Operation evaluation method and system for electric carbon certificate market

Abstract

The invention discloses an operation evaluation method and system for an electric carbon certificate market, and the method comprises the steps: determining an evaluation index and corresponding index data based on the first text data related to the operation of a to-be-evaluated electric carbon certificate market through a natural language processing and semantic analysis technology; determining an index weight corresponding to the evaluation index by adopting an improved entropy weight method based on expert scoring data; and based on the index data and the index weight, determining an operation assessment result of the to-be-assessed electric carbon certificate market, so that a transaction system related to the operation of the electric carbon certificate market can be timely and accurately adjusted. Therefore, energy waste generated in the dispatching operation process of the power system is reduced, the energy utilization rate in the dispatching operation process of the power system is improved, and the resource configuration efficiency can be improved while the cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to an operation evaluation method and system for the electric carbon certificate market. Background Art

[0002] The electricity carbon certificate market usually refers to the relevant trading system used to trade green electricity certificates (green certificates) and carbon emission rights. Its purpose is to promote carbon emission reduction, reduce energy waste generated during the dispatching and operation of the power system, improve the energy utilization rate during the dispatching and operation of the power system, reduce costs while improving resource allocation efficiency.

[0003] In related technologies, manual evaluation of policy documents related to the operation of the power carbon certificate market is usually required to analyze the current operation status and potential problems of the power carbon certificate market. However, manual analysis is inefficient, and relying on personal experience cannot guarantee the accuracy of the analysis, making it difficult to adjust the trading system related to the operation of the power carbon certificate market in a timely and accurate manner, which in turn affects the energy utilization rate during the dispatch and operation of the power system. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, the embodiment of the present application proposes an operation evaluation method and system for the electricity carbon certificate market, which can timely and accurately adjust the trading system related to the operation of the electricity carbon certificate market, thereby reducing the energy waste generated during the dispatching and operation of the power system, improving the energy utilization rate during the dispatching and operation of the power system, reducing costs while improving resource allocation efficiency.

[0005] In a first aspect, the embodiment of the present application provides an operation evaluation method for the electric carbon certificate market, including:

[0006] Based on the first text data related to the operation of the electricity carbon certificate market to be evaluated, the evaluation index and its corresponding index data are determined through natural language processing and semantic analysis technology;

[0007] Based on the expert scoring data, the improved entropy weight method is used to determine the indicator weights corresponding to the evaluation indicators;

[0008] Based on the indicator data and the indicator weights, the operational evaluation results of the electricity carbon certificate market to be evaluated are determined.

[0009] Optionally, the determining of the evaluation index and its corresponding index data based on the first text data related to the operation of the electricity carbon certificate market to be evaluated by natural language processing and semantic analysis technology includes:

[0010] Acquire the first text data by using crawler technology;

[0011] Using statistical analysis, screening and correlation analysis are performed on the first text data to obtain second text data;

[0012] The evaluation index and its corresponding index data are extracted from the second text data through natural language processing and semantic analysis technology.

[0013] Optionally, the step of determining the indicator weight corresponding to the evaluation indicator by using an improved entropy weight method based on the expert scoring data includes:

[0014] Constructing a corresponding indicator evaluation matrix based on the expert scoring data;

[0015] Normalizing the indicator evaluation matrix;

[0016] Determine information entropy based on the normalized index evaluation matrix;

[0017] Using a pre-configured activation function, converting the information entropy into improved entropy;

[0018] An objective weight is calculated based on the improved entropy to serve as the indicator weight.

[0019] Optionally, determining the operational evaluation result of the to-be-evaluated electricity carbon certificate market based on the indicator data and the indicator weights includes:

[0020] Based on the indicator weights, the optimal indicator combination weights are determined using game theory;

[0021] Based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator, the fuzzy evaluation theory is used to determine the target layer evaluation grade membership;

[0022] According to the target layer evaluation grade membership, a target layer risk grade evaluation result for characterizing the operation evaluation result is determined.

[0023] Optionally, based on the indicator weights, determining the optimal indicator combination weights using game theory includes:

[0024] Linearly combining the indicator weights to obtain a weight set, wherein the weight set includes multiple weight groups, and the multiple weight groups correspond to multiple preset levels;

[0025] Game theory is used to optimize the weight set to obtain the optimal indicator combination weight.

