Water and electricity delivery benefit analysis method and analysis system based on time sequence information entropy

Abstract

The invention discloses a time sequence information entropy-based hydropower delivery benefit analysis method and system, and relates to the technical field of digital economic models. Comprising the following steps that S1, a three-level index system of the water and electricity delivery benefits is constructed, and the three-level index system comprises a first-level index, a second-level index and a third-level index; s2, determining the weight of each third-level index according to the information entropy of the third-level indexes; s3, calculating a corresponding second-level index reference value according to the weight and the reference value of each third-level index, and constructing a hydroelectric delivery static benefit analysis model according to the second-level index reference value and the weight thereof; s4, constructing a hydroelectric delivery dynamic benefit analysis model; and S5, based on the dynamic benefit analysis model, introducing a time sequence information entropy, and constructing a hydroelectric delivery combination benefit analysis model. The method can comprehensively, objectively and accurately analyze the benefits of the water and electricity delivery project, improves the utilization efficiency and delivery benefits of the water and electricity resources, and facilitates the promotion of sustainable development and utilization of the water and electricity resources.

Description

Technical Field

[0001] This application relates to the technical field of digital economic models, and in particular to a method and system for analyzing the benefits of hydropower transmission based on time series information entropy. Background Art

[0002] Early analysis of the benefits of hydropower transmission mainly focused on the technical feasibility of the project itself and direct economic benefits. The research methods were relatively simple and usually only considered the direct costs and benefits of project construction and operation. With the development of technology and economy, the single-dimensional evaluation method has gradually been phased out, and benefit analysis has begun to cover a wider range of dimensions, such as social benefits and environmental benefits. In the existing literature, modern researchers have started to construct more systematic indicator systems to comprehensively evaluate the benefits of hydropower transmission projects in different aspects. For example, by introducing the multi-level fuzzy comprehensive evaluation method, production function model, etc., to more comprehensively evaluate the benefits of hydropower transmission projects.

[0003] However, most of the existing benefit analysis models are static models and cannot reflect the dynamic characteristics of the benefits of hydropower transmission changing over time, which limits the accurate evaluation of the long-term benefits of the project by decision-makers. Secondly, when determining the indicator weights, some methods rely too much on the subjective judgment of experts and lack objectivity. When dealing with the time series data of the benefits of hydropower transmission, the complexity and uncertainty of the data are often not fully considered. In addition, some of the existing models lack a dynamic response and adjustment mechanism to external environmental changes and cannot adapt to the rapid changes in policies, markets, and technologies. Some studies lack sufficient empirical analysis, and the effectiveness and practicality of the models have not been fully verified. Summary of the Invention

[0004] Based on the limitations of single-dimensional evaluation, static analysis, etc. existing in the prior art, as well as problems such as the lack of a systematic indicator system, lack of objectivity, neglect of the complexity of time series, lack of a dynamic adjustment mechanism, and insufficient empirical analysis, this application proposes a method and system for analyzing the benefits of hydropower transmission based on time series information entropy, aiming to provide scientific and objective decision-making support for decision-makers, which can significantly promote the optimization of hydropower transmission projects. Through in-depth analysis and evaluation, it promotes the planning and optimization of hydropower transmission projects, improves the utilization efficiency of hydropower resources and the benefits of transmission, and helps to promote the sustainable development and utilization of hydropower resources.

[0005] A method for analyzing the benefits of hydropower transmission based on time series information entropy described in this application includes the following steps:

[0006] S1, constructing a three-level indicator system for the benefits of hydropower transmission, where the three-level indicator system includes first-level indicators, second-level indicators, and third-level indicators; the first-level indicator includes the total benefits of hydropower transmission; the first-level indicator is obtained based on the weight distribution of the second-level indicator parameters, and the second-level indicator is obtained based on the weight distribution of the third-level indicator parameters;

[0007] S2. Determine the weights of each third-level indicator according to the information entropy of the third-level indicator;

[0008] S3. Calculate the corresponding second-level indicator reference values according to the weights and reference values of each third-level indicator, and construct a static benefit analysis model for hydropower transmission based on the second-level indicator reference values and their weights;

[0009] S4. Construct a dynamic benefit analysis model for hydropower transmission;

[0010] S5. Based on the dynamic benefit analysis model, introduce the time series information entropy to construct a combined benefit analysis model for hydropower transmission.

