Industrial park integrated energy system coupling coordination degree evaluation method and equipment

Abstract

The invention relates to an industrial park integrated energy system coupling coordination degree evaluation method comprising the following steps: constructing an evaluation index system and carrying out data standardization processing to obtain a standardized decision matrix; calculating a comprehensive weight vector of each index; calculating a system coupling coordination degree; and forming a historical evaluation result data set, and performing qualitative evaluation and grading on the historical evaluation result data set according to a preset grading standard. A three-layer coupling evaluation framework based on physical architecture-information communication-market transaction is proposed for the first time, complex characteristics of the industrial park comprehensive energy system can be comprehensively and systematically reflected, the one-sidedness of traditional single-dimensional evaluation is fundamentally overcome, and the evaluation result can better reflect the overall performance of the system; the scientificity of the final weight and the robustness to different data distributions are ensured, and the credibility of the evaluation result is remarkably improved; the evaluation process is clear and can be realized through a conventional tool, and the application value is remarkable.

Description

Technical Field

[0001] This invention relates to the field of coupled system evaluation and energy system analysis technology, and in particular to a method and equipment for evaluating the coupling coordination degree of an integrated energy system in an industrial park. Background Technology

[0002] Industrial parks, as concentrated areas of energy consumption and carbon emissions, are transforming their energy systems into highly intelligent integrated energy systems (IES) that integrate multiple energy forms. IES, by integrating renewable energy, energy storage, and combined cooling, heating, and power technologies, form a complex system with a high degree of integration of "source-grid-load-storage." A scientific and comprehensive evaluation of its planning and design scheme is a key prerequisite for ensuring its technical and economic viability and environmental friendliness.

[0003] Existing IES evaluation methods have the following key shortcomings:

[0004] First, the evaluation dimensions are singular, lacking consideration of system coupling: Traditional evaluation methods often focus on a single dimension, such as only evaluating economic or technical aspects, while ignoring the IES as a whole, where there are profound coupling and interaction relationships between its internal physical energy network, information and communication network, and market transaction mechanisms. This fragmented evaluation approach cannot reveal the synergistic effects or constraints between the various subsystems of the system, leading to one-sided evaluation results and potentially guiding planning decisions that are "locally optimal but overall suboptimal."

[0005] Second, the methods for assigning indicator weights have inherent flaws: Indicator weight allocation is a core element of multi-criteria decision-making evaluation, and its rationality directly affects the accuracy of the evaluation results. Subjective weighting methods, such as the analytic hierarchy process (AHP), heavily rely on expert experience. When there are numerous evaluation indicators, this not only increases the cognitive burden on experts but also easily leads to distorted weighting results due to subjective judgment biases and logical inconsistencies. Objective weighting methods, such as the traditional entropy weighting method, rely entirely on the distribution characteristics of the original data to determine the weights. While avoiding subjective arbitrariness, they completely ignore the valuable experience of experts in specific fields, potentially leading to calculated weights that contradict the actual engineering situation. Simple combined weighting methods, such as arithmetic averaging of subjective and objective weights, lack a solid theoretical foundation to scientifically balance the conflict and complementarity of the two types of information. The combination method is relatively arbitrary and cannot guarantee the scientific validity and robustness of the final weights.

[0006] Therefore, there is an urgent need for a comprehensive evaluation method for the integrated energy system of industrial parks that can fully consider the multi-layered coupling characteristics of physical architecture, information communication, and market transactions, and adopt a scientific and rigorous combined weighting mechanism, in order to address the shortcomings of existing technologies. Summary of the Invention

[0007] To address the problems of existing technologies, such as limited evaluation dimensions, one-sided evaluation results, and lack of robustness, the primary objective of this invention is to provide an evaluation method for the coupling coordination degree of integrated energy systems in industrial parks that can comprehensively and systematically reflect the complex characteristics of integrated energy systems in industrial parks. This method ensures that the evaluation results better reflect the overall performance of the system, guarantees the scientific nature of the final weights and robustness to different data distributions, and significantly improves the credibility of the evaluation results.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the coupling coordination degree of an integrated energy system in an industrial park, comprising the following sequential steps:

