Method for selecting continuous dam area transfer port based on game theory and EDAS

Abstract

The application relates to a continuous dam area transfer port site selection method based on game theory and EDAS, and belongs to the technical field of traffic transportation engineering and port planning. The continuous dam area transfer port site selection method based on game theory and EDAS comprises the following steps: constructing a site selection evaluation index system of the continuous dam area transfer port, and screening a plurality of candidate sites from the site selection evaluation index system; obtaining evaluation index data of each candidate site, and performing standardization processing; determining subjective weights and objective weights of each evaluation index by adopting an analytic hierarchy process and an entropy weight method; fusing the subjective weights and the objective weights based on game theory to obtain comprehensive weights of each evaluation index; determining comprehensive scores of each candidate site based on an average solution distance evaluation model EDAS according to the standardization processed evaluation index data and the comprehensive weights, and determining a target site selection scheme according to the comprehensive scores. The application can improve the scientificity and reliability of the continuous dam area transfer port site selection in mountainous areas.

Description

Technical Field

[0001] This invention relates to the fields of transportation engineering and port planning technology, and in particular to a method for selecting a transshipment port in a continuous dam area based on game theory and EDAS. Background Technology

[0002] With the in-depth development of global water resources, the construction of multiple dams for cascade development on many rivers has become a common engineering model. This creates a special composite geographical unit on rivers: continuous dam areas. Original natural rivers are transformed into a series of deep-water reservoirs separated by dams, forming a segmented waterway with excellent navigation conditions but physically disconnected from each other. This waterway configuration, formed by multiple cascade dams, is characterized by impeded connectivity between reservoirs, resembling lotus roots; this is referred to in this paper as a "segmented waterway." Taking the lower reaches of the Jinsha River as an example, the successive completion of four large hydropower projects has created deep-water channels, but the dams have disrupted the continuity of shipping, resulting in the ineffective utilization of water transport resources and severe ship congestion and port delays. Therefore, constructing efficient transshipment ports to connect these shipping bottlenecks has become an urgent need to transform shipping potential into a tangible advantage. As a key node connecting the waterway in the reservoir area above the dam with the passage below the dam, the port of entry for crossing the dam plays a crucial role in the efficient transshipment of goods. The scientific selection of its location directly determines the overall operational efficiency and engineering safety of the transshipment system.

[0003] However, existing port site selection evaluation studies mainly focus on plains, inland rivers, or coastal environments, with relatively few studies specifically addressing the unique and complex environment of continuous dam areas in mountainous regions subject to high-intensity human intervention. Existing site selection technologies suffer from the following three significant limitations and technical deficiencies when dealing with continuous dam areas in mountainous regions: (1) The existing site selection index system lacks specificity and is difficult to adapt to the special needs of port site selection in continuous dam areas. Mountainous canyon river sections are characterized by steep banks, complex geological structures, and fragile ecological environments. The existing evaluation indexes are insufficient to meet their unique engineering challenges, and fail to fully quantify the problems of drastic fluctuations in backwater levels caused by artificial interventions such as dam peak-shaving operation and flood discharge, as well as complex engineering constraints such as geological hazards on high slopes.

[0004] (2) Existing weighting methods have limitations and biases, making it difficult to achieve a scientific integration of subjective and objective factors. In the weight allocation of site selection evaluation, existing technologies are often one-sided. Sole subjective weighting relies heavily on expert experience and is easily affected by subjective cognitive biases; while sole objective weighting determines the weight entirely based on the dispersion of the data itself, which may ignore the red line indicators related to engineering safety in actual engineering experience.

[0005] (3) Existing evaluation ranking models have poor resistance to extreme value interference, and the evaluation results lack robustness. In the multi-objective ranking of alternative solutions, traditional comprehensive evaluation methods mostly use ideal solutions as the benchmark, which are very sensitive to extreme values ​​in the data. In port site selection under special scenarios in continuous dam areas, due to specific terrain and spatial constraints, some indicators often contain abnormal extreme values ​​with extremely large deviations. Traditional methods are easily interfered with by these extreme discrete data, leading to evaluation distortion. Summary of the Invention

[0006] In view of this, it is necessary to provide a method for selecting transshipment ports in continuous dam areas based on game theory and EDAS, so as to solve the problems of poor adaptability of existing port selection technology in the special environment of continuous dam areas in mountainous areas, unscientific integration of subjective and objective weights, and susceptibility of evaluation results to extreme data interference.

[0007] To address the aforementioned problems, in a first aspect, this invention provides a method for selecting transshipment ports in continuous dam areas based on game theory and EDAS, comprising: A site selection evaluation index system for transshipment ports in continuous dam areas is constructed, and several candidate port sites are selected from the set of candidate port sites based on the site selection evaluation index system. Obtain the evaluation index data for each candidate port site, and standardize the evaluation index data; The subjective and objective weights of each evaluation index are determined by the analytic hierarchy process (AHP) and the entropy weight method, respectively. The entropy weight method is calculated based on the standardized evaluation index data, the AHP is used to calculate the subjective weights, and the entropy weight method is used to calculate the objective weights. Based on game theory, the subjective weights and objective weights are integrated to obtain the comprehensive weights of each evaluation index; The EDAS (Evaluation Based on Average Solution Distance) model determines the comprehensive score of each candidate port site based on the standardized evaluation index data and the comprehensive weight, and determines the target site selection scheme based on the comprehensive score.