[0026] Optionally, the step of determining the target layer evaluation grade membership by using fuzzy evaluation theory based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator includes:

[0027] Constructing an initial evaluation set based on the indicator risk assignment;

[0028] Based on the indicator data and the initial evaluation set, calculating the membership degree corresponding to the evaluation indicator, and normalizing the membership degree;

[0029] Based on the optimal indicator combination weights and the normalized membership, the target indicator evaluation matrix corresponding to the evaluation indicator is determined to characterize the target layer evaluation level membership.

[0030] In a second aspect, the embodiment of the present application provides an operation evaluation system for the electric carbon certificate market, including:

[0031] An indicator mining module, used to determine the evaluation indicator and its corresponding indicator data based on the first text data related to the operation of the electric carbon certificate market to be evaluated through natural language processing and semantic analysis technology;

[0032] A weight determination module is used to determine the indicator weight corresponding to the evaluation indicator based on the expert scoring data by using an improved entropy weight method;

[0033] An evaluation module is used to determine the operational evaluation result of the electric carbon certificate market to be evaluated based on the indicator data and the indicator weights.

[0034] Optionally, the determining of the evaluation index and its corresponding index data based on the first text data related to the operation of the electricity carbon certificate market to be evaluated by natural language processing and semantic analysis technology includes:

[0035] Acquire the first text data by using crawler technology;

[0036] Using statistical analysis, the text data is screened and analyzed for relevance to obtain second text data;

[0037] The evaluation index and its corresponding index data are extracted from the second text data through natural language processing and semantic analysis technology.

[0038] Optionally, the step of determining the indicator weight corresponding to the evaluation indicator by using an improved entropy weight method based on the expert scoring data includes:

[0039] Constructing a corresponding indicator evaluation matrix based on the expert scoring data;

[0040] Normalizing the indicator evaluation matrix;

[0041] Determine information entropy based on the normalized index evaluation matrix;

[0042] Using a pre-configured activation function, converting the information entropy into improved entropy;

[0043] An objective weight is calculated based on the improved entropy to serve as the indicator weight.

[0044] Optionally, determining the operational evaluation result of the to-be-evaluated electricity carbon certificate market based on the indicator data and the indicator weights includes:

[0045] Based on the indicator weights, the optimal indicator combination weights are determined using game theory;

[0046] Based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator, the fuzzy evaluation theory is used to determine the target layer evaluation grade membership;

[0047] According to the target layer evaluation grade membership, a target layer risk grade evaluation result for characterizing the operation evaluation result is determined.

[0048] In summary, the embodiments of the present application have at least the following beneficial effects:

[0049] By adopting the embodiment of the present application, based on the first text data related to the operation of the electricity carbon certificate market to be evaluated, the evaluation index and its corresponding index data are determined by natural language processing and semantic analysis technology; based on the expert scoring data, the improved entropy weight method is used to determine the index weight corresponding to the evaluation index; based on the index data and the index weight, the operation evaluation result of the electricity carbon certificate market to be evaluated is determined, so that important evaluation indicators and their corresponding index data can be automatically mined from the relevant data through natural language processing and semantic analysis technology without manual analysis, and the trading system related to the operation of the electricity carbon certificate market can be adjusted in a timely and accurate manner, thereby reducing the energy waste generated during the dispatching and operation of the power system, improving the energy utilization rate during the dispatching and operation of the power system, reducing costs while improving resource allocation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of an operation evaluation method for the electric carbon certificate market provided in an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of an improved entropy weight method provided in an embodiment of the present application;

[0052] Figure 3 It is a structural diagram of an operation evaluation system for the electric carbon certificate market provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more. In the description of the present application, the term "including" and its variations are open inclusions, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "according to" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments".

[0055] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0056] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0057] First, see Figure 1 , shows a flow chart of an operation evaluation method for the electric carbon certificate market provided by an embodiment of the present application, the method includes steps S101-S103, which are as follows:

[0058] S101, based on the first text data related to the operation of the electricity carbon certificate market to be evaluated, determine the evaluation index and its corresponding index data through natural language processing and semantic analysis technology;

[0059] In one example, the electricity carbon certificate market to be evaluated may generally include the carbon trading market, the carbon market, and the electricity market, among which:

[0060] The carbon trading market can be the core mechanism for promoting greenhouse gas emission reduction through market-based means. Enterprises can achieve emission reduction targets through carbon emission rights trading, which can not only reduce costs but also improve resource allocation efficiency.

