy, but in fact, it wastes a large amount of the work capacity of high-grade energy and fails to maximize the energy value.

[0004] In terms of evaluation methods, although the existing technologies use techniques such as scoring systems and index-based comprehensive evaluation methods, these methods often lack an endogenous motivation mechanism that can effectively stimulate parks to continuously improve efficiency. Many evaluations focus on the static evaluation of individual parks or the determination of compliance with preset standards, and rarely introduce a dynamic and public competition or benchmarking mechanism among parks based on key performance indicators (especially indicators reflecting the quality of energy utilization). Benchmark competition is an effective means to drive performance improvement by discovering gaps through performance comparison and learning from the advanced. However, in the existing park evaluation practices, especially based on The competition mechanism of efficiency scale has extremely limited application. In addition, traditional evaluation methods may over-focus on macroeconomic indicators, such as GDP contribution and tax revenue, thereby relatively ignoring technical details such as energy efficiency that are crucial to long-term sustainability.

[0005] Therefore, how to construct a system that can deeply reflect the quality of energy utilization ( How to innovatively introduce a scale competition mechanism to effectively promote efficiency improvement, and propose a comprehensive evaluation decision-making method that can dynamically reflect performance and provide refined and operational improvement guidance, is a severe challenge and key technical problem that needs to be solved in the current park evaluation field. Effectively solving these problems has important theoretical value and practical guiding significance for guiding parks to optimize energy resource allocation, promote green and low-carbon transformation, and achieve high-quality sustainable development. Summary of the invention

[0006] The present invention provides a consideration The comprehensive evaluation method and system of the park with efficiency scale competition is to solve the problems existing in the existing technology that ignore the deep "quality" or effective utilization degree in the energy utilization process and lack the endogenous driving mechanism that can effectively stimulate the park to continuously improve efficiency.

[0007] According to the first aspect, an embodiment provides a consideration A comprehensive evaluation method for parks with efficiency scale competition, the method comprising:

[0008] Construct a hierarchical evaluation index set for the park, including energy layer, environmental layer and economic layer. The energy layer includes Efficiency indicators;

[0009] Based on the analytic hierarchy process, the AHP weights of the evaluation indicators at each level are calculated according to the expert judgment matrix;

[0010] Based on the entropy weight method, the entropy weight of each layer of evaluation indicators is calculated according to the information entropy;

[0011] Based on the AHP weight and entropy weight of each layer of evaluation indicators, the comprehensive weight of each layer of evaluation indicators is calculated;

[0012] According to the regulations Efficiency benchmark value, optimized and adjusted based on the benchmark competition mechanism Comprehensive weight value of efficiency index;

[0013] Based on the optimized comprehensive weight values of evaluation indicators at each level, the comprehensive score of the park is calculated by weighted summation of the evaluation indicators at each level, and the park is evaluated based on the comprehensive score.

[0014] Furthermore, a hierarchical evaluation index set for the park is constructed, including an energy layer, an environmental layer, and an economic layer. The energy layer includes efficiency indicators, specifically including:

[0015] The energy layer includes indicators such as natural gas consumption, electricity consumption, electricity purchase volume, clean energy consumption rate, clean energy utilization rate, multi-energy efficiency, energy supply rate, and electrification rate of electricity load;

[0016] The environmental layer includes indicators such as CO2 emissions, NO x emissions, CCER purchase volume, clean energy carbon emission reduction volume, carbon neutralization rate, CCER sales volume, building carbon emission intensity, and transportation carbon emission intensity;

[0017] The economic layer includes indicators such as investment cost, operation cost, electricity purchase cost, operation revenue, revenue per unit energy storage capacity, CCER revenue, cost of abandoned electricity from renewable energy, and carbon tax cost.

[0018] Furthermore, based on the analytic hierarchy process, the AHP weights of each layer of evaluation indicators are calculated according to the expert judgment matrix, specifically including:

[0019] a. Construct an expert judgment matrix:

[0020]

[0021] Among them, the element a ij represents the degree of importance of factor i relative to factor j. If factor i is judged to be k times more important than factor j, then a ij = k. Correspondingly, the importance of factor j relative to factor i is 1 / k, that is, a ji = 1 / k; the diagonal element a ii of the judgment matrix is always 1, indicating that the factor is equally important to itself;

[0022] Construct judgment matrices for the evaluation index factors included in each of the three levels respectively, and obtain three judgment matrices;

[0023] b. Consistency test: It is achieved by calculating the consistency index CI and the consistency ratio CR. If the CR value exceeds the set threshold, the judgment matrix needs to be re-evaluated and adjusted;

[0024] The consistency index CI measures the degree to which the judgment matrix deviates from perfect consistency. The calculation formula is:

[0025]

[0026] Among them, λ maXis the maximum eigenvalue of the judgment matrix, and n is the order of the judgment matrix, that is, the number of factors to be compared. In the case of complete consistency, λ max = n, and at this time, CI = 0. The larger the value of CI, the higher the degree of non - consistency of the judgment matrix;

[0027] CR is obtained by comparing CI with the random consistency index RI. RI is the average value obtained by randomly generating judgment matrices of the same order multiple times and calculating the corresponding CI, and it is related to the order n of the judgment matrix. The calculation formula of CR is:

[0028]

[0029] When CR is lower than the preset threshold, it is considered that the consistency degree of the judgment matrix meets the requirements, indicating that the weight determination method is reasonable; when CR exceeds the preset threshold, it is considered that the consistency of the judgment matrix does not meet the requirements, and the judgment matrix needs to be modified and adjusted, and pairwise comparison needs to be carried out again until CR is lower than the preset threshold;

[0030] c. Determine the weights of each layer of indicators:

[0031] Column normalization: Divide each element of each column of the judgment matrix A by the sum of the elements of the corresponding column to obtain the column - normalized judgment matrix. Let the judgment matrix be A=(a ij ) n×n , then the elements of the normalized matrix

[0032]

[0033] Row summation: For the column - normalized judgment matrix, add the elements row - by - row to obtain an n - dimensional vector. Let the corresponding vector be v=(v1, v2,..., v n ) T , where

[0034] Normalization: Divide each element in the obtained n - dimensional vector by the sum of all elements in the vector. The obtained vector is the approximate eigenvector w, which is also used as the weight of the corresponding layer of indicators. The AHP weight vector w after normalization ahp =(w ahp,1 , w ahp,2 ,..., w ahp,n ) T , where At this time,

[0035] 4. A comprehensive evaluation method for a park considering the competition of efficiency scales, characterized in that, based on the entropy weight method, the entropy weights of each layer of evaluation indicators are calculated according to information entropy, specifically including:

[0036] a. Construct the original data matrix: Suppose there are n evaluation parks and m evaluation indicators, and the original data matrix X = (x ij ) n×m is constructed, where x ij represents the value of the i-th evaluation park on the j-th indicator;

[0037] b. Data standardization processing: Since different indicators have different dimensions and orders of magnitude, in order to eliminate the influence of these differences on the weight calculation, it is necessary to perform standardization processing on the original data;

[0038] c. Calculate the proportion of each evaluation park under each indicator:

[0039] Calculate the proportion of each evaluation park under each indicator after standardization, that is where z ij is the data after standardization;

[0040] d. Calculate the entropy value of each indicator:

[0041] According to the definition of information entropy, calculate the entropy value of the j-th indicator

[0042] where k = 1 / ln(n) is a constant used to normalize the entropy value to the target interval; if a certain p ij is 0, then define p ij ln(p ij ) = 0;

[0043] e. Calculate the entropy weight of each indicator:

[0044] The entropy weight is obtained by calculating the entropy redundancy d j = 1 - e j , and then normalizing, that is

[0045] Furthermore, based on the AHP weight and entropy weight of each layer of evaluation indicators, the comprehensive weight of each layer of evaluation indicators is calculated, specifically including:

[0046] w i = (w ahp,i + w entropy,i )

[0047] where w i is the comprehensive weight of evaluation indicator i, w ahp,i is the AHP weight of evaluation indicator i, and w entropy,i is the entropy weight of evaluation indicator i.

[0048] Furthermore, according to the specified The efficiency benchmark value is optimized and adjusted based on the yardstick competition mechanism The comprehensive weight value of the efficiency index specifically includes:

[0049] According to what is stipulated in the standard documents of various parks The efficiency benchmark value is optimized and adjusted through yardstick competition The weight distribution of the efficiency index is obtained to get the new Comprehensive weight of the efficiency index. The calculation process is:

[0050] w ex = w ahp,ex + w entropy,ex

[0051]

[0052] Among them, w ahp,ex is The AHP weight of the efficiency index, w entropy,ex is The entropy weight of the efficiency index, w ex and w ex_new are respectively the original Comprehensive weight value of the efficiency index and the optimized Comprehensive weight value of the efficiency index through yardstick competition. β ex is The influence coefficient of the industry benchmark value of the efficiency index, indicating the deviation degree of the current value x ex from the benchmark value x ex,s , and is related to the weight adjustment amplitude coefficient k and the deviation coefficient range [b min , b max .

