Green electricity transaction industry chain dynamic optimization method based on multi-factor evaluation

Abstract

The present application relates to the technical field of green electricity transaction industry chain optimization, and particularly relates to a green electricity transaction industry chain dynamic optimization method based on multi-factor evaluation, which realizes accurate description of the network structure of the green electricity transaction industry chain through multi-dimensional node attribute definition and bidirectional relationship edge construction, and can comprehensively capture the economic cooperation and implicit decision transmission relationship between nodes in the industry chain by distinguishing the quantitative logic of transaction relationship edges and influence edges, combining the comprehensive correlation strength to construct a unified adjacency matrix, and providing a network model basis that fits the actual business scenario for subsequent optimization, the present application innovatively integrates efficiency and fairness targets into the dynamic optimization framework, realizes the collaborative balance of efficiency improvement and fairness guarantee of the industry chain by scientifically designing the objective function and introducing a multi-objective optimization algorithm, and the efficiency target focuses on resource allocation and cost optimization, and the fairness target focuses on the accessibility of edge nodes and the correlation balance.

Description

Technical Field

[0001] This invention relates to the field of green electricity trading industry chain optimization technology, and more specifically, to a dynamic optimization method for the green electricity trading industry chain based on multi-factor evaluation. Background Technology

[0002] As a key vehicle for promoting energy structure transformation, the green electricity trading industry chain involves diverse participants and complex relationships, encompassing various entities such as power generators, electricity retailers, users, and regulatory agencies, and involving multi-dimensional interactions such as power purchase and sale contract execution and strategy following. However, existing technologies for analyzing the industry chain often focus on single transaction links or local node relationships, making it difficult to comprehensively capture the existing economic cooperation and implicit decision-making influences between nodes. This results in an insufficiently accurate depiction of the overall network structure of the industry chain, hindering a deeper understanding of its operational patterns.

[0003] In the optimization practice of the green electricity trading industry chain, traditional methods often focus on a single efficiency goal, such as reducing transaction costs or increasing green electricity consumption, while neglecting the need for fairness in resource acquisition opportunities and benefit distribution among different nodes. Peripheral nodes often face insufficient access to green electricity due to weak connections and low information accessibility, while the concentration of resources in core nodes may exacerbate the imbalance in the development of the industry chain. This optimization model that "emphasizes efficiency over fairness" is difficult to adapt to the requirements of inclusive development of green electricity trading.

[0004] Meanwhile, the green electricity trading industry chain exhibits significant dynamic characteristics. Influenced by factors such as market supply and demand fluctuations and changes in node behavior, the strength of connections between nodes and the network structure continuously evolve. Existing optimization methods lack an effective response mechanism to this dynamism, making it difficult to update optimization strategies in real time to adapt to the changing needs of the industry chain. This results in insufficient timeliness and specificity of optimization measures, failing to provide reliable support for the continuous and stable operation of the green electricity trading industry chain. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a dynamic optimization method for the green electricity trading industry chain based on multi-factor evaluation.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The dynamic optimization method for the green electricity trading industry chain based on multi-factor evaluation includes the following steps:

[0008] Step 1: Identify all participants in the green electricity trading industry chain, define each participant as a node, and define the node type and node attributes of each participant;

[0009] Step 2: Compare each pair of nodes to determine whether there are transaction relationships and influence edges between each pair of compared nodes;

[0010] Step 3: Construct a unified adjacency matrix and determine each element in the adjacency matrix. The comprehensive association strength is determined by calculating the degree center index based on the adjacency matrix, thereby identifying the core nodes and edge nodes in the adjacency matrix.

[0011] Step 4: Determine the efficiency objective function and fair objective function ;

[0012] Step 5: Using NSGA-II, the overall correlation strength is calculated. The degree center index is used as the input of the algorithm to generate a Pareto optimal solution set, output the optimization strategy in the Pareto optimal solution set, and transform it into an executable industrial chain optimization measure. The executable industrial chain optimization measure is used to dynamically optimize the green electricity trading industrial chain.

[0013] Furthermore, the specific process for determining whether a transaction relationship exists is as follows: Determine whether there is an effective and actually executed power purchase and sale contract between the two nodes. After determining that there is an effective and actually executed power purchase and sale contract, calculate the weight of the transaction relationship edge using the basic transaction strength and quality correction coefficient, set a threshold for the weight of the transaction relationship edge, and determine that there is a transaction relationship edge between the two nodes when the weight of the transaction relationship edge is higher than the threshold for the weight of the transaction relationship edge.