[0011] Optionally, the second-level indicators of the total benefits of hydropower transmission at least include enterprise development, resource ecology, and social economy. The third-level indicators of enterprise development at least include external transmission value benefits, production improvement benefits, and external transmission hydropower market competitiveness; the third-level indicators of resource ecology at least include ecological value benefits, carbon dioxide emission reduction benefits, and air pollutant emission reduction benefits; the third-level indicators of social economy at least include hydropower scale benefits, economic promotion benefits, and social development benefits.

[0012] In an implementation manner, step S2 includes the following contents:

[0013] S21. Define the initial data set of the j-th third-level indicator corresponding to the ζ-th second-level indicator in the i-th hydropower transmission channel as where k represents the total number of second-level indicators, m represents the total number of hydropower transmission channels included in the benefit analysis model, n ζ represents the total number of third-level indicators corresponding to the ζ-th second-level indicator, represents the j-th third-level indicator corresponding to the ζ-th second-level indicator in the i-th hydropower transmission channel, and the superscript 0 represents the initial value label;

[0014] S22. Perform positive normalization processing on the initial data set, and the processing formula is:

[0015]

[0016] where, represents the reference value of the j-th third-level indicator corresponding to the ζ-th second-level indicator in the initial data set, and x ζ,ij represents the reference value of the third-level indicator after positive normalization processing;

[0017] S23. Obtain the standardized data set {x ζ,i×j |ζ = 1, 2,..., k; i = 1, 2,..., m; j = 1, 2,..., n ζ, calculate the information entropy H of the third-level indicators based on the standardized dataset ζ,j , and the calculation formula is:

[0018]

[0019] where y ζ,ij represents the P value of the j-th third-level indicator of the ζ-th secondary indicator in the i-th hydropower transmission channel; H ζ,j represents the information entropy of the j-th third-level indicator corresponding to the ζ-th secondary indicator;

[0020] S24. Based on the information entropy H obtained in step S23 ζ,j , construct the weight function of the third-level indicators:

[0021]

[0022] where ω ζ,j represents the weight of the j-th third-level indicator corresponding to the ζ-th secondary indicator.

[0023] In one implementation, step S3 includes the following contents:

[0024] S31. Calculate the reference value of the corresponding secondary indicator by using the reference value of the third-level indicator and its weight. The calculation formula is:

[0025]

[0026] where Z ζ,i represents the reference value of the ζ-th secondary indicator in the i-th hydropower transmission channel;

[0027] S32. Construct a static analysis model of hydropower transmission benefits. The function formula of the model is:

[0028]

[0029] where Z i represents the reference value of the total hydropower transmission benefits of the i-th hydropower transmission channel; ω ζ represents the weight of the ζ-th secondary indicator; preferably, the analytic hierarchy process is used to determine the weight ω ζ of the secondary indicator.

[0030] In one implementation, step S4 includes the following contents:

[0031] For the reference value x ζ,ij of the third-level indicator, use time series for mapping to obtain the function x ζ,ij (t) of the reference value of the third-level indicator with respect to the year t, and then obtain the functions of the reference value of the ζ-th secondary indicator and the total hydropower transmission benefits with respect to the year t. The function formulas are:

[0032]

[0033] Among them, Z ζ,i (t) represents the reference value of the ζ-th secondary index in the i-th hydropower transmission channel in year t; Z i (t) represents the reference value of the total hydropower transmission benefit of the i-th hydropower transmission channel in year t.

[0034] In one implementation, step S5 includes the following content:

[0035] S51, define the time-series weighted vector as:

[0036] δ = (δ 1 , δ 2 ,..., δ t ,..., δ T );

[0037] Among them, δ t ∈[0, 1], T represents the total number of evaluation years;

[0038] S52, define the time-series information entropy S as:

[0039]

[0040] S53, define the time degree θ as:

[0041]

[0042] S54, taking the maximum of the time-series information entropy S as the objective function and the conditions satisfied by the time degree and the time-series weighted vector as the constraint conditions, construct a mathematical model:

[0043]

[0044] S55, use the interior point method to solve the above objective function to obtain the time-series weighted vector, and construct a hydropower transmission benefit combination analysis model. The function expression is as follows:

[0045]

[0046] Among them, Z * is the reference value after the analysis of the hydropower transmission combination benefit.