[0008] In one possible implementation, the standardization of the evaluation index data includes: The evaluation indicators are divided into benefit-type indicators and cost-type indicators; Based on the difference between the original value and the minimum value of each candidate port site under the aforementioned benefit-type indicators, as well as the difference between the maximum value and the minimum value of the indicators, the corresponding evaluation indicator data are normalized. Based on the difference between the maximum value and the original value of each candidate port site under the cost-type index, and the difference between the maximum value and the minimum value of the index, the corresponding evaluation index data are normalized.

[0009] In one possible implementation, the analytic hierarchy process (AHP) calculates subjective weights, including: Construct a judgment matrix using the aforementioned analytic hierarchy process; An initial weight vector is constructed based on the judgment matrix using the geometric mean method; The initial weight vector is normalized to obtain a normalized weight vector, and the normalized weight vector is used as the subjective weight vector, which is composed of several subjective weights.

[0010] In one possible implementation, the method further includes: Calculate the largest eigenvalue in the judgment matrix; Calculate the consistency ratio based on the maximum eigenvalue; When the consistency ratio is less than a preset ratio threshold, the normalized weight vector is determined to be valid, and the normalized weight vector is used as the subjective weight vector.

[0011] In one possible implementation, the entropy weight method calculates the objective weights, including: Based on the standardized evaluation index data, calculate the characteristic weight of each candidate port site under each evaluation index; Calculate the information entropy of each evaluation index based on the weight of the aforementioned features; The objective weights of each evaluation index are obtained based on the information entropy.

[0012] In one possible implementation, the subjective weights and objective weights are integrated based on game theory to obtain a comprehensive weight for each evaluation index, including: Construct an optimization function with the objective of minimizing the deviation between the combined weights and the subjective and objective weights; The optimization function is transformed into a system of linear equations and the target linear combination coefficients are solved. The coefficients of the target linear combination are normalized to obtain the comprehensive weight of each evaluation index.

[0013] In one possible implementation, the Average Solution Distance Evaluation Model (EDAS) determines the comprehensive score of each candidate port site based on the standardized evaluation index data and the comprehensive weight, including: The average value of standardized data of all candidate port sites is calculated based on EDAS for each evaluation index, and the average value is used as the average solution of the corresponding index. Based on the EDAS, calculate the positive and negative distances of the standardized evaluation index data of each candidate port site relative to the average solution; The weighted sum of positive distances and the weighted sum of negative distances for each candidate port site are calculated by combining the comprehensive weights, the positive distances, and the negative distances. The weighted positive distance sum and the weighted negative distance sum are normalized respectively, and the comprehensive score of each candidate port site is obtained based on the normalized positive distance sum and the normalized negative distance sum.

[0014] Secondly, the present invention also provides a continuous dam area transshipment port site selection device based on game theory and EDAS, comprising: The indicator system construction module is used to construct the site selection evaluation indicator system for transshipment ports in continuous dam areas, and to select several candidate port sites from the set of candidate port sites based on the site selection evaluation indicator system. The data acquisition and processing module is used to acquire evaluation index data for each candidate port site and to standardize the evaluation index data. The weight calculation module is used to determine the subjective and objective weights of each evaluation index using the analytic hierarchy process (AHP) and the entropy weight method, respectively. The entropy weight method is calculated based on the standardized evaluation index data, the AHP is used to calculate the subjective weights, and the entropy weight method is used to calculate the objective weights. The weight fusion module is used to fuse the subjective weights and the objective weights based on game theory to obtain the comprehensive weights of each evaluation index. The comprehensive evaluation module is used to determine the comprehensive score of each candidate port site based on the average solution distance evaluation model EDAS, the standardized evaluation index data, and the comprehensive weight, and to determine the target site selection scheme based on the comprehensive score.

[0015] Thirdly, the present invention also provides an electronic device, including a data collector, a memory and a processor, wherein the data collector is used to acquire evaluation index data of each candidate port site; The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the continuous dam area transshipment port site selection method based on game theory and EDAS described in any of the above implementations.

[0016] Fourthly, the present invention also provides a continuous dam area transshipment port site selection system based on game theory and EDAS. The continuous dam area transshipment port site selection system based on game theory and EDAS includes electronic equipment, data acquisition equipment and result output equipment as described above. The data acquisition equipment and the result output equipment are respectively connected to the electronic equipment. The data acquisition equipment is used to acquire evaluation index data of each candidate port site. The result output equipment is used to output and display the target site selection scheme and the comprehensive score of each candidate port site.

