A long distance submarine cable routing method

Abstract

The application provides a long-distance submarine cable route selection method and relates to the technical field of offshore wind power generation. The method comprises the following steps: defining a comprehensive evaluation system; determining subjective weights by using an analytic hierarchy process and determining objective weights by using an entropy weight method; obtaining combined weights by dynamically fusing the subjective and objective weights through a random forest model based on route scene characteristic parameters; normalizing and weighting a decision matrix; using a triangular fuzzy number-orthogonal projection hybrid improved TOPSIS algorithm to represent dynamic time sequence indexes by triangular fuzzy numbers, defining fuzzy positive and negative ideal solutions, calculating the perpendicular distances of each scheme to the ideal solution, and combining Euclidean distances to perform two-dimensional sorting, so that the scheme with the smallest perpendicular distance is regarded as the optimal route scheme. The application realizes scene adaptive fusion of subjective and objective weights, eliminates the fuzziness of dynamic indexes, introduces engineering safety constraint verification, and improves the scientificity, adaptability and engineering reliability of long-distance submarine cable route selection.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power generation technology, and more specifically to a method for comparing and selecting long-distance submarine cable routes. Background Technology

[0002] With the accelerated development of deep-sea energy resources globally, industries such as offshore wind power and marine oil and gas are rapidly developing. Long-distance submarine cables, as key infrastructure for power transmission and communication, are increasingly demonstrating their strategic importance. The rational selection of submarine cable routes directly affects project safety, investment costs, ecological and environmental impacts, and socio-economic benefits, serving as a crucial guarantee for the stability of national energy supply and the intensive use of marine space.

[0003] Current long-distance submarine cable route planning faces a complex and ever-changing deep-sea environment, involving multiple factors such as geological conditions, hydrological characteristics, ecologically sensitive areas, shipping and transportation, aquaculture, marine protected areas, military restricted areas, and various planning constraints. The environmental characteristics and development activities of different sea areas vary significantly, and the layout of cables across regions and seas requires coordination between conflicts of various types of space use and ecological protection needs.

[0004] Existing research largely focuses on route selection at the engineering application level in local sea areas, which has the following shortcomings: 1. Traditional AHP relies on expert experience and is easily influenced by subjective preferences; even with the introduction of entropy weighting for a combination of subjective and objective weights, it cannot dynamically adjust the fusion strategy of subjective and objective weights according to the actual routing scenario (such as high ecological risk areas, planning sensitive areas, and deep-water complex geological areas). 2. Traditional TOPSIS, based on the proximity of the Euclidean distance calculation scheme to the positive and negative ideal solutions, ignores the possible correlation between indicators, leading to information redundancy; at the same time, for indicators with time-series characteristics such as sea state changes during construction and dynamic evolution of ecological impacts, conventionally determined values ​​are difficult to accurately represent their uncertainty. 3. Existing methods mostly focus on comprehensive scoring and ranking, lacking explicit verification of key engineering safety constraints (such as the intersection angle of submarine pipelines), which may lead to the optimal ranking scheme being infeasible in actual engineering. Summary of the Invention

[0005] The main objective of this invention is to provide a method for comparing and selecting routes for long-distance submarine cables, which solves the problems of poor adaptability to different scenarios, low accuracy and reliability of comparison, insufficient consideration of engineering constraints, and insufficient differentiation in the comprehensive evaluation of schemes.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for comparing and selecting routes for long-distance submarine cables, comprising the following steps: Taking long-distance submarine cable routes as the object, a comprehensive evaluation index system is defined; the comprehensive evaluation index system includes multiple primary indicators and multiple secondary indicators, some of which are cost-type indicators, and the rest are benefit-type indicators; The comprehensive evaluation index is divided into two layers using the analytic hierarchy process: the target layer and the criteria layer. Multiple alternative routes for long-distance submarine cables were identified, and a decision matrix was constructed based on all alternative routes. The analytic hierarchy process (AHP) was used to determine the subjective weights of each lower-level indicator relative to the upper-level indicator, and the entropy weight method was used to determine the objective weights of each lower-level indicator relative to the upper-level indicator. Based on the characteristic parameters of the routing scenario, the subjective weights and objective weights are dynamically fused to obtain the combined weights of each indicator; The decision matrix is ​​normalized and combined with the combined weights to obtain a weighted normalized decision matrix; A hybrid improved TOPSIS algorithm using triangular fuzzy numbers and orthogonal projection is adopted to optimize the routing scheme: dynamic time-series indicators are represented by triangular fuzzy numbers, while static indicators are represented by deterministic values. Fuzzy positive ideal solutions and fuzzy negative ideal solutions are defined respectively. The fuzzy vertical distance from each routing scheme to the line connecting the positive and negative ideal solutions is calculated, and the scheme with the best fuzzy vertical distance or the best comprehensive evaluation is determined as the optimal routing scheme for long-distance submarine cables.