[0047] In one implementation, the hydropower transmission benefit analysis method based on time-series information entropy further includes the step of optimizing the hydropower transmission dynamic benefit analysis model.

[0048] An analysis system for performing the hydropower transmission benefit analysis method based on time-series information entropy includes:

[0049] The first processing unit is used to construct a three - level index system for the benefits of hydropower transmission. The three - level index system includes first - level indicators, second - level indicators, and third - level indicators. The first - level indicators are obtained based on the weight distribution of the second - level index parameters, and the second - level indicators are obtained based on the weight distribution of the third - level index parameters. The first - level indicators include the total benefits of hydropower transmission.

[0050] The second processing unit is used to calculate the weights of the third - level indicators and calculate the reference values of the second - level indicators according to the reference values of the third - level indicators and their weights.

[0051] The third processing unit constructs a static benefit analysis model for hydropower transmission based on the reference values of the second - level indicators and their weights.

[0052] The fourth processing unit is used to construct a dynamic benefit analysis model for hydropower transmission.

[0053] The fifth processing unit constructs a combined benefit analysis model for hydropower transmission by introducing the time - series information entropy based on the dynamic benefit analysis model.

[0054] Compared with the prior art, the present application has the following beneficial effects:

[0055] 1. The present application provides a systematic comprehensive evaluation model by constructing a multi - level index system, which can analyze the benefits of hydropower transmission projects in multiple dimensions and comprehensively, and helps decision - makers fully understand and evaluate the overall benefits of hydropower transmission projects.

[0056] 2. The present application uses the information entropy theory to determine the index weights, reduces the influence of subjective judgment, improves the objectivity and accuracy of the model, combines the analytic hierarchy process, improves the scientificity of the analysis. Further, by introducing time - series analysis, the model can dynamically capture the changes in the benefits of hydropower transmission over time, improving the accuracy and objectivity of the model when dealing with time - series data. The application of time - series information entropy enables the model to more precisely analyze the benefits of hydropower transmission when considering the complexity of the impact of time factors on benefits.

[0057] 3. The present application conducts analysis based on actual data, ensuring the reliability and practicality of the analysis results. Further, through case analysis, the effectiveness of the model is verified, and the benefits of different hydropower transmission lines are evaluated, providing a reference for the planning and optimization of actual hydropower transmission projects.

[0058] 4. The present application can adapt to hydropower transmission projects in different regions and different time periods, and has good adaptability and generalization ability. Brief Description of the Drawings

[0059] Figure 1 is a flowchart of a method for analyzing the benefits of hydropower transmission based on time - series information entropy of the present application;

[0060] Figure 2 It is a flowchart of a method for analyzing the benefits of hydropower transmission based on temporal information entropy provided by an embodiment of the present application;

[0061] Figure 3 It is a dynamic benefit graph of hydropower transmission during 2010 - 2019 provided by an embodiment of the present application;

[0062] Figure 4 It is a combined benefit graph of hydropower transmission during 2010 - 2019 provided by an embodiment of the present application. Specific Embodiments

[0063] The present application will be further described below with reference to the accompanying drawings.

[0064] As Figure 1 shown, a method for analyzing the benefits of hydropower transmission based on temporal information entropy provided by an embodiment of the present application includes the following:

[0065] S1. According to the requirements of hydropower transmission benefit indicators, construct a three - level index system architecture with a subordinate relationship. The system architecture includes first - level indicators, second - level indicators, and third - level indicators; among them, the first - level indicators at least include the total benefit of hydropower transmission; the first - level indicators are obtained based on the weight distribution of the second - level indicator parameters, and the second - level indicators are obtained based on the weight distribution of the third - level indicator parameters; optionally, the second - level indicators of the total benefit of hydropower transmission at least include enterprise development, resource ecology, and social economy. Further, the third - level indicators corresponding to the enterprise development at least include the value benefit of transmission, the production improvement benefit, and the market competitiveness of hydropower for transmission; the third - level indicators of the resource ecology at least include the ecological value benefit, the carbon dioxide emission reduction benefit, and the air pollutant emission reduction benefit; the third - level indicators of the social economy at least include the hydropower scale benefit, the economic promotion benefit, and the social development benefit;

