Multi-resource combination constrained plan-oriented two-point automatic path-finding method and system

Abstract

The invention discloses a multi-resource combination constrained plan-oriented two-point automatic path-finding method and system, belongs to the technical field of path planning, and aims to solve the technical problem that the prior art is disjointed from engineering practical application. Comprising the following steps: acquiring geographic coordinates, construction plan indexes and resource state data as input data; forming a multi-resource combination graph model comprising a node network, edges, a resource composition scene and constraint parameters; calculating the comprehensive weight of each resource combination scene of each edge through a plan-oriented cost function; constructing an improved Dijkstra algorithm introducing constraint screening, screening the edges and the corresponding resource combination scenes in the multi-resource combination graph model through the improved Dijkstra algorithm based on the comprehensive weight of each resource combination scene of each edge, and selecting an initial path scheme which meets a constraint condition and has the minimum comprehensive weight to form an initial path scheme; and the optimal path is solved again by improving the Dijkstra algorithm.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, specifically to a plan-oriented two-point automatic pathfinding method and system with multiple resource combination constraints. Background Technology

[0002] In linear engineering projects such as the expansion of communication backbone networks, the renovation of municipal pipelines, and the laying of power transmission lines, path planning between two points is a core element, and it is necessary to coordinate the allocation of two key resources, namely pipelines and optical cables, simultaneously. There is a need for flexible selection between utilizing existing resources or building new resources.

[0003] Current path planning technologies are mainly divided into two categories: one is general path planning algorithms (such as Dijkstra's algorithm and A* algorithm), whose core goal is to find the "shortest path" and is widely used in navigation, logistics and transportation scenarios. However, these algorithms only focus on optimizing the path length in the geospatial dimension and do not consider the constraints of multiple resource combinations in engineering construction. They cannot distinguish between the reuse and new construction attributes of pipelines / fiber optic cables, resulting in the output path being out of touch with actual engineering needs. The other category is engineering-specific path planning technologies. Although some of them involve resource constraints, they are mostly only for a single resource (such as only considering pipelines or only considering fiber optic cables). They do not achieve a suitable combination of reuse and new construction of pipelines and fiber optic cables, and they do not incorporate the core indicators of the construction plan (such as budget, cycle, and reuse priority), making it difficult to meet the comprehensive requirements of engineering construction.

[0004] The existing technology has the following significant shortcomings:

[0005] 1. Lack of suitable matching of multiple resource combinations: The four combination scenarios of pipelines (reuse / new construction) and optical cables (reuse / new construction) are not included in the unified routing model, which makes it impossible to flexibly match the optimal combination scheme according to resource availability, resulting in low utilization of reused resources or excessively high new construction costs.

[0006] 2. Poor coordination of construction plans: The pathfinding objective is singular (such as only pursuing the shortest length) and does not integrate construction plan indicators such as budget, cycle, and priority of reusing existing equipment. This results in the path plan needing to be manually adjusted twice to meet the project requirements, which is inefficient.

[0007] 3. Insufficient dynamic adaptation capability: Existing algorithms are mostly "one-time pathfinding" and cannot synchronize changes in the status of existing resources (such as accelerated aging or insufficient capacity) or adjustments to construction plans (such as budget changes or cycle compression) in real time, which limits the practicality and timeliness of the path solutions.

[0008] 4. Poor project implementation and coordination: The output only includes path coordinates and does not specify the resource type (reuse / new construction) and related construction parameters (cost, cycle, risk) for each segment. Additional manpower is required to organize and coordinate the data before it can be integrated with the construction execution phase.

[0009] The technical problem that needs to be solved is how to provide an automatic pathfinding algorithm that can couple multiple resource constraints, meet the needs of construction plans, and have dynamic optimization capabilities, so as to solve the problem of the disconnect between existing technologies and practical engineering applications. Summary of the Invention

[0010] The technical objective of this invention is to address the above-mentioned shortcomings by providing a plan-oriented two-point automatic pathfinding method and system with multiple resource combination constraints, thereby solving the technical problem of the disconnect between existing technology and practical engineering applications.

[0011] In a first aspect, the present invention provides a plan-oriented automatic two-point pathfinding method with multi-resource combination constraints, comprising the following steps:

[0012] Input parameter acquisition: Geographic coordinates, construction plan indicators, and resource status data are acquired as input data, and the input data is validated. A dataset is constructed based on the validated input data. Geographic coordinates include the starting point coordinates and the ending point coordinates. Construction plan indicators include budget thresholds, construction period upper limits, reuse priority weights, construction and maintenance cost weights, construction period weights, path length weights, and geographic restriction boundaries. Resource status data includes the location information, available capacity, and aging degree of existing pipelines and optical cables, as well as the geographic restriction boundaries and approval period standards for new pipelines and optical cables.

[0013] Construction of a multi-resource combination graph model: Using geographic coordinate points as nodes, a node network covering the start and end points is selected, and the accessible paths between two nodes are used as edges. Each edge is associated with a resource combination scenario, and constraint parameters are configured for each resource combination scenario and a dynamic update mechanism for the constraint parameters is set to form a multi-resource combination graph model including node network, edges, resource composition scenarios, and constraint parameters.

[0014] Edge weight calculation: Using path length, construction and maintenance costs, construction period, and resource utilization rate as indicators, the weight coefficients of the indicators are adjusted based on the construction plan indicators. A plan-oriented cost function is constructed based on the weighted sum of the indicators. The comprehensive weight of each edge for each resource combination scenario is calculated through the plan-oriented cost function.

[0015] Optimal path solution: An improved Dijkstra algorithm with constraint screening is constructed. Based on the comprehensive weight of each resource combination scenario for each edge, the improved Dijkstra algorithm is used to screen the edges and corresponding resource combination scenarios in the multi-resource combination graph model, and select the initial path scheme with the minimum comprehensive weight that satisfies the constraint conditions to form the initial path scheme.

[0016] Dynamic iterative optimization: Real-time synchronization of resource status data and construction plan indicators. When resource status data or construction plan indicators are adjusted, path recalculation is triggered. The optimal path is re-solved by improving the Dijkstra algorithm, and the optimized path and resource usage list are output.

[0017] As a preferred approach, the construction of a multi-resource combination graph model includes the following operations:

[0018] Node generation: Based on geographic coordinates, the node density is configured according to the region type to generate a node network covering the region from the starting point to the ending point.

[0019] Edge definition: The accessible paths between nodes are used as edges, and each edge is associated with a combination of resource scenarios. There are four types of resource scenarios associated with each edge: reuse of existing pipelines and reuse of existing optical cables, reuse of existing pipelines and newly built optical cables, newly built pipelines and reuse of existing optical cables, and newly built pipelines and newly built optical cables.