[0053] Furthermore, based on the optimized comprehensive weight values of each layer of evaluation indicators, the weighted sum of each layer of evaluation indicators is calculated to obtain the comprehensive score of the park, and the park is evaluated according to the comprehensive score, specifically including:

[0054]

[0055] Among them, I is the total number of all evaluation indicators, c i is the value of the evaluation indicator i normalized according to the maximum-minimum value interval, w new,i is the comprehensive weight value of each layer of evaluation indicator i after optimization, where The comprehensive weight value of the efficiency index is the optimized w ex_new , and the comprehensive weight values of the remaining evaluation indicators remain unchanged before and after optimization.

[0056] According to the second aspect, an embodiment provides a consideration A park comprehensive evaluation system with efficiency benchmark competition, the system includes:

[0057] An index set construction module for constructing a hierarchical evaluation index set for the park, including an energy layer, an environmental layer, and an economic layer, where the energy layer contains Efficiency indicators;

[0058] An AHP weight determination module for calculating the AHP weights of each layer of evaluation indicators based on the analytic hierarchy process according to the expert judgment matrix;

[0059] An entropy weight determination module for calculating the entropy weights of each layer of evaluation indicators based on the entropy weight method according to the information entropy;

[0060] A comprehensive weight determination module for calculating the comprehensive weights of each layer of evaluation indicators based on the AHP weights and entropy weights of each layer of evaluation indicators;

[0061] A benchmark competition optimization module for optimizing and adjusting the Comprehensive weight value of efficiency indicators based on the specified Efficiency benchmark value based on the benchmark competition mechanism;

[0062] A comprehensive evaluation module for calculating the comprehensive score of the park by weighted summation of each layer of evaluation indicators based on the optimized comprehensive weight values of each layer of evaluation indicators, and evaluating the park according to the comprehensive score.

[0063] According to the third aspect, an electronic device is provided in an embodiment, the device includes: a processor and a memory;

[0064] The memory is used to store one or more program instructions;

[0065] The processor is used to run one or more program instructions to execute the steps of a park comprehensive evaluation method considering Efficiency benchmark competition as described in any one of the above.

[0066] According to the fourth aspect, a computer-readable storage medium is provided in an embodiment. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of a park comprehensive evaluation method considering Efficiency benchmark competition as described in any one of the above are implemented.

[0067] The present invention provides a park comprehensive evaluation method and system considering Efficiency benchmark competition, aiming to establish a set of Taking efficiency as the core and combining with a comprehensive evaluation index system of traditional energy, environment, and economic indicators, it is possible to more comprehensively and deeply evaluate the true energy utilization efficiency and the overall sustainable development level of the park. At the same time, the present invention is committed to introducing a dynamic yardstick competition mechanism. By publicly and transparently comparing and ranking key performance indicators such as efficiency among various parks, it promotes mutual learning and healthy competition among parks, and drives the improvement of the overall energy utilization efficiency of the region. Finally, the present invention aims to provide more refined and operable evaluation feedback. By conducting efficiency analysis to accurately locate the weak links in energy utilization, it provides more targeted improvement strategies and technical path options for park managers and policy makers, and strongly supports the green and low-carbon transformation and high-quality development of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 The flowchart of a comprehensive park evaluation method considering yardstick competition of efficiency provided by an embodiment of the present invention;

[0069] Figure 2 The three-level progressive weight distribution framework diagram of a comprehensive park evaluation method considering yardstick competition of efficiency provided by an embodiment of the present invention;

[0070] Figure 3 The logical structure schematic diagram of a comprehensive park evaluation system considering yardstick competition of efficiency provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The present invention will be further described in detail below in conjunction with the accompanying drawings through specific embodiments. Similar elements in different embodiments are labeled with related similar element numbers. In the following embodiments, many details are described to make the present invention better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, or methods. In some cases, some operations related to the present invention are not shown or described in the specification, in order to avoid the core part of the present invention being overwhelmed by excessive description. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0072] In addition, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. Meanwhile, the steps or actions in the method description can also be reordered or adjusted in a manner obvious to those skilled in the art. Therefore, the various sequences in the specification and drawings are only for clearly describing a certain embodiment and do not mean a necessary sequence, unless it is stated that a certain sequence must be followed.

[0073] As Figure 1 shown, a comprehensive park evaluation method considering efficiency scale competition provided by the first embodiment of the present invention. Specifically, this embodiment proposes a comprehensive park evaluation method that integrates the Analytic Hierarchy Process (AHP), the entropy weight method, and the efficiency scale competition model, constructs a three-level progressive weight distribution framework of "subjective weight assignment - objective correction - competition optimization", determines the expert experience weight through AHP, calculates the data dispersion weight by the entropy weight method, and then introduces the scale competition model to make a horizontal comparison between the current efficiency indicators and the benchmark value, dynamically adjusts the weight distribution, and solves the problems of large subjective deviation, insufficient utilization of objective data, and insufficient influence of benchmark deviation in traditional methods. The following will be described in detail with reference to Figure 1 and Figure 2 .