[0007] In the preferred embodiment, the comprehensive evaluation index system includes 4 primary indicators and 16 secondary indicators; The primary indicators include ecological environment, geology and engineering, socio-economic coordination, and spatial planning; The secondary indicators include biodiversity conservation, landing site conditions, engineering geological conditions, seabed topography, submarine cable length, area occupied / affected by aquaculture areas, impact on maritime transportation, difficulty in coordinating with stakeholders, local government support, total investment estimate, compliance with relevant plans, area occupied / affected by ecological protection red lines, compliance with natural coastline control, spatial capacity, number of crossings with other pipelines, and onshore supporting conditions. Among them, the length of submarine cable, the area occupied / affected by aquaculture area, the total investment estimate, and the area occupied / affected by ecological protection red line are cost-type indicators, while the remaining 12 secondary indicators are benefit-type indicators.

[0008] In the preferred embodiment, the target layer is a comprehensive evaluation of long-distance submarine cable routes, and the criterion layer includes two layers: the first criterion layer is the primary indicator, and the second criterion layer is the secondary indicator.

[0009] In the preferred scheme, the long-distance submarine cable route scheme includes: the direction, length, specifications, and start and end points of the submarine cable route; Construct the decision matrix X for m evaluation schemes, expressed as: ; In the formula, m represents the number of alternative routing schemes, n represents the number of secondary indicators, and the matrix elements are... Let be the value of the j-th secondary metric under the i-th routing scheme; Cost-related indicators are calculated based on actual measurements of their meaning, while benefit-related indicators are assigned scores according to pre-set scoring standards.

[0010] In the preferred scheme, the analytic hierarchy process (AHP) is used to determine subjective weights, specifically including: The Saaty 1-9 scaling method is used to construct a judgment matrix, and the indicators at the same level are compared pairwise to construct an indicator judgment matrix. Perform a consistency check on the judgment matrix, and pass the check if the preset consistency ratio is met; Calculate and normalize the eigenvector corresponding to the largest eigenvalue of the judgment matrix to obtain the index weight of the current layer index on the corresponding element of the upper layer. The indicator weights are multiplied layer by layer to obtain the subjective weight of the secondary indicator to the target layer.

[0011] In the preferred scheme, the determination of objective weights using the entropy weight method specifically includes: The decision matrix is ​​normalized to its extreme values, as shown in the expression: ; in, A set of benefit-oriented indicators; A set of cost-based indicators; Let j be the minimum value of the j-th index among all possible solutions; The j-th index is the maximum value among all possible solutions; The weight of the i-th option under the j-th indicator is calculated using the following expression: ; Among them, if all under a certain indicator Then define ; The entropy value of each indicator is calculated using the following expression: ; in, m is the number of schemes; k is a constant. ;definition hour ; Calculate the coefficient of variation for the j-th indicator: ; Calculate entropy weights: .

[0012] In the preferred embodiment, the routing scenario characteristic parameters include at least one or more of the following: sea area risk level, route length, and number of pipeline intersections; The dynamic fusion is implemented using a machine learning model or a weighted average model, and the formula for calculating the combined weights is as follows: ; in, Let the combined weight of the j-th indicator be , Subjective weighting, For objective weighting, This is the fusion coefficient.

[0013] In the preferred scheme, the decision matrix is ​​normalized: ; in, ; The formula for calculating the elements of the weighted normalized decision matrix is ​​as follows: ; in, These are the original index values. For combined weights, These are standardized values.