[0020] Constraint parameter configuration: Configure constraint parameters for each resource combination scenario and establish a constraint parameter update mechanism. The constraint parameters include the availability threshold of existing resources, the construction cost coefficient of new resources, the approval cycle coefficient, the maintenance cost coefficient of existing pipelines and optical cables, and the environmental assessment threshold of new resources. The constraint parameter update mechanism includes quarterly synchronization of parameters related to existing resources and monthly synchronization of parameters related to the use of new resources.

[0021] Edge validity determination: In addition to geographical constraints, a new resource reachability determination is added. A path segment must support at least one pipeline-optical cable combination scenario and meet the constraint parameters; otherwise, it is considered an invalid edge and will not be included in the model.

[0022] As a preferred option, the expression for the planning-oriented cost function is:

[0023] W = α*L + β*C + γ*T + δ*U;

[0024] Where L represents the path length, α represents the weight coefficient of L, C represents the construction and maintenance cost, β represents the weight coefficient of C, T represents the construction period, γ represents the weight coefficient of T, U represents the resource utilization rate, δ represents the weight coefficient of U, and α+β+γ+δ=1.

[0025] When adjusting the weight coefficients of indicators based on construction plan indicators, a basic weight template is selected according to the core objectives of the construction plan indicators, and the adjustment range is determined by fuzzy hierarchical analysis. The corrected value of the weight coefficient is calculated by inputting the specific constraint thresholds of the current project. The constraint thresholds include budget deviation and cycle compression.

[0026] There are three types of basic weight templates: a cost-priority template for projects with tight budgets, a cycle-priority template for projects with urgent construction needs, and a reuse-priority template for projects with high resource reuse requirements. Different weight coefficients are set for α, β, γ, and δ in the three types of basic weight templates.

[0027] As a preferred method, the edges and corresponding resource combination scenarios in the multi-resource combination graph model are filtered based on the comprehensive weight of each resource combination scenario for each edge, using an improved Dijkstra algorithm, including the following steps:

[0028] Initialization: Initialize the weight of the starting node to 0, and the weights of other nodes to infinity, and build a priority queue;

[0029] Constraint filtering: A three-stage filtering process is performed by introducing a constraint filtering layer in the improved Dijkstra algorithm. Edges that do not meet the constraints and their corresponding resource combinations are eliminated through basic constraint detection, resource constraint detection, and plan constraint detection.

[0030] Priority calculation and path update: The priority queue is optimized based on dual-key sorting. The node with the smallest weight is selected from the priority queue in turn, and the path weight of the adjacent nodes is updated. In dual-key sorting, the primary key is the comprehensive weight W, and the secondary key is the resource utilization rate U.

[0031] Path pruning: Set an optimal path deviation threshold. When the combined weight of the candidate paths for a node exceeds the deviation threshold of the current optimal path weight, the candidate path is pruned directly.

[0032] Termination judgment: The calculation terminates when the destination is removed from the priority queue and the weights of all reachable destinations exceed the deviation threshold of the optimal path weight. The output includes the sequence of nodes corresponding to the path and the initial path scheme of the resource combination scenario of each segment.

[0033] As a preferred approach, dynamic iterative optimization includes the following operations:

[0034] Real-time synchronization: Through dual listening interfaces, it connects to the resource status database and the construction plan management platform respectively, and synchronizes the updates of resource status data and construction plan indicators in real time;

[0035] Trigger judgment: When the change in resource status exceeds the threshold or the adjustment of the construction plan exceeds the threshold, the departure path is recalculated;

[0036] Recalculation optimization: Update the weights of relevant edges and re-solve for the optimal path using an improved Dijkstra algorithm;

[0037] Verification output: Verify the constraints of the recalculated route to ensure that it meets the requirements of the current construction plan and resource status;

[0038] Output results: The optimized path and resource usage list will be output as the results. The resource usage list includes the type of pipeline and optical cable for each path segment, cost estimate, construction period breakdown and risk level.

[0039] In a second aspect, the present invention provides a plan-oriented two-point automatic pathfinding system with multi-resource combination constraints, comprising an input parameter acquisition module, a multi-resource combination graph model construction module, an edge weight calculation module, an optimal path solution module, and a dynamic iterative optimization module;

[0040] The input parameter acquisition module is used to perform the following: acquire geographic coordinates, construction plan indicators, and resource status data as input data, and validate the input data. Based on the validated input data, a dataset is constructed. The geographic coordinates include the starting point coordinates and the ending point coordinates. The construction plan indicators include budget thresholds, construction period upper limits, reuse priority weights, construction and maintenance cost weights, construction period weights, path length weights, and geographic restriction boundaries. The resource status data includes the location information, available capacity, and aging degree of existing pipelines and optical cables, as well as the geographic restriction boundaries and approval period standards for new pipelines and optical cables.

[0041] The multi-resource combination graph model construction module is used to perform the following: taking geographic coordinate points as nodes, selecting a node network covering the start and end points, taking the accessible path between two nodes as edges, associating resource combination scenarios with each edge, configuring constraint parameters for each resource combination scenario and setting a dynamic update mechanism for constraint parameters, forming a multi-resource combination graph model including node network, edges, resource composition scenarios and constraint parameters;

[0042] The edge weight calculation module is used to perform the following: using path length, construction and maintenance cost, construction period and resource utilization rate as indicators, adjust the weight coefficients of the indicators based on the construction plan indicators, construct a plan-oriented cost function based on the weighted sum of the indicators, and calculate the comprehensive weight of each edge for each resource combination scenario through the plan-oriented cost function;

[0043] The optimal path solution module is used to perform the following: construct an improved Dijkstra algorithm with constraint screening, and screen the edges and corresponding resource combination scenarios in the multi-resource combination graph model based on the comprehensive weight of each edge and each resource combination scenario, and select the initial path scheme with the smallest comprehensive weight that satisfies the constraint conditions to form the initial path scheme.

[0044] The dynamic iterative optimization module is used to perform the following: real-time synchronization of resource status data and construction plan indicators; when resource status data or construction plan indicators are adjusted, path recalculation is triggered; the optimal path is re-solved using an improved Dijkstra algorithm; and the optimized path and resource usage list are output.

[0045] As a preferred option, the multi-resource combination graph model building module is used to perform the following operations:

[0046] Node generation: Based on geographic coordinates, the node density is configured according to the region type to generate a node network covering the region from the starting point to the ending point.

[0047] Edge definition: The accessible paths between nodes are used as edges, and each edge is associated with a combination of resource scenarios. There are four types of resource scenarios associated with each edge: reuse of existing pipelines and reuse of existing optical cables, reuse of existing pipelines and newly built optical cables, newly built pipelines and reuse of existing optical cables, and newly built pipelines and newly built optical cables.