[0074] As Figure 1 shown, in step S100, a hierarchical park evaluation index set is constructed, including an energy layer, an environmental layer, and an economic layer. The energy layer includes efficiency indicators.

[0075] The above steps specifically include:

[0076] The energy layer includes indicators such as natural gas consumption, electricity consumption, electricity purchase volume, clean energy consumption rate, clean energy utilization rate, multi-energy efficiency, energy supply rate, and electrification rate of electricity load;

[0077] The environmental layer includes indicators such as CO2 emissions, NO x emissions, CCER purchase volume, clean energy carbon emission reduction volume, carbon neutralization rate, CCER sales volume, building carbon emission intensity, and transportation carbon emission intensity;

[0078] The economic layer includes indicators such as investment cost, operation cost, electricity purchase cost, operation revenue, revenue per unit energy storage capacity, CCER revenue, renewable energy curtailment cost, and carbon tax cost.

[0079] The park evaluation index set and park evaluation sample constructed in this embodiment are as shown in Table 1 below:

[0080] Table 1 Park Evaluation Index Set and Park Evaluation Sample Example

[0081]

[0082]

[0083] As Figure 1 shown, in step S200, based on the analytic hierarchy process, the AHP weights of each layer of evaluation indicators are calculated according to the expert judgment matrix.

[0084] The above steps specifically include:

[0085] a. Construct an expert judgment matrix: Among the various factors at the same level, for a certain criterion at the upper level, pairwise comparisons are made, and a certain scale (usually the 1-9 scale method) is used to represent the importance of one factor relative to another factor. The results of the comparisons are filled into a judgment matrix, and the elements in the matrix reflect the relative importance of each factor:

[0086]

[0087] Among them, the element a ij represents the degree of importance of factor i relative to factor j. If factor i is judged to be k times more important than factor j, then a ij = k. Correspondingly, the importance of factor j compared to factor i is 1 / k, that is, a ji = 1 / k; the diagonal element a ii of the judgment matrix is always 1, indicating that the factor itself is as important as itself.

[0088] In this embodiment, there are 3 criterion layers set, with 8 indicators corresponding to each criterion layer. Judgment matrices are constructed for the evaluation indicator factors included in each of the three levels respectively, and three judgment matrices are obtained: A11, A12, A13, corresponding to the indicator layer judgment matrices under each criterion layer.

[0089]

[0090] b. Consistency test: Test the consistency of the judgment matrix to ensure the logic of the decision-maker's pairwise comparisons. This is achieved by calculating the consistency index CI and the consistency ratio CR. If the CR value exceeds the set threshold (usually 0.1), then the judgment matrix needs to be re-evaluated and adjusted;

[0091] The consistency index CI measures the degree to which the judgment matrix deviates from perfect consistency, and the calculation formula is:

[0092]

[0093] Among them, λmax is the largest eigenvalue of the judgment matrix, and n is the order of the judgment matrix, that is, the number of factors to be compared. In the case of complete consistency, λ max = n. At this time, CI = 0. The larger the value of CI, the higher the degree of non - consistency of the judgment matrix;

[0094] CR is obtained by comparing CI with the Random Consistency Index (RI). RI is the average value obtained by randomly generating judgment matrices of the same order multiple times and calculating the corresponding CI, and it is related to the order n of the judgment matrix. The calculation formula of CR is:

[0095]

[0096] Whether the consistency of the judgment matrix can be accepted is usually based on the value of CR: when CR < 0.10 (or 10%), it is considered that the consistency degree of the judgment matrix is acceptable, indicating that the weight determination method is reasonable and the constructed judgment matrix has good logic. When CR ≥ 0.10, it is considered that the consistency of the judgment matrix does not meet the requirements, indicating that there may be serious logical errors in the judgment matrix, and the judgment matrix needs to be modified and adjusted, and pairwise comparison needs to be carried out again until CR < 0.10;

[0097] c. Determine the weights of each layer of indicators: For each judgment matrix, calculate its largest eigenvalue and the corresponding eigenvector. After the eigenvector is normalized, it is the weight of the factors at this level relative to the criterion at the upper level. The eigenvector represents the relative priority of the compared elements in their respective levels and reflects their contributions to the higher - level goals.