[0014] In the preferred embodiment, the construction rules for the fuzzy positive ideal solution and the fuzzy negative ideal solution are as follows: For benefit-type dynamic time series indicators, triangular fuzzy number intervals are used to represent and generate fuzzy ideal solutions; for cost-type dynamic time series indicators and static indicators, deterministic values ​​are used to generate ideal solutions. Among them, triangular fuzzy numbers are used for dynamic time series indicators. The representation is performed, and the expected value method is used to defuzzify it as follows: ; In the formula, pessimistic value. The most likely value, This is an optimistic value; The expressions for positive and negative ideal solutions are: ; in, The ideal solution is dimensionless; The solution is a negative ideal solution, dimensionless. For a set of benefit-oriented indicators, A set of cost-based indicators; For combined weights; These are the upper and lower boundary markers of the scoring criteria range, respectively.

[0015] In the preferred embodiment, the formula for calculating the fuzzy vertical distance is: ; In the formula, Let be the difference vector between the i-th solution and the negative ideal solution. The vector connecting the positive and negative ideal solutions; The formula for the line connecting the positive and negative ideal solutions is: ; ; The difference vector between the proposed solution and the negative ideal solution is calculated using the following formula: ; ; The optimal routing scheme is based on vertical distance. The minimum solution is found when multiple solutions have equal vertical distances; in this case, Euclidean distance is used to assist in sorting.

[0016] This invention provides a method for comparing and selecting routes for long-distance submarine cables. By defining a comprehensive evaluation index system and stratifying it, a decision matrix is ​​constructed. Subjective weights are determined using AHP (Adaptive High Power) and objective weights using entropy weighting. Dynamic fusion is then used to obtain the combined weights of each index, resulting in a weighted normalized decision matrix. Finally, a triangular fuzzy number-orthogonal projection-improved TOPSIS (Topological Strategy Analysis) method is used to optimize the route selection. The route with the smallest fuzzy vertical distance is determined as the optimal route for the long-distance submarine cable. This method achieves scenario-adaptive fusion of subjective and objective weights, improves the accuracy of dynamic time-series indicators, avoids information redundancy caused by indicator correlation, and enhances the discriminative power and accuracy of route evaluation. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the routing comparison method of the present invention; Figure 2 This is a schematic diagram of the long-distance comprehensive evaluation structure of the present invention. Detailed Implementation

[0018] Example 1 like Figure 1-2 As shown, a method for comparing and selecting routes for long-distance submarine cables includes the following steps: S1: Taking long-distance submarine cable routes as the object, a comprehensive evaluation index system is defined; the comprehensive evaluation index system includes multiple primary indicators and multiple secondary indicators, some of which are cost-type indicators, and the rest are benefit-type indicators; S2: Combine the analytic hierarchy process to stratify the comprehensive evaluation indicators into target layer and criterion layer; S3: Determine multiple alternative routes for long-distance submarine cables and construct a decision matrix based on all alternative routes; S4: Use AHP (Analytic Hierarchy Process) to determine the subjective weight of each lower-level indicator relative to the upper-level indicator, and use the entropy weight method to determine the objective weight of the lower-level indicator relative to the upper-level indicator. S5: Based on the characteristic parameters of the routing scenario, subjective weights and objective weights are dynamically fused to obtain the combined weights of each indicator; S6: Normalize the decision matrix and combine it with the combined weights to obtain the weighted normalized decision matrix; S7: The improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm, which combines triangular fuzzy numbers and orthogonal projection, is used to optimize the routing scheme. Triangular fuzzy numbers are used to represent dynamic time-series indicators, while deterministic numerical values ​​are used to represent static indicators. Fuzzy positive ideal solutions and fuzzy negative ideal solutions are defined respectively. The vertical distance from each routing scheme to the line connecting the positive and negative ideal solutions is calculated, and the scheme with the smallest fuzzy vertical distance is determined as the optimal routing scheme for long-distance submarine cables.

[0019] S8: Perform critical engineering safety constraint verification on candidate optimal routing schemes. Safety constraints include at least one or more of the following: minimum bending radius of submarine cable, angle of intersection with other pipelines, and minimum burial depth requirement. If a candidate scheme meets all safety constraints, it is determined as the optimal routing scheme for long-distance submarine cables. If it does not meet the constraints, the next scheme is selected in order of vertical distance from smallest to largest for verification until a scheme that meets all safety constraints is selected.