[0048] Constraint parameter configuration: Configure constraint parameters for each resource combination scenario and establish a constraint parameter update mechanism. The constraint parameters include the availability threshold of existing resources, the construction cost coefficient of new resources, the approval cycle coefficient, the maintenance cost coefficient of existing pipelines and optical cables, and the environmental assessment threshold of new resources. The constraint parameter update mechanism includes quarterly synchronization of parameters related to existing resources and monthly synchronization of parameters related to the use of new resources.

[0049] Edge validity determination: In addition to geographical constraints, a new resource reachability determination is added. A path segment must support at least one pipeline-optical cable combination scenario and meet the constraint parameters; otherwise, it is considered an invalid edge and will not be included in the model.

[0050] As a preferred option, the expression for the planning-oriented cost function is:

[0051] W = α*L + β*C + γ*T + δ*U;

[0052] Where L represents the path length, α represents the weight coefficient of L, C represents the construction and maintenance cost, β represents the weight coefficient of C, T represents the construction period, γ represents the weight coefficient of T, U represents the resource utilization rate, δ represents the weight coefficient of U, and α+β+γ+δ=1.

[0053] When adjusting the weight coefficients of indicators based on construction plan indicators, a basic weight template is selected according to the core objectives of the construction plan indicators, and the adjustment range is determined by fuzzy hierarchical analysis. The corrected value of the weight coefficient is calculated by inputting the specific constraint thresholds of the current project. The constraint thresholds include budget deviation and cycle compression.

[0054] There are three types of basic weight templates: a cost-priority template for projects with tight budgets, a cycle-priority template for projects with urgent construction needs, and a reuse-priority template for projects with high resource reuse requirements. Different weight coefficients are set for α, β, γ, and δ in the three types of basic weight templates.

[0055] As a preferred method, the edges and corresponding resource combination scenarios in the multi-resource combination graph model are filtered based on the comprehensive weight of each resource combination scenario for each edge, using an improved Dijkstra algorithm, including the following steps:

[0056] Initialization: Initialize the weight of the starting node to 0, and the weights of other nodes to infinity, and build a priority queue;

[0057] Constraint filtering: A three-stage filtering process is performed by introducing a constraint filtering layer in the improved Dijkstra algorithm. Edges that do not meet the constraints and their corresponding resource combinations are eliminated through basic constraint detection, resource constraint detection, and plan constraint detection.

[0058] Priority calculation and path update: The priority queue is optimized based on dual-key sorting. The node with the smallest weight is selected from the priority queue in turn, and the path weight of the adjacent nodes is updated. In dual-key sorting, the primary key is the comprehensive weight W, and the secondary key is the resource utilization rate U.

[0059] Path pruning: Set an optimal path deviation threshold. When the combined weight of the candidate paths for a node exceeds the deviation threshold of the current optimal path weight, the candidate path is pruned directly.

[0060] Termination judgment: The calculation terminates when the destination is removed from the priority queue and the weights of all reachable destinations exceed the deviation threshold of the optimal path weight. The output includes the sequence of nodes corresponding to the path and the initial path scheme of the resource combination scenario of each segment.

[0061] As a preferred option, the dynamic iterative optimization module is used to perform the following operations:

[0062] Real-time synchronization: Through dual listening interfaces, it connects to the resource status database and the construction plan management platform respectively, and synchronizes the updates of resource status data and construction plan indicators in real time;

[0063] Trigger judgment: When the change in resource status exceeds the threshold or the adjustment of the construction plan exceeds the threshold, the departure path is recalculated;

[0064] Recalculation optimization: Update the weights of relevant edges and re-solve for the optimal path using an improved Dijkstra algorithm;

[0065] Verification output: Verify the constraints of the recalculated route to ensure that it meets the requirements of the current construction plan and resource status;

[0066] Output results: The optimized path and resource usage list will be output as the results. The resource usage list includes the type of pipeline and optical cable for each path segment, cost estimate, construction period breakdown and risk level.

[0067] The multi-resource combination constraint-based plan-oriented two-point automatic pathfinding method and system of the present invention has the following advantages:

[0068] 1. Achieve intelligent adaptation of multiple resource combinations: Incorporate four pipeline-optical cable combination scenarios into a unified model, significantly improve the utilization rate of existing resources, reduce new construction costs, and improve resource utilization rate by ≥80% compared with traditional algorithms;

[0069] 2. Deeply aligned with the construction plan: Through a cost function with configurable weights, integrating indicators such as budget, cycle, and reuse priority, the path plan does not require manual secondary adjustments, achieving a 100% alignment.

[0070] 3. Dynamically adapt to changing scenarios: Real-time synchronization of resource status and construction plan updates, automatic path recalculation, overcoming the limitations of traditional algorithms that only perform "one-time pathfinding";

[0071] 4. Simplify project implementation process: While outputting path coordinates, a resource usage list and construction parameters are generated simultaneously, directly connecting with construction execution and reducing manual coordination costs by ≥60%. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] The invention will be further described below with reference to the accompanying drawings.

[0074] Figure 1 This is a flowchart of a plan-oriented two-point automatic pathfinding method with multiple resource combination constraints, as described in Example 1. Detailed Implementation

[0075] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0076] This invention provides a plan-oriented two-point automatic pathfinding method and system with multiple resource combination constraints, which is used to solve the technical problem of the disconnect between existing technology and practical engineering applications.

[0077] Example 1:

[0078] This invention provides a plan-oriented automatic two-point pathfinding method with multi-resource combination constraints, comprising five steps: input parameter acquisition, multi-resource combination graph model construction, edge weight calculation, optimal path solution, and dynamic iterative optimization.

[0079] Step S100 Input Parameter Acquisition: Acquire geographic coordinates, construction plan indicators, and resource status data as input data, and verify the input data. Construct a dataset based on the verified input data. The geographic coordinates include the starting point coordinates and the ending point coordinates. The construction plan indicators include budget thresholds, construction period upper limits, reuse priority weights, construction and maintenance cost weights, construction period weights, path length weights, and geographic restriction boundaries. The resource status data includes the location information, available capacity, and aging degree of existing pipelines and optical cables, as well as the geographic restriction boundaries and approval period standards for new pipelines and optical cables.

[0080] Step S200: Construction of a multi-resource combination graph model: Using geographic coordinate points as nodes, select a node network covering the start and end points, use the accessible paths between two nodes as edges, associate each edge with a resource combination scenario, configure constraint parameters for each resource combination scenario, and set a dynamic update mechanism for the constraint parameters to form a multi-resource combination graph model including node network, edges, resource composition scenarios, and constraint parameters.