[0014] Furthermore, the underlying trading strength The calculation formula is as follows: ;in, Average trading volume For trading frequency, d1 is the trading distance, d2 is the average trading volume coefficient, d3 is the trading frequency coefficient, and d4 is the trading distance coefficient.

[0015] Furthermore, the quality correction factor The calculation formula is as follows: ;in, For electricity delivery fulfillment rate, For the duration of node cooperation, y1 is the electricity fulfillment rate coefficient, y2 is the node cooperation duration coefficient, and y3 is the transaction volatility coefficient.

[0016] Furthermore, the specific process for determining whether an influence edge exists is as follows: the influence edge weight is calculated by using the decision transmission strength and influence breadth coefficient, an influence edge weight threshold is set, and when the influence edge weight is higher than the influence edge weight threshold, it is determined that an influence edge exists between the two nodes.

[0017] Furthermore, the strength of decision transmission The calculation formula is as follows: ;in, For green electricity price similarity, U1 represents the green electricity strategy following degree, u2 represents the green electricity price similarity coefficient, and u2 represents the green electricity strategy following coefficient.

[0018] Furthermore, the influence of the breadth coefficient The calculation formula is as follows: ;in, To follow the number of nodes, To follow the average size coefficient of the nodes, This represents the total number of nodes.

[0019] Furthermore, matrix elements This represents the overall association strength from node i to node j. The calculation formula is: ; Let be the difference between the weight of the transaction edge from node i to node j and the threshold weight of the transaction edge. If the difference is ≤ 0, then... The value is 0. Let be the difference between the influence edge weight from node i to node j and the influence edge weight threshold. If the difference is ≤ 0, then... The value is 0.

[0020] Furthermore, Where w1 and w2 are dynamic weights, and w1 + w2 = 1. Let i be the green electricity transaction volume from node i to j. For the total electricity demand of the green electricity trading industry chain, Let $\frac{i}{j}$ be the transmission cost from node $i$ to node $j$. Where w3 and w4 are dynamic weights, w3 + w4 = 1, C is the set of core nodes, and E is the set of edge nodes. This represents the variance of the overall correlation strength across the entire industry chain.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This invention's method, through multi-dimensional node attribute definition and bidirectional relation edge construction, achieves a precise characterization of the green electricity trading industry chain network structure. By distinguishing between the quantitative logic of transaction relationship edges and influence edges, and combining comprehensive association strength to construct a unified adjacency matrix, it can comprehensively capture the economic cooperation and implicit decision-making transmission relationships between nodes in the industry chain. This provides a network model foundation that fits actual business scenarios for subsequent optimization. This invention innovatively integrates efficiency and fairness objectives into a dynamic optimization framework. Through the scientific design of objective functions and the introduction of multi-objective optimization algorithms, it achieves a synergistic balance between improving efficiency and ensuring fairness in the industry chain. The efficiency objective focuses on resource allocation and cost optimization, while the fairness objective focuses on the accessibility and association balance of edge nodes. The two are organically linked through the Pareto optimal solution set generation mechanism, avoiding the imbalance between efficiency and fairness caused by single-objective optimization, and meeting the diverse needs of high-quality development of the green electricity trading industry chain. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation;

[0024] Figure 2 A flowchart for determining whether a transaction relationship exists;

[0025] Figure 3 This is a flowchart for determining whether an influential edge exists. Detailed Implementation

[0026] Reference Figures 1 to 3 The dynamic optimization method for the green electricity trading industry chain based on multi-factor evaluation includes the following steps:

[0027] Step 1: Identify all participants in the green electricity trading industry chain, defining each participant as a node, and defining the node type and attributes for each participant. Each participant will only be defined as one node type, including all participants' node types as power generators, electricity retailers, large users, small and medium-sized users, community energy organizations, grid companies, and regulatory agencies. Each participant has multiple node attributes, including basic attributes, behavioral attributes, connection attributes, and influence attributes. Basic attributes specifically include node ID (unique identifier) ​​and node creation time. Behavioral attributes specifically include annual green electricity trading volume, trading frequency, demand response participation rate, and green certificate holdings. Connection attributes specifically include the number of historical cooperative nodes, average cooperation duration, and default rate. Influence attributes specifically include the price proposal adoption rate and user follow-up rate (e.g., the proportion of small and medium-sized users imitating the strategies of large users).

[0028] Step 2: Compare each pair of nodes to determine whether there is a transaction relationship edge and an influence edge between each pair of nodes (both transaction relationship edges and influence edges are unidirectional edges).