[0009] (1) Construct an evaluation index system and perform data standardization processing to obtain a standardized decision matrix. ;

[0010] (2) Based on standardized decision matrix We perform combinatorial weighting based on game theory to calculate the comprehensive weight vector of each indicator. ;

[0011] (3) Using standardized decision matrices With the comprehensive weight vector Calculate the coupling coordination degree of the system ;

[0012] (4) System coupling coordination degree of all evaluation samples This constitutes the historical evaluation results dataset. An adaptive grading standard based on data distribution characteristics is constructed to assess the system coupling coordination degree. Conduct qualitative assessments and grading.

[0013] Step (1) specifically includes the following steps:

[0014] (1a) Construct a multi-dimensional evaluation index system, which includes a target layer, three criterion layers and an indicator layer. The target layer is the level of coupled and coordinated development of the integrated energy system of the industrial park. The criterion layer is divided into three dimensions: physical architecture subsystem, information and communication subsystem and market transaction subsystem. The indicator layer includes multiple performance indicators under each criterion layer. The physical architecture subsystem includes renewable energy penetration rate, network loss rate, voltage deviation rate and power supply reliability. The information and communication subsystem includes data integrity rate, data acquisition cycle deviation, communication delay and network coverage. The market transaction subsystem includes clean energy transaction ratio, integrated energy purchase unit cost and demand response participation.

[0015] (1b) Construct the original data matrix Actual data collected from the integrated energy system of the industrial park The evaluation sample consists of historical operating moments or typical design conditions, along with raw monitoring data for n performance indicators acquired for each sample. Subsequently, the raw data matrix... Perform standardization processing to generate a standardized decision matrix. For both positive and negative indicators, range standardization is performed using equation (1):

[0016] (1);

[0017] in, Let j be the original observed value of the performance index in the i-th evaluation sample. and The j-th indicator is in all Minimum and maximum values ​​in each evaluation sample These are the standardized performance index values;

[0018] Using the 5th percentile of the data distribution and the 95th percentile Replace each of the following in equation (1) and The specific formula after the replacement is as follows:

[0019] ;

[0020] After the calculation is complete, for values ​​outside the interval [0,1] The value is truncated or renormalized.

[0021] Step (2) specifically includes the following steps:

[0022] (2a) Determine the subjective weight vector using the best-worst method By constructing and solving the linear programming model shown in equation (2), the optimal consistency scaling factor is calculated. :

[0023] (2);

[0024] in, Let be the subjective weight of the j-th index to be solved; and The pre-set optimal index C B And worst-case indicator C W The weight; a Bj and a jW All are degrees of importance preference;

[0025] Then, the corresponding values ​​for each indicator can be obtained. Finally, the subjective weight vector is constructed from the weight values. ;

[0026] Calculate the consistency ratio :

[0027] (3);

[0028] in, As a consistency indicator, The numerical value and the parameter a of expert preference BW There exists a deterministic correspondence: when a BW When taking the numbers 1, 2, 3, 4, 5, 6, 7, 8, and 9 respectively, the corresponding... The values ​​are 0.00, 0.44, 1.00, 1.63, 2.30, 3.00, 3.73, 4.47, and 5.23 respectively; if the calculated... If the value is less than or equal to a preset threshold, the expert group's input data is deemed to have acceptable consistency, and the resulting subjective weight vector is... It possesses mathematical validity; if the calculated CR value is greater than the preset threshold, the expert preference parameter a is adjusted. BW And resolve equation (3); expert preference parameter a BW The optimal index C B Compared to the worst indicator C W The preference multiple;