[0017] The beneficial effects of this invention are as follows: The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS provided by this invention achieves a comprehensive quantitative assessment of the characteristics of mountainous canyon terrain and cascade dam projects by constructing a site selection evaluation index system specifically for the characteristics of continuous dam areas. Furthermore, it introduces expert experience to determine subjective weights through the analytic hierarchy process (AHP), mines the inherent laws of data to determine objective weights through the entropy weight method, and solves for the optimal combination coefficients based on a game theory-based combined weighting model with the goal of minimizing the deviation between subjective and objective weights. This scientifically integrates subjective and objective weights, preserving the professional judgment of experts on key engineering red lines while fully mining the inherent discrete laws of objective data. On this basis, an average solution distance evaluation model is introduced, using the average value of each index as the evaluation benchmark to calculate the positive and negative distances of each candidate port site relative to the average solution. Combined with comprehensive weights, a comprehensive score is calculated, effectively avoiding the interference of extreme geographical data unique to mountainous areas on the evaluation results. This successfully solved the technical problems of existing technologies, such as the lack of specificity of the indicator system, the difficulty in scientifically integrating subjective and objective weighting, and the susceptibility of evaluation results to interference from extreme data. It achieved highly reliable quantitative decision support for the site selection of transshipment ports in continuous dam areas in mountainous regions, and significantly improved the scientificity and robustness of the site selection scheme. Attached Figure Description

[0018] Figure 1 A flowchart illustrating an embodiment of the continuous dam area transshipment port site selection method based on game theory and EDAS provided by the present invention. Figure 2 A schematic diagram of the overall process of the site selection evaluation method for an embodiment of the continuous dam area transshipment port site selection method based on game theory and EDAS provided by the present invention; Figure 3 A schematic diagram of the evaluation index system for transshipment port site selection, based on game theory and EDAS, as an embodiment of the transshipment port site selection method for continuous dam areas provided by the present invention. Figure 4 A schematic diagram of the location of candidate port sites for a jointed waterway, based on a method for selecting transshipment ports in a continuous dam area according to game theory and EDAS, provided by the present invention. Figure 5 The weight distribution diagram of each primary index of an embodiment of the continuous dam area transshipment port site selection method based on game theory and EDAS provided by the present invention; Figure 6 The final comprehensive score ranking result of the EDAS model is shown in the figure for an embodiment of the continuous dam area transshipment port site selection method based on game theory and EDAS provided by the present invention. Figure 7 A functional block diagram of a continuous dam area transshipment port site selection device based on game theory and EDAS provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides a method for selecting transshipment ports in continuous dam areas based on game theory and EDAS, which will be described in detail below.

[0024] Figure 1 This is a flowchart illustrating an embodiment of the continuous dam area transshipment port site selection method based on game theory and EDAS provided by the present invention. The executing entity of the continuous dam area transshipment port site selection method based on game theory and EDAS can be a computer, a portable smart device, or a cloud server, etc. Specifically, the executing entity is the storage medium in the computer; this embodiment does not limit this. Figure 1 As shown, the site selection method for transshipment ports in continuous dam areas based on game theory and EDAS includes: Before demonstrating the embodiments, the following terms will be explained.

[0025] AHP: Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that decomposes decision problems into hierarchical structures such as objectives, criteria, and alternatives, and combines qualitative and quantitative approaches by constructing a judgment matrix and calculating weights.

[0026] EWM: Entropy Weight Method, is a weighting method that calculates objective weights based on the dispersion of data for each indicator using information entropy. The smaller the information entropy, the greater the amount of information provided by the indicator, and the greater its weight.

[0027] EDAS (Evaluation based on Distance from Average Solution) is a multi-attribute decision-making method that uses the average value of each indicator as a benchmark and comprehensively evaluates each solution by calculating the positive and negative distances of each solution relative to the average solution.

[0028] Game theory-based combinatorial weighting: a subjective and objective weight fusion method that uses game theory to solve for the optimal combinatorial coefficients by minimizing the difference between the combined weights and the weights obtained by each individual weighting method.

[0029] S101. Construct a site selection evaluation index system for transshipment ports in continuous dam areas, and select several candidate port sites from the set of candidate port sites based on the site selection evaluation index system.

[0030] In this embodiment, a site selection evaluation index system for transshipment ports in continuous dam areas is first constructed. Based on the site selection requirements of transshipment ports in continuous dam areas, this system systematically sets evaluation dimensions and specific indicators to measure the merits of each site selection scheme, ensuring that the evaluation process is based on evidence. Subsequently, according to the constructed evaluation index system, multiple candidate schemes in the set of port sites to be selected are screened. By matching the index requirements and comparing the constraints, port sites that do not meet the basic evaluation standards are eliminated, thereby retaining several candidate port sites that meet the preliminary evaluation conditions, thus establishing a clear decision-making scope for subsequent quantitative analysis.

[0031] It should be noted that the site selection evaluation index system constructed in this embodiment is designed for the special geographical and hydrological environment of continuous dam areas in mountainous regions. It fully considers the mountainous canyon topography and the engineering characteristics of cascade dams. The site selection evaluation index system includes at least: indicators for water level fluctuations during dam peak-shaving operation, indicators for the impact of fluctuating backwater areas, and indicators for the frequency of geological disasters affecting canyon slope stability. This site selection evaluation index involves a total of 23 indicators related to natural conditions, engineering technology, socio-economic benefits, and environmental protection.

[0032] S102. Obtain the evaluation index data of each candidate port site, and standardize the evaluation index data.