[0029] The specific process for determining whether a transaction relationship exists is as follows: First, determine if there is an effective and actually executed power purchase and sale contract (PPC) between the two nodes. Once a PPC is confirmed (if no PPC exists, no further action is required), proceed through... Calculate the edge weights of the transaction relationships ,in, Based on trading strength, To correct the quality, a threshold for the weight of the transaction relationship edge is set (the threshold for the weight of the transaction relationship edge is set based on industry standards and empirical values). When the weight of the transaction relationship edge is higher than the threshold for the weight of the transaction relationship edge (if it is not higher, there is no transaction relationship edge), it is determined that there is a transaction relationship edge between the two nodes.

[0030] Basic trading strength The calculation formula is as follows: ;in, This represents the average transaction volume (the average transaction volume between two nodes over a duration of T). Transaction frequency (the frequency of transactions between two nodes within a time period T). Let d1 be the average transaction volume coefficient, d2 be the transaction frequency coefficient, and d3 be the transaction distance coefficient. The values ​​of d1, d2, and d3 are defined according to the node types of the two nodes. For example, when the node types of the two nodes are a power generator and a small and medium-sized user, the average transaction volume coefficient is 0.5, the transaction frequency coefficient is 0.6, and the transaction distance coefficient is 1.1.

[0031] Quality correction factor The calculation formula is as follows: ;in, The electricity performance rate is the ratio of the actual electricity volume performed by two nodes within a time period T to the electricity volume agreed in the power purchase and sale contract. The duration of node cooperation (the total duration of the power purchase and sale contract between the two nodes). The trading volatility (the trading volatility of electricity between two nodes within a time period T: dividing the time period T into n trading cycles, obtaining the trading electricity in each trading cycle, summing and averaging the trading electricity in all trading cycles to calculate the average trading electricity, calculating the deviation of the trading electricity in each trading cycle from the average trading electricity, and then calculating the standard deviation, and calculating the trading volatility by the ratio of the standard deviation to the average trading electricity), y1 is the electricity fulfillment rate coefficient, y2 is the node cooperation duration coefficient, and y3 is the trading volatility coefficient. The values ​​of y1, y2, and y3 are defined according to the node types of the two nodes. For example, when the node types of the two nodes are a power generator and a small and medium-sized user, the electricity fulfillment rate coefficient is 0.7, the node cooperation duration coefficient is 0.5, and the trading volatility coefficient is 1.8.

[0032] The specific process for determining whether an influence edge exists is as follows: Through Calculate the influence edge weight ,in, For the strength of decision transmission, To influence the breadth coefficient, an influence edge weight threshold is set (the influence edge weight threshold is set based on industry standards and experience). When the influence edge weight is higher than the influence edge weight threshold (if it is not higher, there is no influence edge), it is determined that there is an influence edge between the two nodes.

[0033] Decision transmission strength The calculation formula is as follows: ;in, The similarity of green electricity prices is calculated using the cosine similarity algorithm between node A and node B. The formula is: Green electricity price similarity = (Price vector of A · Price vector of B) / (||Price vector of A|| × ||Price vector of B||); where the price vector is the price data sequence at different time points. The higher the similarity, the stronger the price transmission effect. For example, if the price vectors of node A and node B for the past 10 trading days are [1.2, 1.3, 1.25, ...] and [1.22, 1.31, 1.24, ...], the calculated similarity is 0.95. The green electricity strategy follow-through coefficient is calculated as the proportion of times node B follows node A's strategy within a time period T out of the total number of strategy attempts. For example, if node A proposes a demand response strategy and node B adopts the same strategy within the following 24 hours, it is counted as one follow-through. For instance, if node A proposes a strategy 5 times and node B follows it 3 times, then the green electricity strategy follow-through coefficient = 3 / 5 = 0.6. u1 is the green electricity price similarity coefficient, and u2 is the green electricity strategy follow-through coefficient. In the green electricity trading field, price factors are generally considered to have a more critical and direct impact on node decisions. Long-term industry practice and experience show that price fluctuations and changes are often an important basis for node trading decisions, and their influence is relatively large; therefore, the green electricity price similarity coefficient is 0.6. While strategy follow-through is also important, its impact on overall decision-making is relatively weaker than that of price factors; therefore, the green electricity strategy follow-through coefficient is 0.4.