[0081] As a specific implementation of constructing a multi-resource combination graph model, this step includes the following operations:

[0082] (1) Node generation: Based on geographic coordinates, the node density is configured according to the region type to generate a node network covering the region from the starting point to the end point. When configuring nodes, the node spacing in the core area (such as the urban built-up area) is ≤500m, and the node spacing in the suburbs and remote areas is ≤2km, balancing the calculation accuracy and efficiency.

[0083] (2) Edge definition: The passable path between nodes is used as the edge, and each edge is associated with a combination of resource scenarios. There are four types of resource combination scenarios associated with each edge: reuse of old pipelines and reuse of old optical cables, reuse of old pipelines and new optical cables, new pipelines and reuse of old optical cables, and new pipelines and new optical cables.

[0084] (3) Constraint parameter configuration: Configure constraint parameters for each resource combination scenario and establish a constraint parameter update mechanism. The constraint parameters include the availability threshold of existing resources, the construction cost coefficient of new resources, the approval cycle coefficient, the maintenance cost coefficient of existing pipelines and optical cables, and the environmental assessment threshold of new resources. The constraint parameter update mechanism includes quarterly synchronization of parameters related to existing resources and monthly synchronization of parameters related to new resources.

[0085] (4) Edge validity determination: In addition to geographical constraints, a new resource reachability determination is added. A certain path segment must support at least one pipeline-optical cable combination scenario and meet the constraint parameters; otherwise, it is considered an invalid edge and is not included in the model.

[0086] Step S300 Edge weight calculation: Using path length, construction and maintenance cost, construction period and resource utilization rate as indicators, adjust the weight coefficients of the indicators based on the construction plan indicators, construct a plan-oriented cost function based on the weighted sum of the indicators, and calculate the comprehensive weight of each edge for each resource combination scenario through the plan-oriented cost function.

[0087] In practice, the expression for the plan-oriented cost function is as follows:

[0088] W = α*L + β*C + γ*T + δ*U;

[0089] Where L represents the path length, α represents the weight coefficient of L, C represents the construction and maintenance cost, β represents the weight coefficient of C, T represents the construction period, γ represents the weight coefficient of T, U represents the resource utilization rate, δ represents the weight coefficient of U, and α+β+γ+δ=1.

[0090] When adjusting the weight coefficients of indicators based on construction plan indicators, a basic weight template is selected according to the core objectives of the construction plan indicators. The adjustment range is determined using fuzzy hierarchical analysis, and the corrected value of the weight coefficient is calculated by inputting the specific constraint thresholds of the current project. These constraint thresholds include budget deviation and period compression. There are three types of basic weight templates:

[0091] Cost-priority template: α = 0.05, β = 0.5, γ = 0.15, δ = 0.3 (suitable for projects with tight budgets);

[0092] Cycle-priority template: α = 0.05, β = 0.2, γ = 0.55, δ = 0.2 (applicable to emergency construction projects);

[0093] Reuse-first template: α = 0.05, β = 0.2, γ = 0.15, δ = 0.6 (suitable for projects with high resource reuse requirements);

[0094] During fine-tuning, the adjustment range is determined by fuzzy hierarchical analysis (FAHP). The specific constraint thresholds of the project are input (such as budget deviation ±10% and cycle compression ±20%), and the correction value of the weight coefficient is automatically calculated. The correction range does not exceed ±0.1 of the basic template.

[0095] Step S500 Optimal Path Solution: Construct an improved Dijkstra algorithm with constraint filtering. Based on the comprehensive weight of each edge and each resource combination scenario, the improved Dijkstra algorithm filters the edges and corresponding resource combination scenarios in the multi-resource combination graph model, and selects the initial path scheme with the smallest comprehensive weight that satisfies the constraint conditions, thus forming the initial path scheme.

[0096] In a specific implementation, based on the comprehensive weight of each resource combination scenario for each edge, the edges and corresponding resource combination scenarios in the multi-resource combination graph model are filtered using an improved Dijkstra algorithm, including the following operations:

[0097] (1) Initialization: Initialize the weight of the starting node to 0, and the weight of other nodes to infinity, and build a priority queue;

[0098] (2) Constraint filtering: By introducing a constraint filtering layer in the improved Dijkstra algorithm, a three-check filtering is performed. Edges that do not meet the constraint conditions and corresponding resource combination scenarios are eliminated through basic constraint detection, resource constraint detection and plan constraint detection.

[0099] (3) Priority calculation and path update: Based on the dual-key sorting optimization priority queue, the node with the smallest weight is selected from the priority queue in turn, and the path weight of the adjacent nodes is updated. In the dual-key sorting, the primary key is the comprehensive weight W, and the secondary key is the resource utilization rate U.

[0100] (4) Path pruning: Set the optimal path deviation threshold (default 5%). When the combined weight of the candidate paths of a node exceeds the deviation threshold of the current optimal path weight, the candidate path is pruned directly.

[0101] (5) Termination judgment: When the endpoint is removed from the priority queue and the path weights of all reachable endpoints exceed the deviation threshold of the optimal path weight, the calculation terminates and the output includes the path corresponding node sequence and the initial path scheme of each paragraph resource combination scenario.

[0102] Among them, basic constraint detection: eliminate scenarios where the path crosses prohibited construction areas (such as cultural relic protection areas or military management areas).

[0103] Resource constraint detection: Eliminate scenarios where the availability of existing resources is below the threshold or where newly constructed resources do not meet environmental protection / approval requirements.

[0104] Plan constraint detection: Predict whether the cost and cycle corresponding to the scenario exceed the construction plan threshold (e.g., if the cost of a single edge exceeds the budget by 50%, it will be directly eliminated) to avoid invalid calculations.

[0105] Step S600 Dynamic Iterative Optimization: Real-time synchronization of resource status data and construction plan indicators. When resource status data or construction plan indicators are adjusted, path recalculation is triggered. The optimal path is re-solved using an improved Dijkstra algorithm, and the optimized path and resource usage list are output.

[0106] As a specific implementation of dynamic iterative optimization, this step includes the following operations:

[0107] (1) Real-time synchronization: Through dual listening interfaces, the resource status database and the construction plan management platform are connected respectively to synchronize the updates of resource status data and construction plan indicators in real time;

[0108] (2) Trigger judgment: When the change in resource status exceeds the threshold or the adjustment of the construction plan exceeds the threshold, the departure path is recalculated;

[0109] (3) Recalculation optimization: Update the weights of the relevant edges and re-solve the optimal path by improving the Dijkstra algorithm;

[0110] (4) Verify the output: Verify the constraints of the recalculated path scheme to ensure that it meets the requirements of the current construction plan and resource status;

[0111] (5) Output results: The optimized path and resource usage list will be output as the results. The resource usage list includes the type of pipeline and optical cable for each path segment, cost estimate, construction period breakdown and risk level.