[0033] In this embodiment, evaluation index data for each candidate port site is acquired and standardized. This standardization process aims to convert raw evaluation data with different physical dimensions and numerical ranges into a dimensionless form, eliminating calculation biases caused by dimensional differences and ensuring that subsequent weight allocation and comprehensive evaluation are based on comparable numerical foundations. The evaluation index data includes quantitative measured values ​​and expert scores. Their original values ​​may be expressed in different units such as kilometers, tons, or scores. Standardization maps all index values ​​to a unified scale, providing consistent data input for subsequent quantitative evaluation based on game theory-based combinational weighting and the EDAS model.

[0034] In some embodiments, step S102 includes: dividing the evaluation indicators into benefit-type indicators and cost-type indicators; normalizing the corresponding evaluation indicator data based on the difference between the original value and the minimum value of each candidate port site under the benefit-type indicators, and the difference between the maximum value and the minimum value of each indicator; and normalizing the corresponding evaluation indicator data based on the difference between the maximum value and the original value of each candidate port site under the cost-type indicators, and the difference between the maximum value and the minimum value of each indicator.

[0035] It should be understood that direct calculation of different evaluation indicators will produce errors due to their different physical meanings and dimensions. Therefore, it is necessary to standardize the raw data. The benefit-type indicators are positive indicators where a larger value is better, including water depth at the wharf front, channel width, available land area width, usable shoreline length, potential cargo volume in the hinterland, and long-term development potential. The cost-type indicators are negative indicators where a smaller value is better, including channel flow velocity, water level fluctuation, impact of backwater changes, seismic fortification intensity, frequency of geological disasters, shoreline slope angle, land-based transshipment mileage over dams, mileage of connecting channels, impact of nearby settlements, occupation of cultivated land or residential land, and constraints of ecological protection zones.

[0036] Specifically, the further technical solution and the specific formula for standardization are as follows: Assume the initial decision matrix is ​​X=(x ij ) m×n , where m is the number of alternative solutions and n is the number of indicators.

[0037] For benefit-type indicators (the larger the value, the better), the standardized formula is:

[0038] For cost-related indicators (the smaller the value, the better), the standardized formula is:

[0039] Where, x ij Let x' be the original value of the i-th scheme on the j-th index. ij This is the standardized value.

[0040] S103. The subjective and objective weights of each evaluation index are determined using the analytic hierarchy process (AHP) and the entropy weight method, respectively.

[0041] It should be noted that in this embodiment, the analytic hierarchy process (AHP) is used to calculate subjective weights, while the entropy weight method is used to calculate objective weights. The AHP transforms qualitative judgments into quantitative weights by inviting experts to construct a judgment matrix, reflecting the importance of expert experience in site selection decisions. The entropy weight method calculates weights based on the dispersion of standardized data, reflecting the information content of the data itself.

[0042] In some embodiments, the analytic hierarchy process (AHP) for calculating subjective weights includes: constructing a judgment matrix using the AHP; constructing an initial weight vector based on the judgment matrix using the geometric mean method; normalizing the initial weight vector to obtain a normalized weight vector, and using the normalized weight vector as a subjective weight vector, wherein the subjective weight vector is composed of several subjective weights.

[0043] Specifically, the steps for calculating subjective weights using the Analytic Hierarchy Process (AHP) are as follows: Construct the judgment matrix A=(a ij ) m×n , where aij represents the importance of index i relative to index j; The weights are calculated using the geometric mean method, as shown in the following formula:

[0044] Then, for vector M=[m1,m2,...,m n After normalization, the weight vector can be obtained. = The details are as follows:

[0045] In some embodiments, the method further includes: calculating the largest eigenvalue in the judgment matrix; calculating a consistency ratio based on the largest eigenvalue; and when the consistency ratio is less than a preset ratio threshold, determining that the normalized weight vector is valid, and using the normalized weight vector as the subjective weight vector.

[0046] Specifically, the consistency ratio (CR) of the judgment matrix is ​​calculated for verification: ,

[0047] Where, λ max RI is the largest eigenvalue and the random consistency index; if CR < 0.1, the weights are considered valid.

[0048] In some embodiments, the entropy weight method for calculating objective weights includes: calculating the feature weight of each candidate port site under each evaluation indicator based on the standardized evaluation indicator data; calculating the information entropy of each evaluation indicator based on the feature weight; and obtaining the objective weight of each evaluation indicator based on the information entropy.

[0049] Specifically, using the standardized decision matrix X', the feature weight p of the j-th scheme under the i-th index is calculated. ij :

[0050] Calculate the information entropy E of the j-th indicator. j :

[0051] When p ij When =0, define p ij ln(p ij )=0.

[0052] Calculate the objective weight of the j-th indicator ω j :

[0053] Where n is the total number of evaluation indicators.

[0054] S104. Based on game theory, the subjective weights and objective weights are integrated to obtain the comprehensive weights of each evaluation index.

[0055] It should be noted that this embodiment introduces a game theory-based combined weighting model. By constructing an optimization function aimed at minimizing the bias between subjective and objective weights, the optimal linear combination coefficients are solved, thus integrating subjective and objective weights. This process retains the expert's professional judgment on the key project red lines while fully exploring the inherent discrete patterns of objective data, avoiding the one-sidedness of a single weighting method.