[0029] (2b) Determine the objective weight vector using the entropy weight method First, based on the standardized decision matrix Calculate the proportion of the index value of the i-th evaluation sample among all evaluation samples under the j-th index. :

[0030] ;

[0031] In the formula, is the standardized performance index value; m is the number of evaluation samples;

[0032] Secondly, calculate the information entropy value of the j-th indicator. and :

[0033] ;

[0034] To ensure the validity of logarithmic calculations, when When = 0, define the limiting case. Finally, the information redundancy of each indicator is normalized to obtain the objective weight of the j-th indicator. This constitutes the objective weight vector. :

[0035] ;

[0036] In the formula, n is the number of performance indicators;

[0037] (2c) Apply game theory models to perform combinatorial weighting, and seek subjective weight vectors. With objective weight vector The Nash equilibrium between them yields the comprehensive weight vector. :

[0038] First, the optimal linear combination coefficients are determined by solving the system of linear equations. :

[0039] ;

[0040] in, Represents the dot product of vectors;

[0041] Subsequently, the combination coefficients are normalized and weighted summed to obtain the final comprehensive weight vector. :

[0042] .

[0043] Step (3) specifically includes the following steps:

[0044] (3a) Based on the comprehensive weight vector and standardized decision matrix Calculate the comprehensive development index of the physical architecture subsystem, information and communication subsystem, and market transaction subsystem respectively:

[0045] ;

[0046] in, These are the comprehensive development indices of the physical architecture subsystem, information and communication subsystem, and market transaction subsystem of the i-th sample, respectively. These are indexes of performance metrics contained in the physical architecture subsystem, information and communication subsystem, and market trading subsystem, respectively. These are the standardized performance index values;

[0047] (3b) Calculate the coupling degree characterizing the interaction strength between the three subsystems. :

[0048] ;

[0049] (3c) Calculate the comprehensive evaluation coefficient representing the overall development level of the system. :

[0050] ;

[0051] in, The importance coefficients of the physical architecture subsystem, information and communication subsystem, and market transaction subsystem in the overall evaluation are satisfied. and The undetermined coefficients of the nonnegative convex constraint conditions;

[0052] (3d) Calculate the system coupling coordination degree, which comprehensively reflects the system coupling coordination development level. :

[0053] .

[0054] Step (4) specifically refers to: calculating the system coupling coordination degree for all evaluation samples. This data is compiled to form a historical evaluation results dataset. ;

[0055] The historical evaluation result dataset is calculated according to the following formula. mean and standard deviation :

[0056] ;

[0057] ;

[0058] Based on the calculated mean and standard deviation An adaptive grading standard is constructed to assess the coupling and coordination degree of the system to be evaluated. Comparison with adaptive grading standards:

[0059] like If so, it is judged to be at the level of severe disorder;

[0060] like If so, it is judged as a mild disorder level;

[0061] like If so, it is determined to be at the basic coordination level;

[0062] like If so, it is judged to be at a good coordination level;

[0063] like If so, it is judged as a high-quality coordination level.

[0064] Another object of the present invention is to provide an electronic device comprising:

[0065] Processor; and

[0066] A memory stores computer program instructions that, when executed by the processor, cause the processor to perform the evaluation method for the coupling coordination degree of the integrated energy system in the industrial park as described above.

[0067] The present invention also provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the evaluation method for the coupling coordination degree of an integrated energy system in an industrial park as described above.