[0112] In this step, resource status triggers a recalculation when the availability of a reused resource changes by ≥10%, the approval status of a new resource changes, or policies in the construction area are adjusted. Plan adjustment triggers a recalculation when the budget, cycle, or priority weight of the construction plan changes by ≥5%. The recalculation optimization strategy updates only the edge weights related to the change (e.g., if the availability of a reused pipeline decreases, only the edges associated with that pipeline are recalculated), eliminating the need for a full map recalculation, thus improving recalculation efficiency by ≥70%.

[0113] Based on the method disclosed in this embodiment, a prefecture-level city communication backbone network expansion project is proposed, requiring the planning of an optical cable transmission path between the starting point A (coordinates: E113.8°, N22.5°) and the ending point B (coordinates: E114.1°, N22.7°). The project requires balancing cost control and construction efficiency, prioritizing the reuse of existing pipelines / optical cables, while allowing for the construction of new pipelines / optical cables when existing resources are insufficient. The construction plan specifies: a budget ceiling of 800,000 yuan, a construction period of ≤60 days, and a priority weight of ≥0.6 for reusing existing resources.

[0114] The specific parameters are as follows:

[0115] (1) Basic geographical and planning parameters:

[0116] Starting point A: E113.8000°, N22.5000°; Ending point B: E114.1000°, N22.7000°;

[0117] Construction plan indicators: budget threshold of 800,000 yuan, cycle limit of 60 days, priority weight for reuse of existing resources δ = 0.6, cost weight β = 0.2, cycle weight γ = 0.15, path length weight α = 0.05 (α + β + γ + δ = 1);

[0118] Geographical restrictions: Crossing the core urban cultural relics protection area is prohibited (coordinate range: E113.95°-E114.0°, N22.55°-N22.65°).

[0119] (2) Resource status data is shown in Table 1:

[0120] Table 1. Resource Status Data Table

[0121]

[0122]

[0123] The algorithm execution steps are as follows:

[0124] (1) Construction of Multi-Resource Combination Graph Model

[0125] Node setup: Select A, C, D, and B as core nodes, and add 10 auxiliary nodes (covering key inflection points of the path), for a total of 14 nodes;

[0126] Edge definition: Each edge corresponds to an actual walkable path, associated with four resource combination scenarios, as shown in the following example:

[0127] Side AC: Supports two scenarios: "P1+O1" (existing duct + existing optical cable) and "P1+O_new" (existing duct + newly built optical cable) (no new duct required);

[0128] Edge DB: Supports two scenarios: "P_new+O1" (new pipeline + reuse of existing optical cable) and "P_new+O_new" (new pipeline + new optical cable) (without reuse of existing pipeline resources);

[0129] Constraint parameter set configuration: Configure parameters for each scenario of each edge, such as the “P1+O1” scenario of edge AC: maintenance cost coefficient 0.1, availability threshold 92%; “P1+O_new” scenario: construction cost coefficient 12,000 yuan / km, no maintenance cost.

[0130] (2) Calculation of the planning-oriented cost function

[0131] Using the formula W=α·L+β·C+γ·T+δ·U, and substituting the weight coefficients (α=0.05, β=0.2, γ=0.15, δ=0.6), the comprehensive weight of each edge for each scenario is calculated:

[0132] Side AC (length 3km):

[0133] Scenario “P1+O1”: W = 0.05 × 3 + 0.2 × (3 × 0.1 × 1000) + 0.15 × 0 (no construction period) + 0.6 × 0.88 = 0.15 + 60 + 0 + 0.528 = 60.678

[0134] Scenario “P1+O_new”: W = 0.05×3 + 0.2×(3×1.2×1000) + 0.15×3 (construction period of 3 days) + 0.6×0.3 (new resource utilization rate) = 0.15 + 720 + 0.45 + 0.18 = 720.78

[0135] Side DB (length 4km):

[0136] Scenario “P_new+O1”: W = 0.05×4 + 0.2×(4×20×1000) + 0.15×(5+2)(approval 5 days + construction 2 days) + 0.6×0.88 = 0.2 + 16000 + 1.05 + 0.528 = 16001.778,

[0137] Scenario “P_new+O_new”: W=0.05×4+0.2×(4×20×1000+4×1.2×1000)+0.15×(5+3)+0.6×0.3=0.2+16960+1.2+0.18=16961.58;

[0138] (3) Improve Dijkstra's algorithm to find the optimal path

[0139] Constraint filtering: Pre-exclude scenarios that exceed the budget (e.g., cost of a single edge exceeds 800,000) or have a cycle exceeding 60 days, and retain valid edges and their corresponding scenarios;

[0140] Path search: Starting from point A, calculate the minimum comprehensive weight path for each node, and finally obtain the optimal path: A→C→D→B, with resource combinations of "P1+O1" (AC segment), "P2+O2" (CD segment), and "P_new+O1" (DB segment).

[0141] (4) Dynamic iterative optimization

[0142] Triggering event: During the pre-construction inspection, it was found that the available capacity of the reused pipeline P2 had dropped to 8 cores (availability 70%, below the threshold of 80%), and the resource status database was updated in real time;

[0143] Recalculation process: Automatically remove the "P2+O2" scenario, recalculate the available scenarios for edge CD (only the "P2+O_new" scenario is valid), update the cost function weights, adjust the optimal path to A→C→D→B, and update the resource combination to "P1+O1" (AC segment), "P2+O_new" (CD segment), and "P_new+O1" (DB segment);

[0144] (5) Output results

[0145] Path coordinate sequence: A(E113.8°, N22.5°)→C(E113.85°, N22.55°)→D(E113.9°, N22.6°)→B(E114.1°, N22.7°).

[0146] The resource usage list is shown in Table 2.

[0147] Table 2: Resource Usage List (Excerpt)

[0148]

[0149] Estimated construction indicators: total budget of 790,900 yuan (≤800,000 yuan), total period of 10 days (≤60 days), and utilization rate of existing resources of 86% (≥0.6 weight requirement).

[0150] The comparison results with traditional algorithms are shown in Table 3.

[0151] Table 3. Comparison Display

[0152]

[0153] This embodiment achieves deep coupling of "construction plan - multi-resource combination - path planning" through an algorithm. The output path plan fully meets the requirements of budget, cycle and reuse priority. Compared with traditional algorithms, the cost is reduced by 17.3%, the cycle is shortened by 60%, and the resource utilization rate is increased by 186.7%. It can also dynamically adapt to changes in resource status without the need for manual secondary adjustment, which fully verifies the practicality and advancement of the algorithm.