[0020] Step S8 can also be performed after the alternative solutions are determined in S3 and before the decision matrix is ​​constructed: it is used to verify whether the alternative solutions participating in the comparison meet all security constraints.

[0021] In this embodiment, a decision matrix is ​​constructed by defining and stratifying a comprehensive evaluation index system. Subjective weights are determined using AHP, and objective weights are determined using the entropy weight method. The combined weights of each index are obtained through dynamic fusion, resulting in a weighted normalized decision matrix. Then, the TOPSIS is improved by triangular fuzzy number-orthogonal projection to complete the optimal routing scheme. The scheme with the smallest fuzzy vertical distance is determined as the optimal routing scheme for long-distance submarine cables. This achieves scenario-adaptive fusion of subjective and objective weights, improves the accuracy of dynamic time-series indicators, avoids information redundancy caused by indicator correlation, and improves the discrimination and accuracy of scheme evaluation.

[0022] In this embodiment, the comprehensive evaluation index system for long-distance submarine cable routes in step S1 includes 4 primary indicators and 16 secondary indicators, and its complete framework is shown in Table 1.

[0023] Step S2 divides the comprehensive evaluation index defined in step S1 into two layers: the target layer and the criterion layer.

[0024] In the preferred scheme, the ultimate goal of the target-level decision-making is a comprehensive evaluation of the long-distance submarine cable route. The criterion layer consists of two intermediate steps involved in achieving the goal: the first criterion layer comprises primary indicators, and the second criterion layer comprises secondary indicators.

[0025] In the preferred scheme, the comprehensive evaluation index system includes 4 primary indicators and 16 secondary indicators; The primary indicators include ecological environment, geology and engineering, socio-economic coordination, and spatial planning. Secondary indicators include biodiversity conservation, landing site conditions, engineering geological conditions, seabed topography, submarine cable length, area occupied / affected by aquaculture areas, impact on maritime transportation, difficulty in coordinating with stakeholders, local government support, total investment estimate, compliance with relevant plans, area occupied / affected by ecological protection red lines, compliance with natural coastline control, spatial capacity, number of crossings with other pipelines, and onshore supporting conditions. Among the 16 secondary indicators, four secondary indicators—submarine cable length, area occupied / affected by aquaculture, total investment estimate, and area occupied / affected by ecological protection red line—are cost-based indicators, with lower values ​​being better; the remaining 12 secondary indicators are benefit-based indicators, with higher values ​​being better.

[0026] In the application of the indicators "Area Occupied / Affected by Aquaculture Area" and "Area Occupied / Affected by Ecological Protection Red Line Area," a unified naming principle must be followed to ensure the consistency of the evidence chain. Specifically, the determination method is as follows: if at least one of the assessment schemes involves "occupation," then all schemes will uniformly use "Occupied…Area" as the indicator name; if none of the schemes involve occupation, then "Affected…Area" will uniformly be used as the indicator name.

[0027] A five-level qualitative scoring standard (1 to 5 points) is provided for the 12 benefit-related secondary indicators, corresponding to five levels: "Very Poor," "Poor," "Medium," "Good," and "Excellent." Each indicator is described in terms of its impact on the ecological environment, engineering geological risks, difficulty in coordinating socio-economic factors, and spatial planning compliance. 5 points (Excellent): Completely avoids key sensitive areas, has no adverse geological conditions, no coordination conflicts, fully complies with planning, and the impact is negligible.

[0028] 4 points (Good): Minor or controllable impact, requiring routine measures to handle.

[0029] 3 points (Medium): Foreseeable moderate interference, requiring a specific plan or compensation measures.

[0030] 2 points (poor): Significant negative impact, difficult to coordinate and high risk.

[0031] 1 point (Very poor): Severe damage, project is not feasible or cannot be coordinated.

[0032] The above scoring criteria are used to convert qualitative judgments into quantitative scores, which serve as input data for benefit-type indicators.

[0033] In this embodiment, the benefit index is assigned a score of 1-5. The theoretical extreme value of the scheme is 5 points for optimal and 1 point for worst. The result after vector normalization is used as the positive / negative ideal solution.

[0034] This embodiment constructs a comprehensive evaluation system covering ecology, geology, socio-economic factors, and spatial planning, which improves the economic efficiency and environmental and social impact of the project and provides quantitative decision-making data for the selection of long-distance submarine cable routes.