[0068] As can be seen from the above technical solution, the beneficial effects of this invention are as follows: First, this invention proposes for the first time a three-layer coupled evaluation framework based on "physical architecture - information communication - market transaction," which can comprehensively and systematically reflect the complex characteristics of the integrated energy system of industrial parks, fundamentally overcoming the one-sidedness of traditional single-dimensional evaluation, and making the evaluation results more reflective of the overall performance of the system; Second, this invention innovatively combines Bloomberg Wave Model (BWM), entropy weight method, and game theory to form a theoretically rigorous combined weighting method. Compared with Alternative Power Hierarchy Process (AHP), BWM achieves higher consistency with fewer comparisons, reducing the cognitive burden on experts; Game theory provides a non-arbitrary, Nash equilibrium-seeking optimal solution for integrating subjective and objective weights, ensuring the scientific nature of the final weights and robustness to different data distributions, significantly improving the credibility of the evaluation results; Third, this invention not only provides a comprehensive evaluation score, but also deeply reveals the collaborative development status between various subsystems within the system, providing planning and design personnel with a deeper level of insight; The evaluation process is clear, can be implemented through conventional tools, and can directly serve practical needs such as IES scheme comparison and post-commissioning evaluation, with significant application value. Attached Figure Description

[0069] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0070] like Figure 1 As shown, a method for evaluating the coupling coordination degree of an integrated energy system in an industrial park includes the following sequential steps:

[0071] (1) Construct an evaluation index system and perform data standardization processing to obtain a standardized decision matrix. ;

[0072] (2) Based on standardized decision matrix We perform combinatorial weighting based on game theory to calculate the comprehensive weight vector of each indicator. ;

[0073] (3) Using standardized decision matrices With the comprehensive weight vector Calculate the coupling coordination degree of the system ;

[0074] (4) System coupling coordination degree of all evaluation samples This constitutes the historical evaluation results dataset. An adaptive grading standard based on data distribution characteristics is constructed to assess the system coupling coordination degree. Conduct qualitative assessments and grading.

[0075] Step (1) specifically includes the following steps:

[0076] (1a) Construct a multi-dimensional evaluation index system, which includes a target layer, three criterion layers and an indicator layer. The target layer is the level of coupled and coordinated development of the integrated energy system of the industrial park. The criterion layer is divided into three dimensions: physical architecture subsystem, information and communication subsystem and market transaction subsystem. The indicator layer includes multiple performance indicators under each criterion layer. The physical architecture subsystem includes renewable energy penetration rate, network loss rate, voltage deviation rate and power supply reliability. The information and communication subsystem includes data integrity rate, data acquisition cycle deviation, communication delay and network coverage. The market transaction subsystem includes clean energy transaction ratio, integrated energy purchase unit cost and demand response participation.

[0077] (1b) Construct the original data matrix Actual data collected from the integrated energy system of the industrial park The evaluation sample consists of historical operating moments or typical design conditions, along with raw monitoring data for n performance indicators acquired for each sample. Subsequently, the raw data matrix... Perform standardization processing to generate a standardized decision matrix. For both positive and negative indicators, range standardization is performed using equation (1):

[0078] (1);

[0079] in, Let j be the original observed value of the performance index in the i-th evaluation sample. and The j-th indicator is in all Minimum and maximum values ​​in each evaluation sample These are the standardized performance index values;

[0080] Using the 5th percentile of the data distribution and the 95th percentile Replace each of the following in equation (1) and The specific formula after the replacement is as follows:

[0081] ;

[0082] After the calculation is complete, for values ​​outside the interval [0,1] The value is truncated or renormalized.

[0083] Step (2) specifically includes the following steps:

[0084] (2a) Determine the subjective weight vector using the best-worst method By constructing and solving the linear programming model shown in equation (2), the optimal consistency scaling factor is calculated. :

[0085] (2);

[0086] in, Let be the subjective weight of the j-th index to be solved; and The pre-set optimal index C B And worst-case indicator C W The weight; a Bj and a jW All are degrees of importance preference;

[0087] Then, the corresponding values ​​for each indicator can be obtained. Finally, the subjective weight vector is constructed from the weight values. ;

[0088] Calculate the consistency ratio :

[0089] (3);