[0061] Carbon markets, such as provincial carbon markets, are an important supplement to the national carbon market. By establishing a regional carbon trading system, it can promote low-carbon transformation of local units and enterprises and provide experience support for the construction of the national market.

[0062] The electricity market, as an important platform to promote carbon emission reduction in the power industry, plays an important role in transmitting carbon costs, promoting clean energy consumption, adjusting the power structure, and improving users' electricity usage habits. It has the same carbon emission reduction goal as the carbon market.

[0063] It should be noted that carbon emission rights are the quota of carbon dioxide that enterprises can legally emit. They are allocated by relevant units based on emission reduction targets or obtained through market transactions. They are the core elements of the operation of the carbon market.

[0064] In one example, the number of determined evaluation indicators is multiple.

[0065] In one example, the determined evaluation indicators may include:

[0066] 1) Market structure: 1. Number of market players - the number of market players and types on both the supply and demand sides. 2. Supply-demand ratio - the ratio of market supply to demand. 3. Market concentration - the market share of the top N players in the market. 4. Demand growth rate - the ratio of market demand in a certain period to the previous period x 100%. 5. Supply growth rate - the ratio of market supply in a certain period to the previous period x 100%.

[0067] 2) Market performance: 1. Price level - average transaction price under the same scale, such as the average price per unit of carbon emissions. 2. Price volatility - standard deviation of transaction prices. 3. Transaction scale - total volume of transactions in a certain period, such as how much carbon emissions are traded throughout the year. 4. Transaction frequency - average number of transactions in a certain period. 5. Transaction efficiency - average time spent on trading X tons of carbon emissions.

[0068] 3) Market operation: 1. Market flexibility - includes three sub-indicators: number of trading products, short trading cycle, number of trading methods: the weight of the three is 1 / 3, and the weighted sum is the market flexibility. 2. Market recognition - the weights of the following scenarios are consistent, and 25 points are scored if one is met, and the total score × 100% is the market recognition. Application scenarios: clearing carbon quotas; offsetting EU tariffs; supporting the EU battery bill (carbon footprint); mutual recognition with international green certificates.

[0069] S102, based on the expert scoring data, using an improved entropy weight method to determine the indicator weight corresponding to the evaluation indicator;

[0070] In one example, the entropy weight method is an objective weighting method based on sample data to reduce subjective factors in the hierarchical analysis method. Its principle is to judge the degree of variation of the weights between the index factors at the same level according to the importance of the sample data collected in advance. The traditional entropy weight method has the problem that the entropy obtained cannot fully reflect the relative importance of the factors due to the large variation of some basic data, which affects the accuracy of the weights. The improved entropy weight method (EWM) can improve its adaptability and accuracy by introducing new elements or adjusting existing steps. These improvements can be customized for different application scenarios to better capture the complex relationships in the data and provide a more scientific and reasonable weight distribution. For example, nonlinear transformation functions (such as Logistics activation function) can be introduced, or the entropy weight method can be combined with other methods (such as hierarchical analysis method, Delphi method, etc.) to comprehensively consider both subjective and objective evaluations.

[0071] S103, based on the indicator data and the indicator weights, determining the operational evaluation results of the electricity carbon certificate market to be evaluated.

[0072] In an optional implementation, the evaluation index and its corresponding index data are determined based on the first text data related to the operation of the electricity carbon certificate market to be evaluated by natural language processing and semantic analysis technology, including:

[0073] The first text data is obtained by using crawler technology; specifically, crawler technology is an automated data collection method, which obtains carbon market-related data from public channels on the Internet through programming, thereby solving the problems of data dispersion and low collection efficiency;

[0074] Using statistical analysis, screening and correlation analysis are performed on the first text data to obtain second text data;

[0075] The evaluation index and its corresponding index data are extracted from the second text data through natural language processing and semantic analysis technology.