[0066] S2. For the constructed three - level index system, use the information entropy of the third - level indicators to determine the weights of each third - level indicator;

[0067] S3. Calculate the corresponding second - level indicator reference values using the third - level indicator reference values and their weights, and construct a static benefit analysis model for hydropower transmission according to the second - level indicator reference values and their weights;

[0068] S4. Construct a dynamic benefit analysis model for hydropower transmission;

[0069] S5. Based on the dynamic benefit analysis model, introduce temporal information entropy to construct a combined benefit analysis model for hydropower transmission.

[0070] In an alternative embodiment, step S2 includes the following:

[0071] S21. Define the initial data set of the \(j\)-th third-level index corresponding to the \(i\)-th hydropower transmission channel for the \(\zeta\)-th second-level index as where \(k\) represents the total number of second-level indicators, \(m\) represents the total number of hydropower transmission channels included in the benefit analysis model, and \(n\) ζ represents the total number of third-level indicators corresponding to the \(\zeta\)-th second-level indicator, represents the \(j\)-th third-level index corresponding to the \(\zeta\)-th second-level indicator in the \(i\)-th hydropower transmission channel, and the superscript \(0\) represents the initial value label;

[0072] Each data in the initial data set corresponds to the reference value before positive normalization of the third-level index, such as expert scores, total amounts, carbon dioxide emission reduction amounts, transmitted power, etc.;

[0073] S22. Perform positive normalization processing on the initial data set, and the processing formula is:

[0074]

[0075] where represents the reference value of the \(j\)-th third-level index corresponding to the \(\zeta\)-th second-level indicator in the \(i\)-th hydropower transmission channel in the initial data set, and \(x\) ζ,ij represents the reference value of the third-level index after positive normalization processing;

[0076] S23. Obtain the standardized data set \(\{x ζ,i×j |\zeta = 1, 2, \ldots, k; i = 1, 2, \ldots, m; j = 1, 2, \ldots, n ζ \}\), and calculate the information entropy \(H\) of the third-level index based on the standardized data set ζ,j , and the calculation formula is:

[0077]

[0078] where \(y\) ζ,ij represents the \(P\) value of the \(j\)-th third-level index in the \(i\)-th hydropower transmission channel for the \(\zeta\)-th second-level indicator; \(H\) ζ,j represents the information entropy of the \(j\)-th third-level index corresponding to the \(\zeta\)-th second-level indicator;

[0079] S24. Based on the information entropy \(H\) obtained in step S23 ζ,j , construct the weight function of the third-level index:

[0080]

[0081] where \(\omega\) ζ,j represents the weight of the \(j\)-th third-level index corresponding to the \(\zeta\)-th second-level indicator.

[0082] In an alternative embodiment, step S3 includes the following contents:

[0083] S31. Calculate the reference value of the corresponding secondary index by using the reference value of the tertiary index and its weight. The calculation formula is as follows:

[0084]

[0085] Among them, Z ζ,i represents the reference value of the ζ-th secondary index in the i-th hydropower transmission channel;

[0086] S32. Construct a static analysis model for the hydropower transmission benefit. The function formula of the model is as follows:

[0087]

[0088] Among them, Z i represents the reference value of the total hydropower transmission benefit of the i-th hydropower transmission channel; ω ζ represents the weight of the ζ-th secondary index, which can be determined by the analytic hierarchy process.