[0090] in, As a consistency indicator, The numerical value and the parameter a of expert preference BW There exists a deterministic correspondence: when a BW When taking the numbers 1, 2, 3, 4, 5, 6, 7, 8, and 9 respectively, the corresponding... The values ​​are 0.00, 0.44, 1.00, 1.63, 2.30, 3.00, 3.73, 4.47, and 5.23 respectively; if the calculated... If the value is less than or equal to a preset threshold, the expert group's input data is deemed to have acceptable consistency, and the resulting subjective weight vector is... It possesses mathematical validity; if the calculated CR value is greater than the preset threshold, the expert preference parameter a is adjusted. BW And resolve equation (3); expert preference parameter a BW The optimal index C B Compared to the worst indicator C WThe preference multiple;

[0091] (2b) Determine the objective weight vector using the entropy weight method First, based on the standardized decision matrix Calculate the proportion of the index value of the i-th evaluation sample among all evaluation samples under the j-th index. :

[0092] ;

[0093] In the formula, is the standardized performance index value; m is the number of evaluation samples;

[0094] Secondly, calculate the information entropy value of the j-th indicator. and :

[0095]

[0096] To ensure the validity of logarithmic calculations, when When = 0, define the limiting case. Finally, the information redundancy of each indicator is normalized to obtain the objective weight of the j-th indicator. This constitutes the objective weight vector. :

[0097] ;

[0098] In the formula, n is the number of performance indicators;

[0099] (2c) Apply game theory models to perform combinatorial weighting, and seek subjective weight vectors. With objective weight vector The Nash equilibrium between them yields the comprehensive weight vector. :

[0100] First, the optimal linear combination coefficients are determined by solving the system of linear equations. :

[0101] ;

[0102] in, Represents the dot product of vectors;

[0103] Subsequently, the combination coefficients are normalized and weighted summed to obtain the final comprehensive weight vector. :

[0104] .

[0105] Step (3) specifically includes the following steps:

[0106] (3a) Based on the comprehensive weight vector and standardized decision matrix Calculate the comprehensive development index of the physical architecture subsystem, information and communication subsystem, and market transaction subsystem respectively:

[0107] ;

[0108] in, These are the comprehensive development indices of the physical architecture subsystem, information and communication subsystem, and market transaction subsystem of the i-th sample, respectively. These are indexes of performance metrics contained in the physical architecture subsystem, information and communication subsystem, and market trading subsystem, respectively. These are the standardized performance index values;

[0109] (3b) Calculate the coupling degree characterizing the interaction strength between the three subsystems. :

[0110] ;

[0111] (3c) Calculate the comprehensive evaluation coefficient representing the overall development level of the system. :

[0112] ;

[0113] in, The importance coefficients of the physical architecture subsystem, information and communication subsystem, and market transaction subsystem in the overall evaluation are satisfied. and The undetermined coefficients of the nonnegative convex constraint conditions;

[0114] (3d) Calculate the system coupling coordination degree, which comprehensively reflects the system coupling coordination development level. :

[0115] .

[0116] Step (4) specifically refers to: calculating the system coupling coordination degree for all evaluation samples. This data is compiled to form a historical evaluation results dataset. ;

[0117] The historical evaluation result dataset is calculated according to the following formula. mean and standard deviation :

[0118] ;

[0119] ;

[0120] Based on the calculated mean and standard deviation An adaptive grading standard is constructed to assess the coupling and coordination degree of the system to be evaluated. Comparison with adaptive grading standards:

[0121] like If so, it is judged to be at the level of severe disorder;

[0122] like If so, it is judged as a mild disorder level;

[0123] like If so, it is determined to be at the basic coordination level;

[0124] like If so, it is judged to be at a good coordination level;

[0125] like If so, it is judged as a high-quality coordination level.