[0056] In some embodiments, step S104 includes: constructing an optimization function with the objective of minimizing the deviation between the combined weights and the subjective and objective weights; converting the optimization function into a system of linear equations and solving for the target linear combination coefficients; and normalizing the target linear combination coefficients to obtain the comprehensive weights of each evaluation index.

[0057] Specifically, let the linear combination weight vector be:

[0058] Where w1=φ is the subjective weight vector, w2=ω is the objective weight vector, and αk is the linear combination coefficient.

[0059] Construct an optimization model with the objective of minimizing the deviation:

[0060] Based on the properties of matrix differentiation, the above optimization problem is transformed into solving a system of linear equations to find the optimal combination coefficients (α1, α2):

[0061] The coefficients obtained from the solution are normalized to obtain the final combined weight Wj:

[0062] in, , .

[0063] S105. Based on the average solution distance evaluation model EDAS, the comprehensive score of each candidate port site is determined according to the standardized evaluation index data and the comprehensive weight, and the target site selection scheme is determined according to the comprehensive score.

[0064] It should be noted that this embodiment introduces the Average Solution Distance Evaluation Model (EDAS), which uses the average value of each indicator as the evaluation benchmark, effectively avoiding the interference of extreme geographical data unique to mountainous areas on the evaluation results.

[0065] In some embodiments, step S105 includes: calculating the average value of standardized data of all candidate port sites under each evaluation index based on EDAS, wherein the average value is used as the average solution of the corresponding index; calculating the positive and negative distances of the standardized evaluation index data of each candidate port site relative to the average solution based on the EDAS; calculating the weighted sum of positive distances and the weighted sum of negative distances of each candidate port site by combining the comprehensive weight, the positive distances, and the negative distances; normalizing the weighted sum of positive distances and the weighted sum of negative distances respectively, and obtaining the comprehensive score of each candidate port site based on the normalized sum of positive distances and the normalized sum of negative distances.

[0066] Specifically, calculate the average solution AVj for each index:

[0067] Calculate the positive distance (PDA) and negative distance (NDA) of each solution relative to the average solution: If the j-th indicator is a benefit-type indicator:

[0068] If the j-th indicator is a cost-type indicator:

[0069] By combining the combined weights Wj, calculate the weighted positive distance sum (SPi) and weighted negative distance sum (SNi) for each scheme:

[0070] Normalizing SPi and SNi yields NSPi and NSNi:

[0071] Calculate the final comprehensive score ASi for each scheme:

[0072] Among them, AS i The larger the value, the better the port location plan.

[0073] For example, the site selection of a transshipment port in the reservoir area of ​​the Xiluodu Hydropower Station on the lower reaches of the Jinsha River is used as an example for verification. This can be referred to... Figure 2 As shown, the comprehensive evaluation index system for the site selection of transshipment ports in continuous dam areas can be referenced. Figure 3 As shown, the specific steps include: (1) Screening out potential port sites for continuous dam area ports; Based on on-site surveys and engineering data such as the "Zhaotong City Port Master Plan," three candidate port sites with suitable construction conditions were selected upstream of the Xiluodu Dam: Jinshakou (Option A), Huangjuebao (Option B), and Majiaheba (Option C). The locations of the candidate port sites for the lotus-root-shaped channel can be found in the diagram. Figure 4 As shown in Table 1, the basic characteristics of the three alternative port sites are compared. Table 1 compares the key features of the three alternative port site schemes.

[0074] Table 1:

[0075] (2) Establish an indicator evaluation system and assign values ​​to each evaluation indicator for each candidate port site; This embodiment constructs an evaluation system comprising four primary indicators and 23 secondary indicators, including natural conditions (B1), engineering technology (B2), socio-economic benefits (B3), and environmental protection (B4). The proportion of each indicator can be referenced. Figure 5As shown. Based on the nature of the indicators, they are divided into positive indicators (benefit-oriented), where larger values ​​are better, and negative indicators (cost-oriented), where smaller values ​​are better. Positive indicators include: water depth at the wharf (C12), channel width (C13), usable land depth (C24), usable shoreline length (C25), difficulty of berth hydraulic engineering construction (C41), land foundation bearing capacity (C43), potential cargo volume in the hinterland (C51), long-term development potential (C52), difficulty of cross-regional coordination (C63), ecological vulnerability and vegetation impact (C72), etc. Negative indicators include: channel flow velocity (C11), water level fluctuation range (C14), impact of backwater fluctuations (C15), seismic fortification intensity (C21), frequency of geological disasters (C22), bank slope ratio (C23), transfer distance of diversion road (C31), construction mileage of connecting channels (C32), impact of nearby settlements (C61), occupation of cultivated land / homestead (C62), and constraints of ecological protection zones (C71), etc.

[0076] Values ​​were assigned to various evaluation indicators for each candidate port site. Quantitative indicators (such as C12 and C31) were based on measured data; qualitative indicators (such as C41 and C52) were based on expert scoring (0-5 points). The initial evaluation indicator data (initial decision matrix) for each candidate port site are shown in Table 2. Table 2 shows the initial evaluation indicator data for each alternative scheme.