[0035] Table 1. Comprehensive Evaluation Index System for Long-Distance Submarine Cable Routes

[0036] In the preferred scheme, step S3, the long-distance submarine cable route scheme, includes: the direction, length, specifications, and starting and ending points of the submarine cable route (land-based control center / substation — submarine cable landing point — offshore wind farm booster station).

[0037] Construct a decision matrix X for m evaluation schemes, where each scheme has n secondary indicators, and the corresponding indicator values ​​are... : ; In the formula, m represents the number of schemes, n represents the number of secondary indicators, and matrix elements... This is the calculated or scored value of the j-th secondary indicator under the i-th routing scheme.

[0038] Specifically, the four cost-related indicators of decision matrix X are calculated according to the requirements of Table 1, and the twelve benefit-related indicators are assigned scores and grades according to the rating standard table for benefit-related indicators in the route scheme evaluation.

[0039] This embodiment makes it possible to compare indicators of different units, thereby improving the accuracy of data calculation.

[0040] In the preferred scheme, step S4 involves using Analytic Hierarchy Processing (AHP) to determine the subjective weights of each lower-level indicator relative to the upper-level indicator. This specifically includes: Based on the experts' experience, the Saaty 1-9 scale method was used to compare the importance of each indicator relative to the corresponding element at the upper level, and a comparison matrix was established as the judgment matrix, as shown in Table 2.

[0041] Table 2 Standards for Assigning Scale Values ​​to Matrix Judgments

[0042] A consistency check is performed. If the consistency ratio is ≤0.1, the judgment matrix passes the consistency check. Otherwise, the judgment matrix needs to be adjusted until it meets the consistency requirements, as follows.

[0043] Determine the largest eigenvalue of the matrix. The feature vectors are normalized to obtain the ranking of the importance of the current layer's indicators to the upper layer's elements.

[0044] Calculate the consistency indices CI, RI, and consistency ratio CR of the judgment matrix to verify the consistency of the judgment matrix; the formulas for calculating the consistency indices CI, RI, and CR are as follows: ; in, Construct 1000 sample matrices using a random method, and randomly select numbers from 1 to 15 and their reciprocals to construct the average of the largest eigenvalues ​​of the positive reciprocal matrix.

[0045] When the consistency ratio CR If the value is less than 0.1, the judgment matrix passes the consistency check; otherwise, the judgment matrix needs to be adjusted until it meets the consistency requirements.

[0046] After the judgment matrix passes the consistency test, the eigenvector corresponding to the largest eigenvalue of the judgment matrix is ​​calculated. After normalizing each element of the eigenvector, the index weight of this layer index on the corresponding element of the upper layer is obtained.

[0047] The obtained indicator weights are multiplied layer by layer to finally obtain the indicator weights of the secondary indicators to the target layer.

[0048] In the preferred scheme, the entropy weight method is used to determine the weight of the defined lower-level indicators relative to the upper-level indicators, specifically including: The decision matrix is ​​normalized to its extreme values, as shown in the expression: ; in, It is a set of benefit-oriented indicators, dimensionless, with larger values ​​being better; It is a set of cost-type indicators, dimensionless, and the smaller the value, the better; Let j be the minimum value of the j-th index among all possible solutions; Let j be the maximum value of the j-th index among all possible solutions.

[0049] Calculate the weight of the i-th option under the j-th indicator, expressed as: ; Among them, if all under a certain indicator Then define ; The entropy value of each indicator is calculated using the following expression: ; in, m is the number of schemes; k is a constant. ,definition hour .

[0050] Calculate the coefficient of variation for the j-th indicator: ; Calculate entropy weights: .

[0051] This embodiment provides a standardized expert weighting process, which improves the logical consistency and verifiability of subjective weights; it avoids human bias and is applicable to submarine cable route comparison scenarios with multiple options and multiple indicators, thus improving the applicability of the method to various scenarios.

[0052] In the preferred embodiment, in step S5, the routing scenario characteristic parameters include at least one or more of the following: sea area risk level, route length, and number of pipeline intersections. Dynamic fusion is achieved using a machine learning model or a weighted average model. The formula for calculating the combined weights is as follows: ; in, For combined weights, Subjective weighting, For objective weighting, This is the fusion coefficient, which ranges from [0,1] and is dynamically adjusted according to the routing scenario.