[0154] Example 2:

[0155] This invention discloses a plan-oriented two-point automatic pathfinding system with multi-resource combination constraints, comprising an input parameter acquisition module, a multi-resource combination graph model construction module, an edge weight calculation module, an optimal path solution module, and a dynamic iterative optimization module.

[0156] The input parameter acquisition module is used to perform the following: acquire geographic coordinates, construction plan indicators, and resource status data as input data, and verify the input data. Based on the verified input data, a dataset is constructed. The geographic coordinates include the starting point coordinates and the ending point coordinates. The construction plan indicators include budget thresholds, construction period upper limits, reuse priority weights, construction and maintenance cost weights, construction period weights, path length weights, and geographic restriction boundaries. The resource status data includes the location information, available capacity, and aging degree of existing pipelines and optical cables, as well as the geographic restriction boundaries and approval period standards for new pipelines and optical cables.

[0157] The multi-resource combination graph model construction module is used to perform the following: taking geographic coordinate points as nodes, selecting a node network covering the start and end points, taking the accessible paths between two nodes as edges, associating resource combination scenarios with each edge, configuring constraint parameters for each resource combination scenario and setting a dynamic update mechanism for the constraint parameters, forming a multi-resource combination graph model that includes node networks, edges, resource composition scenarios and constraint parameters.

[0158] As a specific implementation of the multi-resource combination graph model construction module, this module is used to perform the following operations:

[0159] (1) Node generation: Based on geographic coordinates, the node density is configured according to the region type to generate a node network covering the region from the starting point to the end point. When configuring nodes, the node spacing in the core area (such as the urban built-up area) is ≤500m, and the node spacing in the suburbs and remote areas is ≤2km, balancing the calculation accuracy and efficiency.

[0160] (2) Edge definition: The passable path between nodes is used as the edge, and each edge is associated with a combination of resource scenarios. There are four types of resource combination scenarios associated with each edge: reuse of old pipelines and reuse of old optical cables, reuse of old pipelines and new optical cables, new pipelines and reuse of old optical cables, and new pipelines and new optical cables.

[0161] (3) Constraint parameter configuration: Configure constraint parameters for each resource combination scenario and establish a constraint parameter update mechanism. The constraint parameters include the availability threshold of existing resources, the construction cost coefficient of new resources, the approval cycle coefficient, the maintenance cost coefficient of existing pipelines and optical cables, and the environmental assessment threshold of new resources. The constraint parameter update mechanism includes quarterly synchronization of parameters related to existing resources and monthly synchronization of parameters related to new resources.

[0162] (4) Edge validity determination: In addition to geographical constraints, a new resource reachability determination is added. A certain path segment must support at least one pipeline-optical cable combination scenario and meet the constraint parameters; otherwise, it is considered an invalid edge and is not included in the model.

[0163] The edge weight calculation module is used to perform the following: using path length, construction and maintenance cost, construction period and resource utilization rate as indicators, the weight coefficients of the indicators are adjusted based on the construction plan indicators, a plan-oriented cost function is constructed based on the weighted sum of the indicators, and the comprehensive weight of each edge for each resource combination scenario is calculated through the plan-oriented cost function.

[0164] In practice, the expression for the plan-oriented cost function is as follows:

[0165] W = α*L + β*C + γ*T + δ*U;

[0166] Where L represents the path length, α represents the weight coefficient of L, C represents the construction and maintenance cost, β represents the weight coefficient of C, T represents the construction period, γ represents the weight coefficient of T, U represents the resource utilization rate, δ represents the weight coefficient of U, and α+β+γ+δ=1.

[0167] When adjusting the weight coefficients of indicators based on construction plan indicators, a basic weight template is selected according to the core objectives of the construction plan indicators. The adjustment range is determined using fuzzy hierarchical analysis, and the corrected value of the weight coefficient is calculated by inputting the specific constraint thresholds of the current project. These constraint thresholds include budget deviation and period compression. There are three types of basic weight templates:

[0168] Cost-priority template: α = 0.05, β = 0.5, γ = 0.15, δ = 0.3 (suitable for projects with tight budgets);

[0169] Cycle-priority template: α = 0.05, β = 0.2, γ = 0.55, δ = 0.2 (applicable to emergency construction projects);

[0170] Reuse-first template: α = 0.05, β = 0.2, γ = 0.15, δ = 0.6 (suitable for projects with high resource reuse requirements);

[0171] During fine-tuning, the adjustment range is determined by fuzzy hierarchical analysis (FAHP). The specific constraint thresholds of the project are input (such as budget deviation ±10% and cycle compression ±20%), and the correction value of the weight coefficient is automatically calculated. The correction range does not exceed ±0.1 of the basic template.

[0172] The optimal path solution module is used to perform the following: construct an improved Dijkstra algorithm with constraint screening, and screen the edges and corresponding resource combination scenarios in the multi-resource combination graph model based on the comprehensive weight of each edge and each resource combination scenario, and select the initial path scheme with the smallest comprehensive weight that satisfies the constraint conditions to form the initial path scheme.

[0173] In a specific implementation, based on the comprehensive weight of each resource combination scenario for each edge, the edges and corresponding resource combination scenarios in the multi-resource combination graph model are filtered using an improved Dijkstra algorithm, including the following operations:

[0174] (1) Initialization: Initialize the weight of the starting node to 0, and the weight of other nodes to infinity, and build a priority queue;

[0175] (2) Constraint filtering: By introducing a constraint filtering layer in the improved Dijkstra algorithm, a three-check filtering is performed. Edges that do not meet the constraint conditions and corresponding resource combination scenarios are eliminated through basic constraint detection, resource constraint detection and plan constraint detection.

[0176] (3) Priority calculation and path update: Based on the dual-key sorting optimization priority queue, the node with the smallest weight is selected from the priority queue in turn, and the path weight of the adjacent nodes is updated. In the dual-key sorting, the primary key is the comprehensive weight W, and the secondary key is the resource utilization rate U.

[0177] (4) Path pruning: Set the optimal path deviation threshold (default 5%). When the combined weight of the candidate paths of a node exceeds the deviation threshold of the current optimal path weight, the candidate path is pruned directly.

[0178] (5) Termination judgment: When the endpoint is removed from the priority queue and the path weights of all reachable endpoints exceed the deviation threshold of the optimal path weight, the calculation terminates and the output includes the path corresponding node sequence and the initial path scheme of each paragraph resource combination scenario.