[0089] In an alternative embodiment, step S4 includes the following content:

[0090] For the reference value x ζ,ij of the tertiary index, use time series for mapping to obtain the function x ζ,ij (t) of the reference value of the tertiary index with respect to the year t, and then obtain the functions of the reference value of the ζ-th secondary index and the total hydropower transmission benefit with respect to the year t. The function formulas are as follows:

[0091]

[0092] Among them, Z ζ,i (t) represents the reference value of the ζ-th secondary index in the i-th hydropower transmission channel at the year t; Z i (t) represents the reference value of the total hydropower transmission benefit of the i-th hydropower transmission channel at the year t;

[0093] In an alternative embodiment, step S5 includes the following content:

[0094] The time series weighted vector is represented by δ = (δ 1 , δ 2 , …, δ t , …, δ T ), where T represents the total number of evaluation years, and the time series range [1, T] consists of several time nodes; δ t satisfies

[0095] The time series information entropy is expressed as:

[0096]

[0097] Let \(S\) represent the amount of information contained in the time - series weighted vector \(\delta\). The larger \(S\) is, the greater the degree of uncertainty of \(\delta\). By introducing the concept of "time degree", the influence degrees of data in recent years and early data on the comprehensive evaluation result are represented.

[0098] This application uses the time degree \(\theta\) to reflect the influence degrees of several adjacent time nodes and several distant time nodes on the combined benefit analysis result. The calculation formula of the time degree is:

[0099]

[0100] The larger the time degree is, the smaller the influence of recent time nodes on the benefit analysis result is, and the greater the influence of several distant time nodes on the benefit analysis result is, and vice versa.

[0101] Construct a mathematical model based on the objective function and constraint conditions:

[0102]

[0103] The value of the time degree \(\theta\) is determined according to the objective situation of the data set within the time - node sequence. Substitute the time degree into the above formula and use the interior - point method to solve the above formula to obtain the time - series weighted vector, thereby constructing a combined analysis model for the hydropower transmission benefit, as follows:

[0104]

[0105] where \(Z\) * is the reference value after the combined analysis of the hydropower transmission combined benefit.

[0106] Furthermore, the method for analyzing the hydropower transmission benefit based on the time - series information entropy further includes:

[0107] S6. Optimize the dynamic benefit analysis model for hydropower transmission and output the optimized dynamic benefit analysis model for hydropower transmission; specifically, the dynamic benefit analysis model for hydropower transmission can be verified by introducing actual examples, and the loss function is used for model optimization.

[0108] In a second aspect, this application provides a system for analyzing the hydropower transmission benefit based on the time - series information entropy, including:

[0109] The first processing unit is used to construct a three - level index system for the hydropower transmission benefit. The three - level index system includes first - level indexes, second - level indexes, and third - level indexes; the first - level indexes are obtained based on the weight distribution of the second - level index parameters, and the second - level indexes are obtained based on the weight distribution of the third - level index parameters; the first - level indexes include the total hydropower transmission benefit.

[0110] The second processing unit is used to calculate the weights of the third - level indexes and calculate the reference values of the second - level indexes according to the reference values of the third - level indexes and their weights.

[0111] The third processing unit constructs a static benefit analysis model for hydropower transmission based on the secondary index reference value and its weight;

[0112] The fourth processing unit is used to construct a dynamic benefit analysis model for hydropower transmission;

[0113] The fifth processing unit constructs a combined benefit analysis model for hydropower transmission based on the dynamic benefit analysis model and introducing the temporal information entropy.

[0114] It should be understood that the division of each processing unit in the above system is only a division of logical functions. In actual implementation, it can be fully or partially integrated into a physical entity, or physically separated. In addition, the processing units in the system can be implemented in the form of a processor calling software; for example, the system includes a processor, the processor is connected to a memory, instructions are stored in the memory, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of each processing unit of the system, where the processor is a general-purpose processor, such as a central processing unit or a microprocessor, and the memory is a memory inside or outside the system.

[0115] As Figures 2 - 4 shown is an example of the benefit analysis of hydropower transmission in Sichuan Province by an enterprise from 2010 to 2019 in 10 years, and the feasibility and effectiveness of this application are illustrated through this embodiment.

[0116] I. Construction of the index system.