[0077] Table 2:

[0078] (3) Normalize the evaluation index data of each candidate port site; Because different evaluation indicators have different physical meanings and dimensions (e.g., distance in km, cargo volume in 10,000 tons), direct calculation will produce errors. According to the normalization formula described in the invention, the original data in Table 2 is processed to obtain a dimensionless normalized matrix, as shown in Table 3. Table 3 shows the normalization results of the evaluation indicators for the candidate port sites.

[0079] Table 3:

[0080] (4) Calculate subjective weights using the analytic hierarchy process (AHP), calculate objective weights using the entropy weight method, and introduce game theory concepts for combined weighting: First, the Analytic Hierarchy Process (AHP) was used to invite eight experts to score and construct a judgment matrix, and the subjective weight vector was calculated, as shown in Table 4. Table 4 shows the subjective weight calculation results of the Analytic Hierarchy Process. Second, the objective weight vector was calculated based on standardized data using the Entropy Weight Method (EWM), as shown in Table 5. Table 5 shows the objective weight calculation results of the Entropy Weight Method.

[0081] Table 4:

[0082] Table 5:

[0083] Introducing a game theory combinatorial weighting model to construct an optimization function The linear combination coefficients are calculated as follows: α*1 = 0.4978, α*2 = 0.5022. These coefficients are then combined to obtain the final comprehensive weight Wj. The calculated weights of each evaluation index are shown in Table 6, which presents the combined weighting results based on game theory. Further references can also be made to... Figure 6 As shown.

[0084] Table 6:

[0085] Analysis of the data in Table 6 shows that C31 (transfer distance) and C14 (water level fluctuation range) have the highest weights, totaling over 13%. This indicates that in mountainous continuous dam areas, transfer efficiency (which determines the economics of dam transfer) and adaptability to water level fluctuations (which determines the safety of port operations) are the core constraints of site selection decisions.

[0086] (5) Based on the EDAS model, calculate the score of each candidate port site, and determine the optimal port site according to the score. Use the EDAS model to comprehensively evaluate each scheme. The EDAS evaluation results of each candidate port site are shown in Table 7.

[0087] Table 7:

[0088] According to the data analysis in Table 7, Scheme C (Majiahe Dam) ranks highest with a score of 0.9695. This scheme demonstrates significant advantages in the highest-weighted indicators C31 (transfer transport distance of 18.5 km, the shortest of the three) and C22 (lowest frequency of geological disasters). Although its water level fluctuation range (C14) is slightly inferior to Scheme A, its higher SP value (0.1966) and lower SN value (0.0141) indicate that it has the best overall benefits and the lowest risk. Scheme A (Jinshakou) ranks second. Its main advantage lies in its long shoreline, but it is constrained by steep bank slopes (C23) and insufficient land depth (C24), resulting in a lower score in engineering and technical indicators. Scheme B (Huangjuebao) ranks last. This is mainly because it is adjacent to a residential area, resulting in significant deficiencies in social indicators such as C61 and C62, leading to an excessively large negative distance (SN).

[0089] (6) Conduct sensitivity analysis to verify the stability of the evaluation results: To examine the stability of the ranking results under different decision preferences, this study simulated three different weighting scenarios and compared them with the "baseline scenario." Different decision preferences were simulated by adjusting the weighting of the primary criteria (increasing it by 20% from the baseline weights and normalizing and compressing other criteria): Baseline scenario: Using the final combined weights in Table 6 (B1=41.1%, B2=26.4%, B3=21.7%, B4=10.8%).

[0090] Scenario 1 (Safety First): Increase the weight of B1 (Natural Conditions) by 20% to simulate decision-makers' preference for geological safety and engineering feasibility.

[0091] Scenario 2 (Efficiency and Economy First): Increase the weight of B2 (Engineering and Technology) and B3 (Socio-economic) by 20% each to simulate decision-makers' preference for dam crossing efficiency and potential cargo sources.

[0092] Scenario 3 (Environmental and Social Priorities): Increase the weight of B3 (Socio-economic) and B4 (Environmental Protection) by 20% each to simulate policymakers' preference for social impact and ecological protection.

[0093] The EDAS model was rerun using these four different sets of combined weights, and the final evaluation values ​​(ASi) and rankings are shown in Table 8. Table 8 shows the sensitivity test results under different scenarios.

[0094] Table 8:

[0095] As shown in Table 8, although the ASi evaluation values ​​fluctuated under the three scenarios, the final ranking results remained unchanged. This fully demonstrates that the comprehensive advantages of Scheme C (Majiahe Dam) are comprehensive, and its status as the optimal scheme remains stable even when decision preferences change significantly.

[0096] In summary, this invention achieves accurate decision-making for port site selection in continuous dam areas through the construction of a scientific indicator system, game theory-based weighting, and EDAS model evaluation, solving the problem of poor adaptability of traditional methods under complex terrain and hydrological conditions.