[0076] In related technologies, difficulties in data acquisition and analysis are common problems. The multi-dimensional indicators involved in the electricity carbon certificate market, such as market trading activity, carbon price fluctuations, policy support, etc., have scattered and highly heterogeneous data. Traditional manual or inefficient data collection methods are difficult to meet actual needs.

[0077] In this embodiment, crawler technology can be used to efficiently obtain indicator data from multiple sources, automatically identify and filter out the most relevant and representative evaluation indicators for the operation of the (provincial region) electric carbon certificate market from massive and heterogeneous network resources, and support real-time updating and automatic sorting. The crawled data is screened and analyzed for relevance using statistical analysis methods to remove redundant or irrelevant indicators to ensure the scientificity and simplicity of the final evaluation indicator system. Through natural language processing and semantic analysis technology, potential indicators are extracted from the second text data (such as policy texts and documents), and hidden indicators that are difficult to quantify but important, such as policy support and market innovation, are mined. In order to adapt to the dynamic changes in the electric carbon certificate market environment, vector space models and collaborative filtering recommendation algorithms can also be added to the screening and relevance analysis process to achieve a more accurate evaluation of the operation of the electric carbon certificate market. In this way, through natural language processing and machine learning algorithms, crawlers can understand the text context, accurately extract key information, and improve the accuracy and efficiency of indicator screening. And the vector space model and collaborative filtering recommendation algorithm are added here to achieve the screening of more accurate evaluation indicators for the operation of the electric carbon certificate market.

[0078] In an optional implementation, the method of determining the indicator weight corresponding to the evaluation indicator based on the expert scoring data by using an improved entropy weight method includes:

[0079] Constructing a corresponding indicator evaluation matrix based on the expert scoring data;

[0080] Normalizing the indicator evaluation matrix;

[0081] Determine information entropy based on the normalized index evaluation matrix;

[0082] Using a pre-configured activation function, converting the information entropy into improved entropy;

[0083] An objective weight is calculated based on the improved entropy to serve as the indicator weight.

[0084] In this embodiment, the activation function Logistics is introduced in the process of information entropy calculation, which reduces the basic data difference and more accurately calculates the weight of the evaluation index, making the weight calculation of the evaluation index more accurate and reasonable, and also improving the scientific nature of the weight distribution.

[0085] In one example, see Figure 2The expert scoring data can be obtained by inviting experts to independently score in a back-to-back manner, and the values ​​are 5-10 according to the low, lower, medium, higher, and high safety levels. When m experts are invited to independently score the system risk assessment hierarchical model with n indicators, the membership matrix (indicator evaluation matrix) M of each indicator system can be constructed as shown below:

[0086]

[0087] Where: x ij Give the j-th expert a score for the i-th indicator.

[0088] Normalize the matrix M, that is:

[0089]

[0090] Among them, the information entropy E of the security status of the jth indicator is j for:

[0091]

[0092] The information entropy E of the security status of the jth indicator j , introduce activation function for transformation, and finally obtain the improved entropy value of the jth indicator security state Right now:

[0093]

[0094] In this way, the objective weight wj of the jth indicator is:

[0095]

[0096] In an optional implementation, determining the operational evaluation result of the to-be-evaluated electricity carbon certificate market based on the indicator data and the indicator weight includes:

[0097] Based on the indicator weights, the optimal indicator combination weights are determined using game theory;

[0098] Based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator, the fuzzy evaluation theory is used to determine the target layer evaluation grade membership;

[0099] According to the target layer evaluation grade membership, a target layer risk grade evaluation result for characterizing the operation evaluation result is determined.

[0100] In the relevant technologies, most of the existing evaluation methods rely on a single evaluation model, such as subjective weight allocation method or simple statistical analysis, empirical method or subjective weighting. They lack comprehensive consideration of complex systems, objectivity and scientific basis, which makes the evaluation results susceptible to interference from human factors and difficult to fully reflect the operating status and potential problems of the electricity carbon certificate market.