[0098] Column normalization: Divide each element of each column of the judgment matrix A by the sum of the elements in the corresponding column to obtain the column - normalized judgment matrix. Let the judgment matrix be A = (a ij ) n×n , then the elements of the normalized matrix

[0099]

[0100] Row summation: For the column - normalized judgment matrix, sum the elements row - by - row to obtain an n - dimensional vector. Let the corresponding vector be v = (v1, v2,..., v n ) T , where

[0101] Normalization: Divide each element in the obtained n - dimensional vector by the sum of all elements in the vector. The obtained vector is the approximate eigenvector w and is also used as the weight of the corresponding layer of indicators. The normalized AHP weight vector w ahp =(wahp,1 , w ahp,2 ,..., w ahp,n ) T , where At this time,

[0102] As Figure 1 shown, in step S300, based on the entropy weight method, the entropy weights of each layer of evaluation indicators are calculated according to the information entropy.

[0103] The above steps specifically include:

[0104] The Entropy Weight Method (EWM) is an objective weighting method that determines the weights of indicators based on the information entropy theory. Information entropy is an important concept in information theory, used to measure the uncertainty or randomness of information. In the entropy weight method, the smaller the entropy value of an indicator, the greater the degree of data variation of the indicator, the more information it provides, and therefore the greater its weight should be in the comprehensive evaluation. On the contrary, if all the values of an indicator are the same, then the entropy value of the indicator is the largest, the amount of information provided is zero, and its weight should also be zero. The core idea of the entropy weight method is to objectively determine the weights of each evaluation indicator through the information contained in the data itself, thereby reducing the interference of subjective factors.

[0105] a. Construct the original data matrix: Suppose there are n evaluation parks and m evaluation indicators, and the original data matrix X = (x ij ) n×m , where x ij represents the value of the i-th evaluation park on the j-th indicator;

[0106] b. Data standardization processing: Since different indicators have different dimensions and orders of magnitude, in order to eliminate the influence of these differences on the weight calculation, it is necessary to perform standardization processing on the original data;

[0107] c. Calculate the proportion of each evaluation park under each indicator:

[0108] Calculate the proportion of each evaluation park under each indicator after standardization, that is where z ij is the standardized data;

[0109] d. Calculate the entropy value of each indicator:

[0110] According to the definition of information entropy, calculate the entropy value of the j-th indicator

[0111] where k = 1 / ln(n) is a constant used to normalize the entropy value to the target interval, such as [0.002, 0.996]; if a certain p ijIf it is 0, then define p ij ln(p ij ) = 0;

[0112] e. Calculate the entropy weight of each index:

[0113] The entropy weight is obtained by calculating the entropy redundancy d j = 1 - e j , and then normalizing it, that is

[0114] As Figure 1 shown, in step S400, based on the AHP weight and entropy weight of each layer of evaluation indexes, the comprehensive weight of each layer of evaluation indexes is calculated.

[0115] The above steps specifically include:

[0116] w i = (w ahp,i + w entropy,i )

[0117] Among them, w i is the comprehensive weight of evaluation index i, w ahp,i is the AHP weight of evaluation index i, and w entropy,i is the entropy weight of evaluation index i.

[0118] As Figure 1 shown, in step S500, according to the specified efficiency benchmark value, based on the yardstick competition mechanism, optimize and adjust the comprehensive weight value of the efficiency index.

[0119] The above steps specifically include:

[0120] According to the efficiency benchmark value specified in the standard documents of various parks, optimize and adjust the weight allocation of the efficiency index through yardstick competition to obtain the new comprehensive weight of the efficiency index. The calculation process is:

[0121] w ex = w ahp,ex + w entropy,ex

[0122] w ex_new = w ex ·β ex = (w ahp,ex + w entropy,ex )β ex

[0123]

[0124] Among them, w ahp,ex is the AHP weight of the efficiency index, w entropy,ex is the entropy weight of the efficiency index, w ex and w ex_new are respectively the comprehensive weight value of the efficiency index and the comprehensive weight value of the efficiency index after optimization through ruler competition , β ex is the influence coefficient of the industry benchmark value of the efficiency index, indicating the deviation degree of the current value x ex from the benchmark reference value x ex,s , related to the weight adjustment amplitude coefficient k and the deviation coefficient range [b min , b max . Here, k = 0.2 is taken, and the deviation coefficient range is taken as [0.8, 1.2].

[0125] As Figure 1 shown, in step S600, based on the optimized comprehensive weight values of each layer of evaluation indicators, the comprehensive scores of each layer of evaluation indicators are weighted and summed to evaluate the park according to the comprehensive scores.