[0097] This embodiment constructs a site selection evaluation index system specifically tailored to the characteristics of continuous dam areas, achieving a comprehensive quantitative assessment of the topography of mountainous canyons and the engineering characteristics of cascade dams. Furthermore, it introduces expert experience to determine subjective weights using the analytic hierarchy process (AHP), mines the inherent patterns in the data to determine objective weights using the entropy weight method, and solves for the optimal combination coefficients based on a game theory-based combined weighting model with the goal of minimizing the deviation between subjective and objective weights. This scientifically integrates subjective and objective weights, preserving expert judgment on key engineering red lines while fully exploring the inherent discrete patterns of objective data. On this basis, an average solution distance evaluation model is introduced, using the average value of each index as the evaluation benchmark to calculate the positive and negative distances of each candidate port site relative to the average solution. Combined with comprehensive weights, a comprehensive score is calculated, effectively avoiding the interference of extreme geographical data unique to mountainous areas on the evaluation results. Thus, it successfully solves the technical problems of existing technologies, such as the lack of specificity in the index system, the difficulty in scientifically integrating subjective and objective weights, and the susceptibility of evaluation results to extreme data interference. It achieves highly reliable quantitative decision support for the site selection of transshipment ports in continuous dam areas of mountainous regions, significantly improving the scientific rigor and robustness of the site selection scheme.

[0098] Based on the same idea as the game theory and EDAS-based continuous dam area transshipment port site selection method in the above embodiments, this application also provides a game theory and EDAS-based continuous dam area transshipment port site selection device 700, which can be used to execute the above-described game theory and EDAS-based continuous dam area transshipment port site selection method. Figure 7 As shown, the system comprises an indicator system construction module 701, a data acquisition and processing module 702, a weight calculation module 703, a weight fusion module 704, and a comprehensive evaluation module 705. In some embodiments, these modules may be programmable software instructions stored in memory and executable by a processor. It is understood that in other embodiments, these modules may also be program instructions or firmware embedded in a processor.

[0099] The indicator system construction module 701 is used to construct the site selection evaluation indicator system for transshipment ports in continuous dam areas, and to select several candidate port sites from the set of candidate port sites based on the site selection evaluation indicator system. The data acquisition and processing module 702 is used to acquire the evaluation index data of each candidate port site and to standardize the evaluation index data. The weight calculation module 703 is used to determine the subjective weight and objective weight of each evaluation index using the analytic hierarchy process (AHP) and the entropy weight method, respectively. The entropy weight method is calculated based on the standardized evaluation index data, the AHP is used to calculate the subjective weight, and the entropy weight method is used to calculate the objective weight. The weight fusion module 704 is used to fuse the subjective weights and the objective weights based on game theory to obtain the comprehensive weights of each evaluation index. The comprehensive evaluation module 705 is used to determine the comprehensive score of each candidate port site based on the average solution distance evaluation model EDAS according to the standardized evaluation index data and the comprehensive weight, and to determine the target site selection scheme according to the comprehensive score.

[0100] Please refer to Figure 8 , Figure 8 This is a schematic diagram of an embodiment of the electronic device of this application. In this embodiment of the invention, the electronic device 800 includes a processor 801, a memory 802, a display 803, and a data acquisition device 804. Figure 8 Only some components of the electronic device 800 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0101] In some embodiments, processor 801 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 802 or process data, such as the continuous dam area transshipment port site selection method based on game theory and EDAS in this invention.

[0102] In some embodiments, display 803 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 803 is used to display information from electronic device 800 and to display visual user applications. Components 801-804 of electronic device 800 communicate with each other via a system bus.

[0103] In one embodiment, when the processor 801 executes the continuous dam area transshipment port site selection program based on game theory and EDAS stored in the memory 802, the following steps can be implemented: A site selection evaluation index system for transshipment ports in continuous dam areas is constructed, and several candidate port sites are selected from the set of candidate port sites based on the site selection evaluation index system. Obtain the evaluation index data for each candidate port site, and standardize the evaluation index data; The subjective and objective weights of each evaluation index are determined by the analytic hierarchy process (AHP) and the entropy weight method, respectively. The entropy weight method is calculated based on the standardized evaluation index data, the AHP is used to calculate the subjective weights, and the entropy weight method is used to calculate the objective weights. Based on game theory, the subjective weights and objective weights are integrated to obtain the comprehensive weights of each evaluation index; The EDAS (Evaluation Based on Average Solution Distance) model determines the comprehensive score of each candidate port site based on the standardized evaluation index data and the comprehensive weight, and determines the target site selection scheme based on the comprehensive score.

[0104] It should be understood that when the processor 801 executes the aero-optical effect sequence image restoration program in the memory 802, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0105] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 800 mentioned. Electronic device 800 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 800 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0106] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0107] The above provides a detailed description of the continuous dam area transshipment port site selection method based on game theory and EDAS provided in this application. Specific examples are used in this paper to illustrate the principle and implementation of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​this application. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for selecting transshipment ports in continuous dam areas based on game theory and EDAS, characterized in that, include: A site selection evaluation index system for transshipment ports in continuous dam areas is constructed, and several candidate port sites are selected from the set of candidate port sites based on the site selection evaluation index system. Obtain the evaluation index data for each candidate port site, and standardize the evaluation index data; The subjective and objective weights of each evaluation index are determined by the analytic hierarchy process (AHP) and the entropy weight method, respectively. The entropy weight method is calculated based on the standardized evaluation index data, the AHP is used to calculate the subjective weights, and the entropy weight method is used to calculate the objective weights. Based on game theory, the subjective weights and objective weights are integrated to obtain the comprehensive weights of each evaluation index; The EDAS (Evaluation Based on Average Solution Distance) model determines the comprehensive score of each candidate port site based on the standardized evaluation index data and the comprehensive weight, and determines the target site selection scheme based on the comprehensive score.