[0101] In this embodiment, game theory is combined with fuzzy comprehensive evaluation to evaluate the operation of the electricity carbon certificate market, a decision-making game model of multiple participants is constructed, and the strategic interaction and equilibrium state among different stakeholders are analyzed to provide deeper decision support for the evaluation. In view of the uncertainty factors existing in the operation of the electricity carbon certificate market, fuzzy mathematics theory (fuzzy evaluation theory) is used to quantify the evaluation indicators, and by constructing fuzzy membership functions and fuzzy evaluation matrices, accurate description and comprehensive evaluation of the market operation status can be achieved.

[0102] In an optional implementation, the determining of the optimal indicator combination weights based on the indicator weights by using game theory includes:

[0103] Linearly combining the indicator weights to obtain a weight set, wherein the weight set includes multiple weight groups, and the multiple weight groups correspond to multiple preset levels;

[0104] Game theory is used to optimize the weight set to obtain the optimal indicator combination weight.

[0105] In one example, the indicator weight vector set β can be established through game theory q ={β1, β2, ..., β p}(q=1, 2, ..., p), where p is the number of weighting methods, and in this embodiment, p=2. Let a={a1, a2} be the linear combination coefficient.

[0106] 1) Construct the indicator weight set. Linearly combine the two weight vectors at each level to obtain the weight set:

[0107] 2) Find the optimal indicator combination weight. Optimize the two linear combination coefficients and obtain the optimal vector as the optimal indicator combination weight. The optimization process is:

[0108] Establish the objective function:

[0109]

[0110] The optimal conditions for the first-order derivative are obtained:

[0111]

[0112] So we get the corresponding linear equations:

[0113]

[0114] After finding (a1, a2), normalize it and finally get the index combination weights as

[0115]

[0116] In an optional implementation, the determination of the target layer evaluation grade membership using fuzzy evaluation theory based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator includes:

[0117] Constructing an initial evaluation set based on the indicator risk assignment;

[0118] Based on the indicator data and the initial evaluation set, calculating the membership degree corresponding to the evaluation indicator, and normalizing the membership degree;

[0119] Based on the optimal indicator combination weights and the normalized membership, the target indicator evaluation matrix corresponding to the evaluation indicator is determined to characterize the target layer evaluation level membership.

[0120] In one example, the indicator risk assignment corresponding to the evaluation indicator can be that the expert transfers subjective consciousness to the evaluation of the indicator and assigns risk to each indicator according to his own experience to collect the corresponding indicator risk assignment. Here, it is assumed that the initial evaluation set is: U d = {u d1 ,u d2 ,…,u dn}.

[0121] According to the set of factors to be evaluated (such as indicator data) and the initial evaluation set, the corresponding membership of each evaluation indicator is calculated, and each single factor C is evaluated separately, which is the set C d To U d The fuzzy mapping f, where f induces the fuzzy relation R f , R f The element r in nj This is the degree of membership. The process of obtaining the degree to which each indicator belongs to the risk level is as follows:

[0122] f:C d →U d

[0123]

[0124] In the formula, j is the index C dnFor the membership degree of the evaluation level j, this embodiment divides the risk level into 5 levels, that is, j=5.

[0125] Normalize the membership degree:

[0126]

[0127] The weight vector ω of the evaluation index factor (for example, it can be determined by the optimal index combination weight) is combined with the normalized matrix to obtain the evaluation grade membership of each first-level index, as shown in the following formula:

[0128]

[0129] And the indicator evaluation matrix is ​​obtained:

[0130]

[0131] And further, the first-level indicator weight vector and the first-level indicator evaluation matrix are combined to obtain the final target layer evaluation grade membership:

[0132]

[0133] Finally, the target layer risk level evaluation results are obtained, as shown in Table 1.

[0134] Table 1

[0135]

[0136] Among them, r(u) is the evaluation result when the membership function is normally distributed, r * (u) is the evaluation result when the membership function is a triangular distribution.

[0137] On the second aspect, accordingly, the embodiments of the present application also provide an operation evaluation system for the electricity carbon certificate market, which can implement all processes of the operation evaluation method for the electricity carbon certificate market provided in the above embodiments.