[0053] This embodiment is based on route length L (unit: km), number of intersections with other pipelines C (unit: times), and ecological sensitivity zone level E (dimensionless, with values ​​of 1, 2, and 3, representing low, medium, and high sensitivity, respectively). Determine the function: ; In the formula, , The maximum and minimum route lengths among all alternative options; , The maximum and minimum number of crossovers among all alternatives. This area is classified as an ecologically sensitive zone. In this embodiment, .

[0054] This embodiment realizes dynamic adaptation of the weight fusion strategy to the routing scenario, improving the model's generalization ability and robustness to different sea areas and engineering backgrounds.

[0055] In the preferred scheme, step S6 involves normalizing the decision matrix: ; in, ; The formula for calculating the elements of the weighted normalized decision matrix is: ; in, These are the original index values. For combined weights, These are standardized values.

[0056] Furthermore, triangular fuzzy numbers are used for dynamic time-series indicators. The representation is performed, and the expected value method is used to defuzzify it as follows: ; In the formula, This is the pessimistic value (lower limit). The most likely value (median). The optimistic value (upper limit) can be determined through historical data.

[0057] The defuzzed values ​​are substituted into the decision matrix and used for subsequent normalization and weighted calculations.

[0058] In the preferred scheme, the construction rules for the fuzzy positive ideal solution and the fuzzy negative ideal solution in step S7 are as follows: For benefit-type dynamic time series indicators, triangular fuzzy number intervals are used to represent and generate fuzzy ideal solutions; for cost-type dynamic time series indicators and static indicators, deterministic values ​​are used to generate ideal solutions. The expressions for positive and negative ideal solutions are: ; in, The ideal solution is dimensionless; The solution is a negative ideal solution, dimensionless. For a set of benefit-oriented indicators, A set of cost-based indicators; For combined weights; These are the upper and lower boundary markers of the scoring criteria range, respectively.

[0059] In this embodiment, the vector normalization results in Table 1 are used as the positive / negative ideal solutions, with a=5 and b=1: .

[0060] This embodiment improves the distinguishability of indicator types and sources of uncertainty, reduces the information distortion rate of forced fuzzification, and enhances the engineering rationality of the ideal solution.

[0061] In the preferred scheme, the formula for calculating the vertical distance is: ; In the formula, Let be the difference vector between the i-th solution and the negative ideal solution. The vector connecting the positive and negative ideal solutions; The formula for the line connecting the positive and negative ideal solutions is: ; ; The difference vector between the proposed solution and the negative ideal solution is calculated using the following formula: ; ; The optimal routing scheme is based on vertical distance. The minimum solution is found when multiple solutions have equal vertical distances; in this case, Euclidean distance is used to assist in sorting.

[0062] This embodiment improves the matching degree between the scheme and the ideal solution, enhances the reliability and interpretability of the sorting results, and ensures the uniqueness of the sorting.

[0063] Step S8: Verification of critical engineering safety constraints includes: Minimum bending radius constraint: Actual bending radius of the cable It should be greater than or equal to the design minimum bending radius. ; Pipeline crossing angle constraints: Crossing angle with other pipelines It should be greater than or equal to the minimum allowable intersection angle. (Usually not less than 30°); Minimum burial depth constraint: cable burial depth It should be greater than or equal to the minimum design burial depth. (Determined based on maritime shipping activities and geological conditions) If a candidate solution satisfies all of the above constraints, it is determined to be the optimal routing solution; otherwise, the next solution is checked in ascending order of vertical distance until a solution that satisfies all constraints is selected; if none of the solutions are satisfied, a constraint conflict report is output, indicating that the routing solution needs to be replanned.

[0064] This embodiment avoids selecting a theoretically superior but practically infeasible solution, thus improving the engineering feasibility and practicality of the comparison results.

[0065] To better illustrate the technical solution of this embodiment, taking the route selection of a long-distance submarine cable in a certain project as an example, a hierarchical model is constructed, the index weights are determined, and the optimal route is determined by calculating the closeness of each route scheme to the ideal scheme. Specifically, this includes: 1) Establish a hierarchical model. Through data research and questionnaire surveys, the target layer—the criterion layer—was ultimately determined, such as... Figure 2 As shown. The importance of each indicator should be determined based on the actual situation of the project.