2. The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS according to claim 1, characterized in that, The standardization process for the evaluation index data includes: The evaluation indicators are divided into benefit-type indicators and cost-type indicators; Based on the difference between the original value and the minimum value of each candidate port site under the aforementioned benefit-type indicators, as well as the difference between the maximum value and the minimum value of the indicators, the corresponding evaluation indicator data are normalized. Based on the difference between the maximum value and the original value of each candidate port site under the cost-type index, and the difference between the maximum value and the minimum value of the index, the corresponding evaluation index data are normalized.

3. The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS according to claim 1, characterized in that, The analytic hierarchy process (AHP) calculates subjective weights, including: Construct a judgment matrix using the aforementioned analytic hierarchy process; An initial weight vector is constructed based on the judgment matrix using the geometric mean method; The initial weight vector is normalized to obtain a normalized weight vector, and the normalized weight vector is used as the subjective weight vector, which is composed of several subjective weights.

4. The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS according to claim 3, characterized in that, The method further includes: Calculate the largest eigenvalue in the judgment matrix; Calculate the consistency ratio based on the maximum eigenvalue; When the consistency ratio is less than a preset ratio threshold, the normalized weight vector is determined to be valid, and the normalized weight vector is used as the subjective weight vector.

5. The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS according to claim 1, characterized in that, The entropy weight method calculates objective weights, including: Based on the standardized evaluation index data, calculate the characteristic weight of each candidate port site under each evaluation index; Calculate the information entropy of each evaluation index based on the weight of the aforementioned features; The objective weights of each evaluation index are obtained based on the information entropy.

6. The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS according to claim 1, characterized in that, The method of integrating the subjective and objective weights based on game theory to obtain the comprehensive weights of each evaluation index includes: Construct an optimization function with the objective of minimizing the deviation between the combined weight vector and the subjective and objective weight vectors; The optimization function is transformed into a system of linear equations and the target linear combination coefficients are solved. The coefficients of the target linear combination are normalized to obtain the comprehensive weight of each evaluation index.

7. The method for selecting transshipment ports in continuous dam areas based on game theory and EDAS according to claim 1, characterized in that, The EDAS (Evaluation Model Based on Average Solution Distance) determines the comprehensive score of each candidate port site based on the standardized evaluation index data and the comprehensive weight, including: The average value of standardized data of all candidate port sites is calculated based on EDAS for each evaluation index, and the average value is used as the average solution of the corresponding index. Based on the EDAS, calculate the positive and negative distances of the standardized evaluation index data of each candidate port site relative to the average solution; The weighted sum of positive distances and the weighted sum of negative distances for each candidate port site are calculated by combining the comprehensive weights, the positive distances, and the negative distances. The weighted positive distance sum and the weighted negative distance sum are normalized respectively, and the comprehensive score of each candidate port site is obtained based on the normalized positive distance sum and the normalized negative distance sum.

8. A continuous dam area transshipment port site selection device based on game theory and EDAS, characterized in that, include: The indicator system construction module is used to construct the site selection evaluation indicator system for transshipment ports in continuous dam areas, and to select several candidate port sites from the set of candidate port sites based on the site selection evaluation indicator system. The data acquisition and processing module is used to acquire evaluation index data for each candidate port site and to standardize the evaluation index data. The weight calculation module is used to determine the subjective and objective weights of each evaluation index using the analytic hierarchy process (AHP) and the entropy weight method, respectively. The entropy weight method is calculated based on the standardized evaluation index data, the AHP is used to calculate the subjective weights, and the entropy weight method is used to calculate the objective weights. The weight fusion module is used to fuse the subjective weights and the objective weights based on game theory to obtain the comprehensive weights of each evaluation index. The comprehensive evaluation module is used to determine the comprehensive score of each candidate port site based on the average solution distance evaluation model EDAS, the standardized evaluation index data, and the comprehensive weight, and to determine the target site selection scheme based on the comprehensive score.

9. An electronic device, characterized in that, It includes a data collector, a memory, and a processor, wherein the data collector is used to acquire evaluation index data for each candidate port site; The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the continuous dam area transshipment port site selection method based on game theory and EDAS as described in any one of claims 1 to 7.

10. A continuous dam area transshipment port site selection system based on game theory and EDAS, characterized in that, The continuous dam area transshipment port site selection system based on game theory and EDAS includes the electronic equipment, data acquisition equipment, and result output equipment as described in claim 9. The data acquisition equipment and the result output equipment are respectively connected to the electronic equipment. The data acquisition equipment is used to acquire evaluation index data of each candidate port site, and the result output equipment is used to output and display the target site selection scheme and the comprehensive score of each candidate port site.

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