[0138] See also Figure 3 , shows a schematic diagram of the structure of an operation evaluation system for the electric carbon certificate market provided in an embodiment of the present application, the operation evaluation system for the electric carbon certificate market comprises:

[0139] The indicator mining module 301 is used to determine the evaluation indicator and its corresponding indicator data based on the first text data related to the operation of the electric carbon certificate market to be evaluated through natural language processing and semantic analysis technology;

[0140] A weight determination module 302 is used to determine the indicator weight corresponding to the evaluation indicator by using an improved entropy weight method based on the expert scoring data;

[0141] The evaluation module 303 is used to determine the operational evaluation result of the electricity carbon certificate market to be evaluated based on the indicator data and the indicator weights.

[0142] In an optional implementation, the evaluation index and its corresponding index data are determined based on the first text data related to the operation of the electricity carbon certificate market to be evaluated by natural language processing and semantic analysis technology, including:

[0143] Acquire the first text data by using crawler technology;

[0144] Using statistical analysis, the text data is screened and analyzed for relevance to obtain second text data;

[0145] The evaluation index and its corresponding index data are extracted from the second text data through natural language processing and semantic analysis technology.

[0146] In an optional implementation, the method of determining the indicator weight corresponding to the evaluation indicator based on the expert scoring data by using an improved entropy weight method includes:

[0147] Constructing a corresponding indicator evaluation matrix based on the expert scoring data;

[0148] Normalizing the indicator evaluation matrix;

[0149] Determine information entropy based on the normalized index evaluation matrix;

[0150] Using a pre-configured activation function, converting the information entropy into improved entropy;

[0151] An objective weight is calculated based on the improved entropy to serve as the indicator weight.

[0152] In an optional implementation, determining the operational evaluation result of the to-be-evaluated electricity carbon certificate market based on the indicator data and the indicator weight includes:

[0153] Based on the indicator weights, the optimal indicator combination weights are determined using game theory;

[0154] Based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator, the fuzzy evaluation theory is used to determine the target layer evaluation grade membership;

[0155] According to the target layer evaluation grade membership, a target layer risk grade evaluation result for characterizing the operation evaluation result is determined.

[0156] In an optional implementation, the determining of the optimal indicator combination weights based on the indicator weights by using game theory includes:

[0157] Linearly combining the indicator weights to obtain a weight set, wherein the weight set includes multiple weight groups, and the multiple weight groups correspond to multiple preset levels;

[0158] Game theory is used to optimize the weight set to obtain the optimal indicator combination weight.

[0159] In an optional implementation, the determination of the target layer evaluation grade membership using fuzzy evaluation theory based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator includes:

[0160] Constructing an initial evaluation set based on the indicator risk assignment;

[0161] Based on the indicator data and the initial evaluation set, calculating the membership degree corresponding to the evaluation indicator, and normalizing the membership degree;

[0162] Based on the optimal indicator combination weights and the normalized membership, the target indicator evaluation matrix corresponding to the evaluation indicator is determined to characterize the target layer evaluation level membership.

[0163] In summary, the embodiments of the present application have at least the following beneficial effects:

[0164] By adopting the embodiment of the present application, based on the first text data related to the operation of the electricity carbon certificate market to be evaluated, the evaluation index and its corresponding index data are determined by natural language processing and semantic analysis technology; based on the expert scoring data, the improved entropy weight method is used to determine the index weight corresponding to the evaluation index; based on the index data and the index weight, the operation evaluation result of the electricity carbon certificate market to be evaluated is determined, so that important evaluation indicators and their corresponding index data can be automatically mined from the relevant data through natural language processing and semantic analysis technology without manual analysis, and the trading system related to the operation of the electricity carbon certificate market can be adjusted in a timely and accurate manner, thereby reducing the energy waste generated during the dispatching and operation of the power system, improving the energy utilization rate during the dispatching and operation of the power system, reducing costs while improving resource allocation efficiency.

[0165] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary hardware platform, and of course it can also be implemented entirely by hardware. Based on such an understanding, all or part of the contribution of the technical solution of the present application to the background technology can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0166] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. An operational evaluation method for the electric carbon certificate market, characterized in that: include: Based on the first text data related to the operation of the electricity carbon certificate market to be evaluated, the evaluation index and its corresponding index data are determined through natural language processing and semantic analysis technology; Based on the expert scoring data, the improved entropy weight method is used to determine the indicator weights corresponding to the evaluation indicators; Based on the indicator data and the indicator weights, the operational evaluation results of the electricity carbon certificate market to be evaluated are determined.