[0117] Taking the total benefit of hydropower transmission as the primary index, 3 secondary indexes such as enterprise development are selected (or newly defined), and 9 tertiary indexes such as the value benefit of transmission are selected to construct the tertiary index system as shown in Table 1 below:

[0118] Table 1 Tertiary index system

[0119]

[0120] The primary index, secondary index, and tertiary index have a subordinate relationship of progressive succession. In the process of determining the primary index "total benefit of hydropower transmission", based on the reference value and weight of the tertiary index, the reference value of the secondary index is deduced, and then the reference value of the primary index is deduced from the reference value and weight of the secondary index.

[0121] II. Determination of index weights.

[0122] In this embodiment, the weights of the secondary indicators (enterprise development benefits, ecological resource benefits, and social and economic benefits) are determined through hierarchical analysis; while the weights of the tertiary indicators (external delivery value benefits, production improvement benefits, external delivery hydropower market competitiveness, ecological value benefits, carbon dioxide emission reduction benefits, air pollutant emission reduction benefits, hydropower scale benefits, economic promotion benefits, and social development benefits) are determined by the information entropy existing between the data, that is, the greater the information entropy existing between the data under this indicator, the smaller the amount of information contained in this indicator, and the weight should also be correspondingly reduced, and vice versa.

[0123] First, construct an initial data set, denoted as Among them, the total number of hydropower external delivery channels is 10, the number of tertiary indicators corresponding to each secondary indicator is 3, and the multiplication sign × is a mathematical expression form. represents the reference value of the jth tertiary indicator corresponding to the ζth secondary indicator, and this value is set based on the actual year. Since there may be differences in dimensions between the data under each indicator, it is necessary to standardize it. Since at the beginning of establishing the indicator system, all indicators have been deliberately controlled as positive indicators, only the tertiary indicators need to be positively standardized, and the calculation formula is as follows:

[0124]

[0125] This implementation is represented by a matrix. After the indicators are standardized, a standardized data set {x ζ,i×j |ζ = 1, 2, 3; i = 1, 2,..., 10; j = 1, 2,..., 3} is obtained;

[0126] Then, calculate the information entropy of the tertiary indicators, and the calculation formula is as follows:

[0127]

[0128] Determine the weights of each tertiary indicator:

[0129]

[0130] Calculate the reference value of the secondary indicator based on the reference value of the tertiary indicator and its weight:

[0131]

[0132] Z ζ,i represents the reference value of the ζth secondary indicator in the ith hydropower external delivery channel; it should be noted that a certain tertiary indicator of all external delivery channels shares the same weight ω ζ,j .

[0133] In this embodiment, the analytic hierarchy process is used to determine the weight ω of the ζth secondary indicator ζ. The process of determining weights using the Analytic Hierarchy Process mainly includes the following steps:

[0134] (1) Establish a hierarchical structure model;

[0135] (2) Construct a judgment matrix;

[0136] (3) Calculate the weight vector and conduct a consistency test;

[0137] The Analytic Hierarchy Process is a conventional method for determining weights in this field, and the specific process will not be elaborated here.

[0138] Through the above process, the weights of the secondary indicators and tertiary indicators in this embodiment are determined, as shown in Table 2.

[0139] Table 2 Indicator Weights

[0140]

[0141] III. Benefit analysis.

[0142] Construct a static analysis model of the hydropower transmission benefit based on the reference values of the secondary indicators and their weights:

[0143]

[0144] Z i represents the reference value of the total hydropower transmission benefit of the i-th hydropower transmission channel; ω ζ represents the weight of the ζ-th secondary indicator, which is determined by the Analytic Hierarchy Process.

[0145] Based on the static analysis model, map the reference values of the tertiary indicators at a single time node according to the time series to make them a function of time. At this time, the reference value Z ζ,i of the secondary indicator and the reference value Z i of the total hydropower transmission benefit also become functions of the time year:

[0146]

[0147] (1) Dynamic benefit analysis:

[0148] Substitute the collected data of each indicator into the dynamic benefit analysis model established in this application, and conduct dynamic benefit analysis on the 4 main hydropower transmission lines (Fufeng DC, JinSu DC, BinJin DC, and Sichuan-Chongqing DC) in Sichuan Province from 2010 to 2019 for 10 years. The dynamic benefit analysis results of the 4 lines are as Figure 3 shown.