[0126] In summary, this invention proposes for the first time a three-layer coupled evaluation framework based on "physical architecture - information communication - market transaction," which can comprehensively and systematically reflect the complex characteristics of the integrated energy system of industrial parks. It fundamentally overcomes the one-sidedness of traditional single-dimensional evaluation, making the evaluation results more reflective of the overall system performance. This invention uniquely combines Bloom-Walking (BWM), entropy weighting, and game theory to form a theoretically rigorous combined weighting method. Compared to Alternative Power Hierarchy Process (AHP), BWM achieves higher consistency with fewer comparisons, reducing the cognitive burden on experts. Game theory provides a non-arbitrary, Nash equilibrium-seeking optimal solution for integrating subjective and objective weights, ensuring the scientific nature of the final weights and their robustness to different data distributions, significantly improving the credibility of the evaluation results. This invention not only provides a comprehensive evaluation score but also deeply reveals the collaborative development status among the various subsystems within the system, providing deeper insights for planning and design personnel. The evaluation process is clear, can be implemented using conventional tools, and can directly serve practical needs such as IES scheme comparison and post-commissioning evaluation, demonstrating significant application value.

[0127] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An evaluation method for coupling coordination degree of an industrial park integrated energy system, characterized in that: The method comprises the following steps in sequence: (1) Constructing the evaluation index system and processing the data standardization to obtain the standardization decision matrix ; (2) Based on the standardization of the decision matrix , the combination weighting based on game theory is carried out, and the comprehensive weight vector of each index is calculated ; (3) using a standardized decision matrix and a comprehensive weight vector , calculating the system coupling coordination degree ; (4) System coupling coordination degree of all evaluation samples This constitutes the historical evaluation results dataset. An adaptive grading standard based on data distribution characteristics is constructed to assess the system coupling coordination degree. Conduct qualitative assessments and grading.

2. The method of claim 1, wherein the coupling coordination degree of the industrial park integrated energy system is evaluated. Step (1) specifically comprises the following steps: (1a) constructing a multi-dimensional evaluation index system, the index system comprising one target layer, three criterion layers and one index layer, the target layer being the coupling coordination development level of the industrial park comprehensive energy system; the criterion layer being divided into three dimensions of a physical architecture subsystem, an information communication subsystem and a market transaction subsystem; the index layer comprising a plurality of performance indexes under the jurisdiction of each criterion layer; the physical architecture subsystem comprising renewable energy penetration rate, network loss rate, voltage deviation rate and power supply reliability; the information communication subsystem comprising data integrity rate, acquisition cycle deviation, communication delay and network coverage rate; the market transaction subsystem comprising clean transaction proportion, comprehensive energy purchasing unit cost and demand response participation rate; (1b) Constructing the original data matrix The actual collected data from the industrial park comprehensive energy system At each of the n historical operation time or typical design conditions as evaluation samples, and the original monitoring data of n performance indicators obtained for each sample; then, the original data matrix Perform standardization to generate a standardized decision matrix For positive and negative indicators, use formula (1) for range standardization: （1）； wherein, is the original observation value of the jth performance indicator in the ith evaluation sample, and are the minimum and maximum values of the jth indicator in all evaluation samples, respectively, is the normalized performance indicator value. using the 5th percentile of the data distribution and the 95th percentile respectively in place of and in equation (1) as follows: ； After the computation, for values that exceed the interval [0, 1] a truncation or renormalization is performed.