2. The method according to claim 1, characterized in that The method of determining the evaluation index and its corresponding index data based on the first text data related to the operation of the electricity carbon certificate market to be evaluated by natural language processing and semantic analysis technology includes: Acquire the first text data by using crawler technology; Using statistical analysis, screening and correlation analysis are performed on the first text data to obtain second text data; The evaluation index and its corresponding index data are extracted from the second text data through natural language processing and semantic analysis technology.

3. The method according to claim 1, characterized in that The method of determining the indicator weights corresponding to the evaluation indicators based on the expert scoring data by using the improved entropy weight method includes: Constructing a corresponding indicator evaluation matrix based on the expert scoring data; Normalizing the indicator evaluation matrix; Determine information entropy based on the normalized index evaluation matrix; Using a pre-configured activation function, converting the information entropy into improved entropy; An objective weight is calculated based on the improved entropy to serve as the indicator weight.

4. The method according to claim 1, characterized in that: Determining the operational evaluation result of the to-be-evaluated electricity carbon certificate market based on the indicator data and the indicator weights includes: Based on the indicator weights, the optimal indicator combination weights are determined using game theory; Based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator, the fuzzy evaluation theory is used to determine the target layer evaluation grade membership; According to the target layer evaluation grade membership, a target layer risk grade evaluation result for characterizing the operation evaluation result is determined.

5. The method according to claim 4, characterized in that Based on the indicator weights, the optimal indicator combination weights are determined using game theory, including: Linearly combining the indicator weights to obtain a weight set, wherein the weight set includes multiple weight groups, and the multiple weight groups correspond to multiple preset levels; Game theory is used to optimize the weight set to obtain the optimal indicator combination weight.

6. The method according to claim 4, characterized in that The method of determining the target layer evaluation grade membership by using fuzzy evaluation theory based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator includes: Constructing an initial evaluation set based on the indicator risk assignment; Based on the indicator data and the initial evaluation set, calculating the membership degree corresponding to the evaluation indicator, and normalizing the membership degree; Based on the optimal indicator combination weights and the normalized membership, the target indicator evaluation matrix corresponding to the evaluation indicator is determined to characterize the target layer evaluation level membership.

7. An operation evaluation system for the electric carbon certificate market, characterized in that: include: An indicator mining module, used to determine the evaluation indicator and its corresponding indicator data based on the first text data related to the operation of the electric carbon certificate market to be evaluated through natural language processing and semantic analysis technology; A weight determination module is used to determine the indicator weight corresponding to the evaluation indicator based on the expert scoring data by using an improved entropy weight method; An evaluation module is used to determine the operational evaluation result of the electric carbon certificate market to be evaluated based on the indicator data and the indicator weights.

8. The system according to claim 7, characterized in that The method of determining the evaluation index and its corresponding index data based on the first text data related to the operation of the electricity carbon certificate market to be evaluated by natural language processing and semantic analysis technology includes: Acquire the first text data by using crawler technology; Using statistical analysis, the text data is screened and analyzed for relevance to obtain second text data; The evaluation index and its corresponding index data are extracted from the second text data through natural language processing and semantic analysis technology.

9. The system according to claim 7, characterized in that The method of determining the indicator weights corresponding to the evaluation indicators based on the expert scoring data by using the improved entropy weight method includes: Constructing a corresponding indicator evaluation matrix based on the expert scoring data; Normalizing the indicator evaluation matrix; Determine information entropy based on the normalized index evaluation matrix; Using a pre-configured activation function, converting the information entropy into improved entropy; An objective weight is calculated based on the improved entropy to serve as the indicator weight.

10. The system according to claim 7, characterized in that Determining the operational evaluation result of the to-be-evaluated electricity carbon certificate market based on the indicator data and the indicator weights includes: Based on the indicator weights, the optimal indicator combination weights are determined using game theory; Based on the indicator data, the optimal indicator combination weight and the indicator risk assignment corresponding to the evaluation indicator, the fuzzy evaluation theory is used to determine the target layer evaluation grade membership; According to the target layer evaluation grade membership, a target layer risk grade evaluation result for characterizing the operation evaluation result is determined.

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