[0034] Influence on breadth coefficient The calculation formula is as follows: ;in, To determine the number of following nodes, we identify the target node among two nodes (the target node is the node that has an impact among the two nodes), compare the target node with all other nodes in the green electricity trading industry chain pairwise, obtain the decision transmission strength between the target node and each other, set a decision transmission strength threshold (the decision transmission strength threshold is set based on industry standards and experience values), when the decision transmission strength is higher than the decision transmission strength threshold, the corresponding node is marked as a following node, and when it is not higher, it is not marked. The total number of following nodes is counted. The average size factor for the following nodes is calculated (different size factors are assigned based on the node type of the following nodes, such as 1.2 for large users, 0.8 for small and medium-sized users, and 1.0 for community energy organizations. The average size factor of all following nodes is calculated. For example, if there are 3 following nodes, namely large users, small and medium-sized users, and community energy organizations, with size factors of 1.2, 0.8, and 1.0 respectively, the average value is (1.2 + 0.8 + 1.0) / 3 = 1.0). This refers to the total number of nodes (i.e., the total number of nodes in the green electricity trading industry chain).

[0035] Step 3: Construct a unified adjacency matrix and determine each element in the adjacency matrix. The overall association strength is determined by calculating the degree centrality index based on the adjacency matrix (the calculation of the degree centrality index is the most basic and commonly used conventional method in network analysis), thereby determining the core nodes and edge nodes in the adjacency matrix (among all nodes, the first M% of degree centrality are core nodes (M can be 20), and the last N% are edge nodes (N can be 30)).

[0036] The adjacency matrix is ​​an n×n square matrix (where n is the total number of nodes in the green electricity trading industry chain), and the matrix elements are... This represents the overall association strength from node i to node j. The calculation formula is: The transaction relationship reflects direct economic cooperation and has a higher weighting, with a set coefficient of [value missing]. The influence factor reflects the implicit correlation in decision-making guidance, with a secondary weighting and a set coefficient of [value missing]. ; Let be the difference between the weight of the transaction edge from node i to node j and the threshold weight of the transaction edge. If the difference is ≤ 0, then... The value is 0. Let be the difference between the influence edge weight from node i to node j and the influence edge weight threshold. If the difference is ≤ 0, then... The value is 0.

[0037] Step 4: Determine the efficiency objective function and fair objective function ;

[0038] Among them, w1 and w2 are dynamic weights, w1+w2=1, which can be adjusted according to the scenario (e.g., when green electricity is in short supply, w1 is increased to 0.7). Let i be the green electricity transaction volume from node i to j. For the total electricity demand of the green electricity trading industry chain, The transmission cost from node i to j (including direct costs such as line occupancy fees and dispatching fees); Among them, w3 and w4 are dynamic weights, w3+w4=1, which can be adjusted according to the scenario (e.g., w3 is increased to 0.7 in rural areas), C is the core node set (e.g., electricity sales companies, power generators, regulatory agencies, the top 20% of nodes in degree centrality), and E is the edge node set (e.g., small and medium-sized users, remote communities, the bottom 30% of nodes in tight centrality). The variance of the overall correlation strength of the entire industry chain (measures the balance of correlation distribution, i.e., calculating the variance of all elements in the adjacency matrix). (variance).

[0039] Step 5: Using an improved NSGA-II (non-dominated sorting genetic algorithm), the overall association strength is calculated. The degree center metric is used as input to the algorithm to generate a Pareto optimal solution set (simultaneously optimizing a combination of efficient and fair policies). The output is the optimized policies in the Pareto optimal solution set (e.g., if there are three policies in the Pareto optimal solution set, namely policy A, policy B, and policy C, the weighted score POF of policy A, policy B, and policy C is obtained). k1+k2=1. Based on the core demands of the green electricity trading industry chain, the weights of k1 and k2 are set. When the green electricity consumption rate of the green electricity trading industry chain is low, k1 can be 0.6 and k2 can be 0.4. The strategy with the largest weighted score POF is marked as the optimization strategy and transformed into an executable industry chain optimization measure. The executable industry chain optimization measure is used to dynamically optimize the green electricity trading industry chain.

[0040] The aforementioned method, through multi-dimensional node attribute definition and bidirectional relation edge construction, achieves a precise characterization of the green electricity trading industry chain network structure. By distinguishing between the quantitative logic of transaction relationship edges and influence edges, and combining comprehensive association strength to construct a unified adjacency matrix, it can comprehensively capture the economic cooperation and implicit decision-making transmission relationships between nodes in the industry chain, providing a network model foundation that fits actual business scenarios for subsequent optimization. This invention innovatively integrates efficiency and fairness objectives into a dynamic optimization framework. Through the scientific design of objective functions and the introduction of multi-objective optimization algorithms, it achieves a synergistic balance between improving efficiency and ensuring fairness in the industry chain. The efficiency objective focuses on resource allocation and cost optimization, while the fairness objective focuses on the accessibility and association balance of edge nodes. The two are organically linked through the Pareto optimal solution set generation mechanism, avoiding the imbalance between efficiency and fairness caused by single-objective optimization, and meeting the diverse needs of high-quality development of the green electricity trading industry chain.