[0126] The above steps specifically include:

[0127] If the ruler competition mechanism is not introduced to optimize and adjust the efficiency index weight, then the original total score of the park is:

[0128]

[0129] w origin,i =(w ahp,i +w entropy,i )

[0130] Among them, w ahp,i is the AHP weight of evaluation indicator i, w entropy,i is the entropy weight of evaluation indicator i;

[0131] After introducing the ruler competition mechanism to optimize and adjust the efficiency index weight, then the new total score of the park is:

[0132]

[0133] Among them, I is the total number of all evaluation indicators, c i is the value of evaluation indicator i normalized by the maximum-minimum value interval, w new,i is the optimized comprehensive weight value of each layer of evaluation indicator i, among which the comprehensive weight value of the efficiency index is the optimized wex_new The comprehensive weight values of the remaining evaluation indicators remain unchanged before and after optimization.

[0134] According to the sample example values of the industrial parks in Table 1, the index values representing 3 industrial parks are used for evaluation and comparative analysis respectively. It is mainly used to evaluate the differential effect of the final evaluation value after considering the efficiency benchmark competition. The efficiency benchmark value is set to 80%. The evaluation results of the industrial parks are shown in Table 2:

[0135] Table 2 Evaluation Results of Industrial Parks

[0136]

[0137] It can be found from Table 2 that: based on the re - adjustment of the efficiency benchmark competition, the increase rate of Park B is the largest, followed by Park A, and there is no growth in Park C. From the score gap before and after, the benchmark competition further strengthens the discrimination of the evaluation value, making the excellent values better and the inferior values worse, further widening the development gap between industrial parks, and can more intuitively identify the weak - developing industrial parks, thus promoting the healthy competition between industrial parks and driving the improvement of the overall regional efficiency.

[0138] Corresponding to the above - disclosed comprehensive evaluation method of industrial parks considering the efficiency benchmark competition, the embodiment of the present invention also discloses a comprehensive evaluation system of industrial parks considering the efficiency benchmark competition. As Figure 3 shown, it specifically includes:

[0139] An index set construction module, used to construct a hierarchical evaluation index set for industrial parks, including an energy layer, an environmental layer, and an economic layer. The energy layer contains efficiency indicators;

[0140] An AHP weight determination module, used to calculate the AHP weights of each layer of evaluation indicators based on the analytic hierarchy process according to the expert judgment matrix;

[0141] An entropy weight determination module, used to calculate the entropy weights of each layer of evaluation indicators based on the entropy weight method according to the information entropy;

[0142] A comprehensive weight determination module, used to calculate the comprehensive weights of each layer of evaluation indicators based on the AHP weights and entropy weights of each layer of evaluation indicators;

[0143] A benchmark competition optimization module, used to optimize and adjust the comprehensive weight value of the efficiency indicators based on the specified efficiency benchmark value according to the benchmark competition mechanism;

[0144] A comprehensive evaluation module is used to calculate the comprehensive score of the park by weighted summing of the evaluation indicators at each layer based on the optimized comprehensive weight values of the evaluation indicators at each layer, and evaluate the park according to the comprehensive score.

[0145] It should be noted that for the detailed description of a park comprehensive evaluation system considering the efficiency benchmark competition provided by the embodiments of the present invention, reference can be made to the relevant description of a park comprehensive evaluation method considering the efficiency benchmark competition provided by the embodiments of the present invention, which will not be elaborated here.

[0146] In addition, the embodiments of the present invention also provide an electronic device, which includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute a park comprehensive evaluation method considering the efficiency benchmark competition as described in any one of the above.

[0147] It should be noted that for the detailed description of an electronic device provided by the embodiments of the present invention, reference can be made to the relevant description of a park comprehensive evaluation method considering the efficiency benchmark competition provided by the embodiments of the present application, which will not be elaborated here.

[0148] In addition, the embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it realizes the steps of a park comprehensive evaluation method considering the efficiency benchmark competition as described in any one of the above.

[0149] It should be noted that for the detailed description of a computer-readable storage medium provided by the embodiments of the present invention, reference can be made to the relevant description of a park comprehensive evaluation method considering the efficiency benchmark competition provided by the embodiments of the present application, which will not be elaborated here.

[0150] Those skilled in the art can understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the programs can be stored in a computer-readable storage medium, which can include: read-only memory, random access memory, magnetic disks, optical disks, hard disks, etc. The above functions can be realized by a computer executing these programs. For example, storing the programs in the memory of a device, when the programs stored in the memory are executed by a processor, all or part of the above functions can be realized. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the programs can also be stored in storage media such as servers, other computers, magnetic disks, optical disks, flash drives or external hard drives, and are saved to the memory of a local device by downloading or copying, or the system of the local device is updated. When the programs stored in the memory are executed by a processor, all or part of the functions in the above embodiments can be realized.

[0151] The above uses specific examples to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art of the present invention, according to the idea of the present invention, several simple deductions, deformations or substitutions can also be made.