[0179] Among them, basic constraint detection: eliminate scenarios where the path crosses prohibited construction areas (such as cultural relic protection areas or military management areas).

[0180] Resource constraint detection: Eliminate scenarios where the availability of existing resources is below the threshold or where newly constructed resources do not meet environmental protection / approval requirements.

[0181] Plan constraint detection: Predict whether the cost and cycle corresponding to the scenario exceed the construction plan threshold (e.g., if the cost of a single edge exceeds the budget by 50%, it will be directly eliminated) to avoid invalid calculations.

[0182] The dynamic iterative optimization module is used to perform the following: real-time synchronization of resource status data and construction plan indicators; when resource status data or construction plan indicators are adjusted, path recalculation is triggered; the optimal path is re-solved using an improved Dijkstra algorithm; and the optimized path and resource usage list are output.

[0183] As a specific implementation of the dynamic iterative optimization module, this module is used to perform the following operations:

[0184] (1) Real-time synchronization: Through dual listening interfaces, the resource status database and the construction plan management platform are connected respectively to synchronize the updates of resource status data and construction plan indicators in real time;

[0185] (2) Trigger judgment: When the change in resource status exceeds the threshold or the adjustment of the construction plan exceeds the threshold, the departure path is recalculated;

[0186] (3) Recalculation optimization: Update the weights of the relevant edges and re-solve the optimal path by improving the Dijkstra algorithm;

[0187] (4) Verify the output: Verify the constraints of the recalculated path scheme to ensure that it meets the requirements of the current construction plan and resource status;

[0188] (5) Output results: The optimized path and resource usage list will be output as the results. The resource usage list includes the type of pipeline and optical cable for each path segment, cost estimate, construction period breakdown and risk level.

[0189] In this step, resource status triggers a recalculation when the availability of a reused resource changes by ≥10%, the approval status of a new resource changes, or policies in the construction area are adjusted. Plan adjustment triggers a recalculation when the budget, cycle, or priority weight of the construction plan changes by ≥5%. The recalculation optimization strategy updates only the edge weights related to the change (e.g., if the availability of a reused pipeline decreases, only the edges associated with that pipeline are recalculated), eliminating the need for a full map recalculation, thus improving recalculation efficiency by ≥70%.

[0190] The system in this embodiment can execute the method disclosed in Embodiment 1 to achieve planned two-point automatic pathfinding.

[0191] The above provides a detailed description of the multi-resource combination constraint-based plan-oriented two-point automatic pathfinding method and system provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-resource combined constraint planning-oriented two-point automatic routing method, characterized in that, The method comprises the following steps: Input parameter acquisition: acquiring geographic coordinates, construction plan indicators and resource state data as input data, and verifying the input data, and constructing a data set based on the verified input data; Wherein the geographic coordinates include the starting point coordinates and the ending point coordinates, the construction plan indicators include the budget threshold, the construction period upper limit, the priority weight of old resources, the construction and maintenance cost weight, the construction period weight, the path length weight and the geographical limit boundary, and the resource state data includes the location information, the available capacity, the aging degree of the existing pipeline and optical cable, and the geographical limit boundary and the approval period standard of the newly built pipeline and optical cable; Multi-resource combination graph model construction: taking the geographic coordinate points as nodes, selecting the node network covering the starting point and the ending point, taking the passable path between the two nodes as an edge, associating a resource combination scene with each edge, configuring constraint parameters for each resource combination scene and setting a constraint parameter dynamic updating mechanism, forming a multi-resource combination graph model including node network, edge, resource combination scene and constraint parameter; Edge weight calculation: taking the path length, the construction and maintenance cost, the construction period and the resource utilization rate as indicators, adjusting the weight coefficient of the indicators based on the construction plan indicators, constructing a plan-oriented cost function based on the weighted sum of the indicators, and calculating the comprehensive weight of each resource combination scene of each edge through the plan-oriented cost function; Optimal path solving: constructing an improved Dijkstra algorithm with constraint screening, screening the edges and corresponding resource combination scenes in the multi-resource combination graph model based on the comprehensive weight of each resource combination scene of each edge and through the improved Dijkstra algorithm, selecting an initial path scheme with the smallest comprehensive weight that meets the constraint conditions, and forming an initial path scheme; Dynamic iterative optimization: synchronizing the resource state data and the construction plan indicators in real time, triggering path recalculation when the resource state data or the construction plan indicators are adjusted, re-solving the optimal path through the improved Dijkstra algorithm, and outputting the optimized path and resource usage list.

2. The multi-resource combined constraint's plan-guided two-point automatic routing method according to claim 1, wherein, The multi-resource combination graph model construction comprises the following operations: Node generation: based on the geographic coordinate points, the node density is configured in combination with the region type, and the node network covering the starting point to the ending point region is generated; Edge definition: taking the passable path between the nodes as an edge, associating a resource scene combination with each edge, wherein the resource combination scene associated with each edge includes four kinds, which are old pipeline and old optical cable, old pipeline and new optical cable, new pipeline and old optical cable, and new pipeline and new optical cable; Constraint parameter configuration: configuring constraint parameters for each resource combination scene, and establishing a constraint parameter updating mechanism, the constraint parameters include the old resource availability threshold, the new resource construction cost coefficient, the approval period coefficient, the maintenance cost coefficient of old pipeline and optical cable, and the environmental assessment threshold of new resources, the constraint parameter updating mechanism includes quarterly synchronization of old resource related parameters and monthly synchronization of new resource related parameters; Edge validity determination: in addition to geographical restrictions, the accessibility of new resources is determined, a certain path segment must support at least one pipeline-optical cable combination scene and meet the constraint parameters, otherwise it is considered as an invalid edge and is not included in the model.

3. The multi-resource combined constraint's plan-directed two-point automatic pathfinding method according to claim 1, wherein, The expression of the plan-oriented cost function is: W = a*L + b*C + g*T + d*U; Wherein, L represents the path length, a represents the weight coefficient of L, C represents the construction and maintenance cost, b represents the weight coefficient of C, T represents the construction period, g represents the weight coefficient of T, U represents the resource utilization rate, d represents the weight coefficient of U, a+b+g+d=1; When adjusting the weight coefficient of the construction plan index based on the construction plan index, the core target of the construction plan index is selected to select the basic weight template, and the adjustment range is determined by the fuzzy analytic hierarchy process. The specific constraint threshold of the current project is input to calculate the correction value of the weight coefficient, wherein the constraint threshold includes the budget deviation and the period compression; There are three types of basic weight templates, which are cost priority template suitable for budget tight projects, period priority template suitable for urgent construction projects, and reuse priority template suitable for projects with high resource reuse requirements. Different weight coefficients are set for a, b, g and d in the three types of basic weight templates.