[0041] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0042] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0043] It should be understood that, in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0044] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0045] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0046] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0047] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic optimization method for the green electricity trading industry chain based on multi-factor evaluation, characterized in that, Includes the following steps: Step 1: Identify all participants in the green electricity trading industry chain, define each participant as a node, and define the node type and node attributes of each participant; Step 2: Compare each pair of nodes to determine whether there are transaction relationships and influence edges between each pair of compared nodes; Step 3: Construct a unified adjacency matrix and determine each matrix element A in the adjacency matrix. ij The overall association strength is determined by calculating the degree center index based on the adjacency matrix, thereby identifying the core and edge nodes in the adjacency matrix; matrix element A ij A represents the overall association strength from node i to node j. ij The calculation formula is: ; Let be the difference between the weight of the transaction edge from node i to node j and the threshold weight of the transaction edge. If the difference is ≤ 0, then... The value is 0. Let be the difference between the influence edge weight from node i to node j and the influence edge weight threshold. If the difference is ≤ 0, then... The value is 0; Step 4: Determine the efficiency objective function F eff and the fair objective function F fair ; Where w1 and w2 are dynamic weights, w1 + w2 = 1, Q ij Let i be the green electricity transaction volume from node i to j. For the total electricity demand of the green electricity trading industry chain, Let $\frac{i}{j}$ be the transmission cost from node $i$ to node $j$. Where w3 and w4 are dynamic weights, w3 + w4 = 1, C is the set of core nodes, and E is the set of edge nodes. The variance of the overall correlation strength of the entire industry chain; Step 5: Using NSGA-II, the overall correlation strength is calculated. The degree center index is used as the input of the algorithm to generate a Pareto optimal solution set, output the optimization strategy in the Pareto optimal solution set, and transform it into an executable industrial chain optimization measure. The executable industrial chain optimization measure is used to dynamically optimize the green electricity trading industrial chain.

2. The method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation as described in claim 1, characterized in that, The specific process for determining whether a transaction relationship exists is as follows: First, determine whether there is an effective and actually executed power purchase and sale contract between the two nodes. After determining that there is an effective and actually executed power purchase and sale contract, calculate the weight of the transaction relationship edge using the basic transaction strength and quality correction coefficient. Set a threshold for the weight of the transaction relationship edge. When the weight of the transaction relationship edge is higher than the threshold for the weight of the transaction relationship edge, it is determined that there is a transaction relationship edge between the two nodes.

3. The method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation as described in claim 2, characterized in that, Basic Trading Strength S basic The calculation formula is as follows: Where Vwap is the average trading volume, Trfr is the trading frequency, Ditr is the trading distance, d1 is the average trading volume coefficient, d2 is the trading frequency coefficient, and d3 is the trading distance coefficient.

4. The method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation according to claim 2, characterized in that, Quality correction factor Q coe The calculation formula is as follows: Among them, Per ra For electricity delivery fulfillment rate, Dur co y1 is the node cooperation duration, Ec is the transaction volatility, y2 is the electricity fulfillment rate coefficient, y3 is the node cooperation duration coefficient, and y3 is the transaction volatility coefficient.

5. The method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation as described in claim 1, characterized in that, The specific process for determining whether an influence edge exists is as follows: the influence edge weight is calculated by using the decision transmission strength and influence breadth coefficient, an influence edge weight threshold is set, and when the influence edge weight is higher than the influence edge weight threshold, it is determined that an influence edge exists between the two nodes.

6. The method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation as described in claim 5, characterized in that, Decision transmission strength The calculation formula is as follows: Where Pri is the green electricity price similarity, Fow is the green electricity strategy following degree, u1 is the green electricity price similarity coefficient, and u2 is the green electricity strategy following coefficient.

7. The method for dynamic optimization of the green electricity trading industry chain based on multi-factor evaluation as described in claim 5, characterized in that, Influence on breadth coefficient The calculation formula is as follows: Where Nofo is the number of follower nodes, dfp is the average size coefficient of follower nodes, and toa is the total number of nodes.

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