Claims

1. A comprehensive evaluation method for industrial parks considering the competition of efficiency benchmarks, characterized in that The method includes: Construct a hierarchical evaluation index set for the park, including an energy layer, an environmental layer, and an economic layer. The energy layer includes efficiency indicators; Based on the analytic hierarchy process (AHP), calculate the AHP weights of the evaluation indicators at each level according to the expert judgment matrix; Based on the entropy weight method, calculate the entropy weights of the evaluation indicators at each level according to the information entropy; Based on the AHP weights and entropy weights of the evaluation indicators at each level, calculate the comprehensive weights of the evaluation indicators at each level; According to the specified efficiency benchmark value, optimize and adjust based on the ruler competition mechanism comprehensive weight value of the efficiency index; Based on the optimized comprehensive weight values of the evaluation indicators at each level, perform weighted summation on the evaluation indicators at each level to calculate the comprehensive score of the park, and evaluate the park according to the comprehensive score.

2. The one described in claim 1, considering A comprehensive evaluation method for a park considering the competition of efficiency benchmarks, characterized in that Construct a hierarchical evaluation index set for the park, including an energy layer, an environmental layer, and an economic layer. The energy layer includes efficiency indicators, specifically including: The energy layer includes indicators such as natural gas consumption, electricity consumption, electricity purchase volume, clean energy consumption rate, clean energy utilization rate, multi-energy efficiency, energy supply rate, and electrification rate of electricity load; The environmental layer includes CO2 emissions, NO x emissions, CCER purchase volume, clean energy carbon emission reduction volume, carbon neutrality rate, CCER sales volume, building carbon emission intensity, and transportation carbon emission intensity indicators; The economic layer includes investment cost, operation cost, power purchase cost, operation revenue, revenue per unit energy storage capacity, CCER revenue, cost of abandoned renewable energy, and carbon tax cost indicators.

3. A consideration as described in claim 1 A comprehensive evaluation method for a park considering the competition of efficiency benchmarks, characterized in that Based on the analytic hierarchy process (AHP), calculate the AHP weights of the evaluation indicators at each level according to the expert judgment matrix, specifically including: a. Construct an expert judgment matrix: Among them, element a ih represents the importance degree of factor i relative to factor j. If factor i is judged to be k times more important than factor j, then a ij = k. Correspondingly, the importance of factor j relative to factor i is 1 / k, that is, a hi = 1 / k; the diagonal element a hi of the judgment matrix is always 1, indicating that the importance of a factor itself is the same as itself; Construct judgment matrices for the evaluation indicator factors included in each of the three levels respectively to obtain three judgment matrices; b. Consistency test: Achieved by calculating the consistency index CI and the consistency ratio CR. If the CR value exceeds the set threshold, it is necessary to re-evaluate and adjust the judgment matrix; The consistency index CI measures the degree to which the judgment matrix deviates from perfect consistency. The calculation formula is: Among them, λ max is the maximum eigenvalue of the judgment matrix, and n is the order of the judgment matrix, that is, the number of factors to be compared. In the case of complete consistency, λ max = n. At this time, CI = 0. The larger the value of CI, the higher the degree of non-consistency of the judgment matrix; CR is obtained by comparing CI with the random consistency index RI. RI is the average value obtained by randomly generating judgment matrices of the same order multiple times and calculating the corresponding CI, and is related to the order n of the judgment matrix. The calculation formula of CR is: When CR is lower than the preset threshold, it is considered that the consistency degree of the judgment matrix meets the requirements, indicating that the weight determination method is reasonable; when CR exceeds the preset threshold, it is considered that the consistency of the judgment matrix does not meet the requirements, and the judgment matrix needs to be modified and adjusted, and pairwise comparison needs to be carried out again until CR is lower than the preset threshold; c. Determine the weights of the indicators at each level: Column normalization: Divide the elements of each column of the judgment matrix A by the sum of the elements in the corresponding column to obtain the column-normalized judgment matrix. Let the judgment matrix be A = (a ij ) b×b , then the elements of the normalized matrix Row summation: For the judgment matrix after column normalization, add the elements row by row to obtain an n-dimensional vector. Let the corresponding vector be v = (v1, v2,..., v n ) T , where Normalization: Divide each element in the obtained n-dimensional vector by the sum of all elements in the vector. The resulting vector is the approximate eigenvector w, which also serves as the weight of the corresponding layer index. The normalized AHP weight vector w ahp =(w ahp,1 , w ahp,2 ,..., w ahp,n ) T , where At this time, 4. A consideration as described in claim 1 The comprehensive evaluation method for a park considering the competition of efficiency benchmarks, characterized in that Based on the entropy weight method, calculate the entropy weights of the evaluation indicators at each level according to the information entropy, specifically including: a. Construct the original data matrix: Suppose there are n evaluation parks and m evaluation indicators, and the original data matrix X=(x ij ) n×m , where x ij represents the value of the i-th evaluation park on the j-th indicator; b. Data standardization processing: Since different indicators have different dimensions and orders of magnitude, in order to eliminate the influence of these differences on weight calculation, it is necessary to perform standardization processing on the original data; c. Calculate the proportion of each evaluated park under each indicator; Calculate the proportion of each evaluated park under each indicator after standardization, that is where z ij is the data after standardization; d. Calculate the entropy value of each indicator; According to the definition of information entropy, calculate the entropy value of the j-th index where k = 1 / ln(n) is a constant used to normalize the entropy value to the target interval; if a certain p ij is 0, then define p ij ln(p ij ) = 0; e. Calculate the entropy weight of each indicator. The entropy weight is obtained by calculating the entropy redundancy d j = 1 - e j , and then normalizing it, that is 5. A consideration as described in claim 1 The comprehensive evaluation method for a park considering the competition of efficiency benchmarks, characterized in that Based on the AHP weights and entropy weights of the evaluation indicators at each level, calculate the comprehensive weights of the evaluation indicators at each level, specifically including: w i = (w ahp,i + w entropy,i ) Among them, w i is the comprehensive weight of evaluation index i, w ahp,i is the AHP weight of evaluation index i, w entropy,i is the entropy weight of evaluation index i.