[0066] 2) Construct a judgment matrix and perform hierarchical single sorting and consistency checks.

[0067] Based on the Saaty1-9 scaling method, the judgment matrix of the criterion layer to the target layer and the judgment matrix of the index layer under each criterion layer were constructed and consistency tests were performed, as shown in Tables 3-6.

[0068] Table 3 Criterion Layer Judgment Matrix AB

[0069] Table 4 Geological and Engineering Judgment Matrix B2-C2

[0070] Table 5 Socioeconomic and Coordination Judgment Matrix B3-C3

[0071] Table 6 Spatial and Planning Judgment Matrix B4-C4

[0072] 3) Multiply the obtained indicator weights layer by layer to finally obtain the indicator weights of the secondary indicators to the target layer.

[0073] Table 7 Evaluation Indicators and Weights

[0074] 4) Construct the decision matrix as follows: .

[0075] 5) Determine the weights using the entropy weight method.

[0076] .

[0077] 6) Determine the weights of the indicator attribute combinations: 0.0296, 0.0932, 0.0053, 0.0662, 0.0512, 0.0523, 0.0155, 0.0807, 0.1287, 0.0643, 0.1177, 0.0651, 0.0270, 0.1029, 0.0097, 0.0907 .

[0078] 7) The standardized decision matrix is ​​obtained through normalization. The standardized decision matrix is ​​combined with the indicator attribute weights to obtain the weighted normalized decision matrix. The positive and negative ideal solutions of each evaluation indicator are determined. The vertical distance from each routing scheme to the line connecting the positive and negative ideal solutions is calculated and sorted. The routing scheme with the smallest vertical distance is determined as the optimal routing scheme for long-distance submarine cables, as shown in Table 8.

[0079] Table 8 Optimal Routing Scheme

[0080] The sorting result is Therefore, submarine cable route scheme 1 is selected as the optimal route scheme.

[0081] This invention achieves scene adaptive fusion of subjective and objective weights, effective processing of fuzzy time-series indicators, improved TOPSIS ranking mechanism, and explicit verification of engineering safety constraints, significantly improving the scientificity, adaptability, and engineering reliability of long-distance submarine cable route selection.

[0082] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for comparing and selecting routes for long-distance submarine cables, characterized in that, Includes the following steps: Taking long-distance submarine cable routes as the object, a comprehensive evaluation index system is defined; the comprehensive evaluation index system includes multiple primary indicators and multiple secondary indicators, some of which are cost-type indicators, and the rest are benefit-type indicators; The comprehensive evaluation index is divided into two layers using the analytic hierarchy process: the target layer and the criteria layer. Multiple alternative routes for long-distance submarine cables were identified, and a decision matrix was constructed based on all alternative routes. The analytic hierarchy process (AHP) was used to determine the subjective weights of each lower-level indicator relative to the upper-level indicator, and the entropy weight method was used to determine the objective weights of each lower-level indicator relative to the upper-level indicator. Based on the characteristic parameters of the routing scenario, the subjective weights and objective weights are dynamically fused to obtain the combined weights of each indicator; The decision matrix is ​​normalized and combined with the combined weights to obtain a weighted normalized decision matrix; A hybrid improved TOPSIS algorithm using triangular fuzzy numbers and orthogonal projection is adopted to optimize the routing scheme: dynamic time-series indicators are represented by triangular fuzzy numbers, while static indicators are represented by deterministic values. Fuzzy positive ideal solutions and fuzzy negative ideal solutions are defined respectively. The fuzzy vertical distance from each routing scheme to the line connecting the positive and negative ideal solutions is calculated, and the scheme with the best fuzzy vertical distance or the best comprehensive evaluation is determined as the optimal routing scheme for long-distance submarine cables.

2. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The comprehensive evaluation index system includes 4 primary indicators and 16 secondary indicators; The primary indicators include ecological environment, geology and engineering, socio-economic coordination, and spatial planning; The secondary indicators include biodiversity conservation, landing site conditions, engineering geological conditions, seabed topography, submarine cable length, area occupied / affected by aquaculture areas, impact on maritime transportation, difficulty in coordinating with stakeholders, local government support, total investment estimate, compliance with relevant plans, area occupied / affected by ecological protection red lines, compliance with natural coastline control, spatial capacity, number of crossings with other pipelines, and onshore supporting conditions. Among them, the length of submarine cable, the area occupied / affected by aquaculture area, the total investment estimate, and the area occupied / affected by ecological protection red line are cost-type indicators, while the remaining 12 secondary indicators are benefit-type indicators.

3. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The target layer is a comprehensive evaluation of long-distance submarine cable routes, and the criterion layer includes two layers: the first criterion layer is the primary indicator, and the second criterion layer is the secondary indicator.

4. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, Long-distance submarine cable routing scheme, including: cable route direction, length, specifications, and start and end points; Construct the decision matrix X for m evaluation schemes, expressed as: ； In the formula, m represents the number of alternative routing schemes, n represents the number of secondary indicators, and the matrix elements are... Let be the value of the j-th secondary metric under the i-th routing scheme; Cost-related indicators are calculated based on actual measurements of their meaning, while benefit-related indicators are assigned scores according to pre-set scoring standards.

5. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The subjective weights are determined using the analytic hierarchy process (AHP), specifically including: The Saaty 1-9 scaling method is used to construct a judgment matrix, and the indicators at the same level are compared pairwise to construct an indicator judgment matrix. Perform a consistency check on the judgment matrix; the check passes when the preset consistency ratio is met. Calculate and normalize the eigenvector corresponding to the largest eigenvalue of the judgment matrix to obtain the index weight of the current layer index on the corresponding element of the upper layer. The indicator weights are multiplied layer by layer to obtain the subjective weight of the secondary indicator to the target layer.

6. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The determination of objective weights using the entropy weight method specifically includes: The decision matrix is ​​normalized to its extreme values, as shown in the expression: ； in, A set of benefit-oriented indicators; A set of cost-based indicators; Let j be the minimum value of the j-th index among all possible solutions; The j-th index is the maximum value among all possible solutions; The weight of the i-th option under the j-th indicator is calculated using the following expression: ； Among them, if all under a certain indicator Then define ; The entropy value of each indicator is calculated using the following expression: ； in, m is the number of schemes; k is a constant. ;definition hour ; Calculate the coefficient of variation for the j-th indicator: ； Calculate entropy weights: 。 7. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The routing scenario characteristic parameters include at least one or more of the following: sea area risk level, route length, and number of pipeline intersections; The dynamic fusion is implemented using a machine learning model or a weighted average model, and the formula for calculating the combined weights is as follows: ； in, Let the combined weight of the j-th indicator be , Subjective weighting, For objective weighting, This is the fusion coefficient.

8. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, Normalize the decision matrix: ； in, ; The formula for calculating the elements of the weighted normalized decision matrix is ​​as follows: ； in, These are the original indicator values. For combined weights, These are standardized values.

9. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The construction rules for the fuzzy positive ideal solution and the fuzzy negative ideal solution are as follows: For benefit-type dynamic time series indicators, triangular fuzzy number intervals are used to represent and generate fuzzy ideal solutions; for cost-type dynamic time series indicators and static indicators, deterministic values ​​are used to generate ideal solutions. Among them, triangular fuzzy numbers are used for dynamic time series indicators. The representation is performed, and the expected value method is used to defuzzify it as follows: ； In the formula, pessimistic value. The most likely value, This is an optimistic value; The expressions for positive and negative ideal solutions are: ； in, The ideal solution is dimensionless; The solution is a negative ideal solution, dimensionless. For a set of benefit-oriented indicators, A set of cost-based indicators; For combined weights; These are the upper and lower boundary markers of the scoring criteria range, respectively.

10. The method for comparing and selecting long-distance submarine cable routes according to claim 1, characterized in that, The formula for calculating the fuzzy vertical distance is: ； In the formula, Let be the difference vector between the i-th solution and the negative ideal solution. The vector connecting the positive and negative ideal solutions; The formula for the line connecting the positive and negative ideal solutions is: ； ； The difference vector between the proposed solution and the negative ideal solution is calculated using the following formula: ； ； The optimal routing scheme is based on vertical distance. The minimum solution is found when multiple solutions have equal vertical distances; in this case, Euclidean distance is used to assist in sorting.

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