3. The method for evaluating the coupling coordination degree of an integrated energy system in an industrial park according to claim 1, characterized in that: Step (2) specifically comprises the following steps: (2a) The subjective weight vector is determined by the optimal worst method The optimal consistency ratio factor is calculated by constructing and solving the linear programming model shown in equation (2) : （2）； wherein, is the subjective weight of the jth index to be solved; and are the weights of the optimal index C B and the worst index C W respectively; a Bj and a jW are the importance preference degrees. Further, the corresponding indicators are solved , and finally the subjective weight vector is composed of each weight value ; Computing a consistency ratio : （3）； in, As a consistency indicator, The numerical value and the parameter a of expert preference BW There exists a deterministic correspondence: when a BW When taking the numbers 1, 2, 3, 4, 5, 6, 7, 8, and 9 respectively, the corresponding... The values ​​are 0.00, 0.44, 1.00, 1.63, 2.30, 3.00, 3.73, 4.47, and 5.23 respectively; if the calculated... If the value is less than or equal to a preset threshold, the expert group's input data is deemed to have acceptable consistency, and the resulting subjective weight vector is... It possesses mathematical validity; if the calculated CR value is greater than the preset threshold, the expert preference parameter a is adjusted. BW And resolve equation (3); expert preference parameter a BW The optimal index C B Compared to the worst indicator C W The preference multiple; (2b) The objective weight vector is determined by using the entropy weight method : First, the proportion of the index value of the ith evaluation sample under the jth index in all evaluation samples is calculated based on the normalized decision matrix . : ； In the formula, is the normalized performance index value; m is the number of evaluation samples; Secondly, the information entropy value of the jth index is calculated and : ； To ensure the effectiveness of the logarithmic calculation, when =0, the limit case is defined ; finally, the information redundancy of each index is normalized to obtain the objective weight of the jth index , which constitutes the objective weight vector : ； In the formula, n is the number of performance indexes; (2c) applying a game theory model to combine the weights, by seeking a Nash equilibrium between the subjective weight vector and the objective weight vector to obtain a comprehensive weight vector : First, the optimal linear combination coefficients are determined by solving a system of linear equations : ； wherein denotes the dot product of vectors; Subsequently, the combination coefficients are normalized and summed up with weights to obtain the final comprehensive weight vector : 。 4. The method of claim 1, wherein the coupling coordination degree of the industrial park integrated energy system is evaluated. Step (3) specifically comprises the following steps: (3a) Based on the integrated weight vector and the normalized decision matrix , the integrated development index of the physical architecture subsystem, the information communication subsystem and the market transaction subsystem is calculated respectively: ； wherein, respectively, are the comprehensive development indexes of the physical architecture subsystem, the information communication subsystem, and the market transaction subsystem of the i-th sample; respectively, are the index sets of the performance indicators contained in the physical architecture subsystem, the information communication subsystem, and the market transaction subsystem; is the standardized performance indicator value. (3b) calculating a coupling degree representing the strength of interaction between the three subsystems : ； (3c) calculating a comprehensive evaluation coefficient representing the overall development level of the system : ； wherein, is the importance coefficient of the physical architecture subsystem, the information communication subsystem, and the market transaction subsystem in the overall evaluation, satisfying and the non-negative convex constraint condition of the undetermined coefficient; (3d) calculating a system coupling coordination degree comprehensively reflecting the system coupling coordination development level : 。 5. The method of claim 1, wherein the coupling coordination degree of the industrial park integrated energy system is evaluated. Step (4) is specifically defined as follows: calculating the system coupling coordination degree for all evaluation samples aggregating to form a historical evaluation result dataset ; The historical evaluation result dataset is calculated according to the mean value of the historical evaluation result dataset and the standard deviation of the historical evaluation result dataset : ； ； Based on the calculated mean and standard deviation An adaptive grading standard is constructed to assess the coupling and coordination degree of the system to be evaluated. Comparison with adaptive grading standards: If then the decision is made that the misalignment is of a severe grade; If then the mild misalignment level is determined; If then the basic coordination level is determined; If then the level of coordination is determined to be good; If then the quality coordination level is determined to be superior.

6. An electronic device comprising: a processor; and a memory having computer program instructions stored therein, the computer program instructions causing the processor to execute the industrial park comprehensive energy system coupling coordination degree evaluation method according to any one of claims 1-5 when the computer program instructions are run by the processor.

7. A computer readable storage medium having computer program instructions stored thereon, the computer program instructions causing the processor to execute the industrial park comprehensive energy system coupling coordination degree evaluation method according to any one of claims 1-5 when the computer program instructions are run by the processor.

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