6. A comprehensive evaluation method for a park considering the competition of efficiency scales, characterized in that According to the specified efficiency benchmark value, optimize and adjust based on the ruler competition mechanism comprehensive weight value of the efficiency index, specifically including: According to the efficiency benchmark values specified in various park standard documents, through yardstick competition for optimization and adjustment the weight distribution of efficiency indicators is obtained, and a new comprehensive weight of efficiency indicators is obtained. The calculation process is as follows: ​ w ex = w ahp,ex + w entropy,ex w ex_new = w ex ·β ex =(w ahp,ex + w entropy,ex )β ex Among them, w ahp,ex is the AHP weight of the efficiency index, w entropy,ex is the entropy weight of the efficiency index, w ex and w ex_new are respectively the comprehensive weight value of the original efficiency index and the comprehensive weight value of the efficiency index after optimization through ruler competition, β is ex the influence coefficient of the industry benchmark value of the efficiency index, indicating the deviation degree of the current value x from the benchmark reference value x ex , and is related to the weight adjustment amplitude coefficient k and the deviation coefficient range [b ex,s min , b max .​ 7. A consideration as described in claim 6 The comprehensive evaluation method for a park considering the competition of efficiency benchmarks, characterized in that Based on the optimized comprehensive weight values of the evaluation indicators at each level, perform weighted summation on the evaluation indicators at each level to calculate the comprehensive score of the park, and evaluate the park according to the comprehensive score, specifically including: where I is the total number of all evaluation indicators, and c i is the value of evaluation indicator i after normalization by the maximum-minimum value interval, and w new,i is the comprehensive weight value of each layer's evaluation indicator i after optimization, where the comprehensive weight value of the efficiency indicator is w after optimization ex_new , and the comprehensive weight values of the remaining evaluation indicators remain unchanged before and after optimization.

8. A park comprehensive evaluation system considering the competition of efficiency benchmarks, characterized in that The system includes: An index set construction module, which is used to construct a hierarchical evaluation index set for the park, including an energy layer, an environmental layer and an economic layer, and the energy layer includes efficiency indicators; An AHP weight determination module, used to calculate the AHP weights of the evaluation indicators at each level based on the analytic hierarchy process (AHP) according to the expert judgment matrix; An entropy weight determination module, used to calculate the entropy weights of the evaluation indicators at each level based on the entropy weight method according to the information entropy; A comprehensive weight determination module, used to calculate the comprehensive weights of the evaluation indicators at each level based on the AHP weights and entropy weights of the evaluation indicators at each level; A scale competition optimization module for optimizing and adjusting, based on a scale competition mechanism, the comprehensive weight value of an efficiency indicator according to a specified efficiency benchmark value; ​ The comprehensive evaluation module is used to calculate the comprehensive score of the park by weighted summation of the evaluation indicators at each layer based on the optimized comprehensive weight values of the evaluation indicators at each layer, and evaluate the park according to the comprehensive score.

9. An electronic device, characterized in that, The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions for performing the steps of a comprehensive park evaluation method considering the competition of an efficiency scale as described in any one of claims 1 to 7. ​ 10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps of a comprehensive park evaluation method considering the competition of an efficiency scale as described in any one of claims 1 to 7. ​

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