[0149] It can be seen that the largest gap in the benefits of the four lines occurred in 2013. After several changes in ranking, the ranking of the benefits of the four lines returned to that of 10 years ago, but the gap between them has narrowed. The Sichuan-Chongqing DC and the Fufeng-Chongqing DC are in an advantageous position in terms of both enterprise development and social economy benefits, but they are relatively disadvantaged in terms of resource and ecological benefits. The reason is that the average carbon dioxide emission factor of the power grid in Chongqing is small and the waste gas emission reduction measures in Shanghai are good.

[0150] (2) Combined benefit analysis:

[0151] Construct a mathematical model based on the objective function and constraints:

[0152]

[0153] Let the time degree θ = 0.3 and substitute it into the above formula to solve, and the time series weighted vector is shown in Table 3:

[0154] Table 3 Time series weighted vector

[0155] Year δ 2010 0.0389 2011 0.0467 2012 0.0559 2013 0.0669 2014 0.0802 2015 0.096 2016 0.115 2017 0.1378 2018 0.165 2019 0.197

[0156] Perform linear weighted aggregation on the dynamic benefit analysis results of the four power transmission lines from 2010 to 2019, and the combined benefit analysis results of hydropower transmission are as Figure 4 shown. It can be seen that in the combined benefit analysis model, the Fufeng-Chongqing DC has good performance in the benefits of each sub-aspect. Among them, the social development benefit, ecological value benefit and power transmission value benefit are the most obvious. The Sichuan-Chongqing DC is the most prominent in terms of production improvement benefit and economic promotion benefit. Although the carbon dioxide emission reduction benefit of the Jinsu DC is the best among the four power transmission channels, the benefits in other aspects are not ideal compared with the other three power transmission channels. In other aspects, the Binjin DC is slightly better than the Sichuan-Chongqing DC, but mostly not as good as the Fufeng-Chongqing DC and the Sichuan-Chongqing DC.

[0157] The total benefit of hydropower transmission is consistent with the radar chart on the left. From large to small, they are the Fufeng-Chongqing DC (0.033), the Sichuan-Chongqing DC (0.032), the Binjin DC (0.025) and the Jinsu DC (0.023), which also verifies the effectiveness of the secondary index weighting model.

[0158] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. The method for analyzing the benefits of hydropower transmission based on time series information entropy is characterized by: The following steps are involved: S1, constructing a three-level indicator system for the benefits of hydropower transmission, wherein the three-level indicator system includes a primary indicator, a secondary indicator and a tertiary indicator; the primary indicator includes the total benefits of hydropower transmission; the primary indicator is obtained based on the weight distribution of the secondary indicator parameters, and the secondary indicator is obtained based on the weight distribution of the tertiary indicator parameters; S2, determining the weight of each third-level indicator according to the information entropy of the third-level indicators; S3, calculating the corresponding secondary indicator reference value according to the weight and reference value of each tertiary indicator, and constructing a hydropower transmission static benefit analysis model according to the secondary indicator reference value and its weight; S4, construct a dynamic benefit analysis model for hydropower transmission; S5, based on the dynamic benefit analysis model, introduce time series information entropy to build a combined benefit analysis model for hydropower transmission.

2. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 1, characterized in that: The secondary indicators of the total benefits of hydropower transmission include at least enterprise development, resource ecology, and social economy.

3. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 2 is characterized in that: The three-level indicators of enterprise development include at least the benefits of exported value, production improvement benefits, and market competitiveness of exported hydropower; the three-level indicators of resource ecology include at least the benefits of ecological value, carbon dioxide emission reduction benefits, and air pollutant emission reduction benefits; the three-level indicators of social economy include at least the benefits of hydropower scale, economic promotion benefits, and social development benefits.

4. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 1, characterized in that: Step S2 includes the following contents: S21, define the initial data set of the jth third-level indicator corresponding to the ζth second-level indicator in the ith hydropower transmission channel as Among them, k represents the total number of secondary indicators, m represents the total number of hydropower transmission channels included in the benefit analysis model, and n ζ represents the total number of third-level indicators corresponding to the ζ-th second-level indicator, It represents the jth third-level indicator corresponding to the ζth second-level indicator in the ith hydropower transmission channel, and the superscript 0 indicates the initial value label; S22, forward normalization processing is performed on the initial data set, and the processing formula is: in, represents the reference value of the jth third-level indicator corresponding to the ζth second-level indicator in the i-th hydropower transmission channel in the initial data set, x ζ,ij It represents the reference value of the third-level index after positive normalization; S23, obtain the standardized data set {x ζ,i×j |ζ=1,2,…,k; i=1,2,…,m; j=1,2,…,n ζ }, calculate the information entropy H of the three-level indicators based on the standardized data set ζ,j , the calculation formula is: Among them, y ζ,ij represents the P value of the jth tertiary indicator of the ζth secondary indicator in the ith hydropower transmission channel; H ζ,j represents the information entropy of the jth third-level indicator corresponding to the ζth second-level indicator; S24, based on the information entropy H obtained in step S23 ζ,j , construct the weight function of the three-level indicators: Among them, ω ζ,j It represents the weight of the jth third-level indicator corresponding to the ζth second-level indicator.

5. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 4 is characterized in that: Step S3 includes the following contents: S31, use the third-level indicator reference value and its weight to calculate the corresponding second-level indicator reference value, the calculation formula is: Among them, Z ζ,i represents the reference value of the ζ-th secondary indicator in the ith hydropower transmission channel; S32, construct a static analysis model of hydropower transmission benefits, the function formula of the model is: Among them, Z i represents the total benefit reference value of hydropower transmission of the ith hydropower transmission channel; ω ζ Represents the weight of the ζ-th secondary indicator.

6. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 5, characterized in that: In step S32, the weight ω of the secondary index is determined by using the hierarchical analysis method. ζ .

7. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 5, characterized in that: Step S4 includes the following contents: For the third-level indicator reference value x ζ,ij , use time series for mapping, and obtain the function x of the reference value of the third-level indicators for year t ζ,ij (t), and then the function of the reference value of the ζ-th secondary indicator and the total benefit of hydropower transmission with respect to year t is obtained. The function formula is: Among them, Z ζ,i (t) represents the reference value of the ζ-th secondary indicator in the ith hydropower transmission channel in year t; Z i (t) represents the reference value of the total benefit of hydropower transmission of the ith hydropower transmission channel in year t.

8. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 7, characterized in that: Step S5 includes the following contents: S51, define the time series weight vector as: δ=(δ1,δ2,...,δ t ,...,d T ); Among them, δ t ∈[0,1], T represents the total number of evaluation years; S52, define the time series information entropy S as: S53, define the time degree θ as: S54, taking the maximum time series information entropy S as the objective function and the conditions satisfied by the time degree and the time series weighted vector as the constraints, a mathematical model is constructed: S55, the above objective function is solved by the interior point method to obtain the time series weighted vector, and a hydropower transmission benefit combination analysis model is constructed. The function expression is as follows: Among them, Z * It is the reference value after the combined benefit analysis of hydropower transmission.

9. The method for analyzing the benefits of hydropower transmission based on time series information entropy according to claim 1, characterized in that: The method also includes the step of optimizing the dynamic benefit analysis model of hydropower transmission.

10. An analysis system for executing the method for analyzing the benefits of hydropower transmission based on time series information entropy as claimed in any one of claims 1 to 9, characterized in that: include: The first processing unit is used to construct a three-level indicator system for the benefits of hydropower transmission, wherein the three-level indicator system includes a primary indicator, a secondary indicator and a tertiary indicator; the primary indicator is obtained based on the weight distribution of the secondary indicator parameters, and the secondary indicator is obtained based on the weight distribution of the tertiary indicator parameters; the primary indicator includes the total benefits of hydropower transmission; The second processing unit is used to calculate the weights of the third-level indicators and calculate the reference values ​​of the second-level indicators according to the reference values ​​of the third-level indicators and their weights; The third processing unit builds a static benefit analysis model for hydropower transmission based on the reference values ​​of the secondary indicators and their weights; The fourth processing unit is used to construct a dynamic benefit analysis model for hydropower transmission; The fifth processing unit introduces time series information entropy based on the dynamic benefit analysis model to construct a combined benefit analysis model for hydropower transmission.

Images