4. The method of claim 1, wherein the method further comprises: Based on the comprehensive weight of each resource combination scene of each edge, the edges and corresponding resource combination scenes in the multi-resource combination graph model are screened by improving the Dijkstra algorithm, including the following steps: Initialization: The weight value of the starting node is initialized to 0, and the weight values of other nodes are set to infinity. A priority queue is constructed; Constraint screening: three screening is performed through the constraint screening layer introduced in the improved Dijkstra algorithm. The edges and corresponding resource combination scenes that do not meet the constraint conditions are removed through basic constraint detection, resource constraint detection and plan constraint detection; Priority calculation and path update: based on double-key sorting optimization priority queue, the node with the minimum weight is selected from the priority queue in turn, and the path weight of the adjacent node is updated, wherein the main key of the double-key sorting is the comprehensive weight W, and the secondary key is the resource utilization rate U; Path pruning: set the optimal path deviation threshold. When the comprehensive weight of a certain node exceeds the deviation threshold of the current optimal path weight, the candidate path is pruned directly; Termination judgment: when the end point is taken out from the priority queue, and the path weight of all reachable end points exceeds the deviation threshold of the optimal path weight, the calculation is terminated, and the initial path scheme including the path corresponding node sequence and the resource combination scene of each paragraph is output.

5. The multi-resource combined constraint's plan-directed two-point automatic pathfinding method according to claim 1, wherein, Dynamic iterative optimization includes the following operations: Real-time synchronization: through the double listening interface, the resource state database and the construction plan management platform are connected respectively, and the updates of the resource state data and the construction plan index are synchronized in real time; Trigger judgment: when the resource state changes exceed the threshold or the construction plan adjustment range exceeds the threshold, the path recalculation is triggered; Recalculating optimization: update the weight of the related edge, and solve the optimal path again by improving the Dijkstra algorithm; Verification output: the path scheme after recalculation is verified to ensure that it meets the current construction plan and resource state requirements; Result output: the optimized path and resource usage list are output as the result, and the resource usage list includes the type, cost estimate, construction period split and risk level of each path paragraph pipeline and optical cable.

6. A multi-resource combined constraint-based plan-directed two-point automatic routing system, characterized by, It includes an input parameter acquisition module, a multi-resource combination graph model construction module, an edge weight calculation module, an optimal path solution module, and a dynamic iterative optimization module; The input parameter acquisition module is used to perform the following: acquire geographic coordinates, construction plan indicators, and resource status data as input data, validate the input data, and construct a dataset based on the validated input data; The geographical coordinates include the starting point coordinates and the ending point coordinates. The construction plan indicators include the budget threshold, the upper limit of the construction period, the priority weight of reusing existing infrastructure, the weight of construction and maintenance costs, the weight of the construction period, the weight of the path length, and the geographical restriction boundary. The resource status data includes the location information, available capacity, and aging degree of existing pipelines and optical cables, as well as the geographical restriction boundary and approval cycle standard for new pipelines and optical cables. The multi-resource combination graph model construction module is used to perform the following: taking geographic coordinate points as nodes, selecting a node network covering the start and end points, taking the accessible path between two nodes as edges, associating resource combination scenarios with each edge, configuring constraint parameters for each resource combination scenario and setting a dynamic update mechanism for constraint parameters, forming a multi-resource combination graph model including node network, edges, resource composition scenarios and constraint parameters; The edge weight calculation module is used to perform the following: using path length, construction and maintenance cost, construction period and resource utilization rate as indicators, adjust the weight coefficients of the indicators based on the construction plan indicators, construct a plan-oriented cost function based on the weighted sum of the indicators, and calculate the comprehensive weight of each edge for each resource combination scenario through the plan-oriented cost function; The optimal path solution module is used to perform the following: construct an improved Dijkstra algorithm with constraint screening, and screen the edges and corresponding resource combination scenarios in the multi-resource combination graph model based on the comprehensive weight of each edge and each resource combination scenario, and select the initial path scheme with the smallest comprehensive weight that satisfies the constraint conditions to form the initial path scheme. The dynamic iterative optimization module is used to perform the following: real-time synchronization of resource status data and construction plan indicators; when resource status data or construction plan indicators are adjusted, path recalculation is triggered; the optimal path is re-solved using an improved Dijkstra algorithm; and the optimized path and resource usage list are output.

7. The multi-resource combined constraint's plan-guided two-point automatic routing system according to claim 6, wherein, The multi-resource combination graph model building module is used to perform the following operations: Node generation: Based on geographic coordinates, the node density is configured according to the region type to generate a node network covering the region from the starting point to the ending point. Edge definition: The accessible paths between nodes are used as edges, and each edge is associated with a combination of resource scenarios. There are four types of resource scenarios associated with each edge: reuse of existing pipelines and reuse of existing optical cables, reuse of existing pipelines and newly built optical cables, newly built pipelines and reuse of existing optical cables, and newly built pipelines and newly built optical cables. Constraint parameter configuration: Configure constraint parameters for each resource combination scenario and establish a constraint parameter update mechanism. The constraint parameters include the availability threshold of existing resources, the construction cost coefficient of new resources, the approval cycle coefficient, the maintenance cost coefficient of existing pipelines and optical cables, and the environmental assessment threshold of new resources. The constraint parameter update mechanism includes quarterly synchronization of parameters related to existing resources and monthly synchronization of parameters related to the use of new resources. Edge validity determination: In addition to geographical constraints, a new resource reachability determination is added. A path segment must support at least one pipeline-optical cable combination scenario and meet the constraint parameters; otherwise, it is considered an invalid edge and will not be included in the model.

8. The multi-resource combined constraint's plan-guided two-point automatic routing system according to claim 6, wherein, The expression for the plan-oriented cost function is: W = α*L + β*C + γ*T + δ*U; Where L represents the path length, α represents the weight coefficient of L, C represents the construction and maintenance cost, β represents the weight coefficient of C, T represents the construction period, γ represents the weight coefficient of T, U represents the resource utilization rate, δ represents the weight coefficient of U, and α+β+γ+δ=1. When adjusting the weight coefficients of indicators based on construction plan indicators, a basic weight template is selected according to the core objectives of the construction plan indicators, and the adjustment range is determined by fuzzy hierarchical analysis. The corrected value of the weight coefficient is calculated by inputting the specific constraint thresholds of the current project. The constraint thresholds include budget deviation and cycle compression. There are three types of basic weight templates: a cost-priority template for projects with tight budgets, a cycle-priority template for projects with urgent construction needs, and a reuse-priority template for projects with high resource reuse requirements. Different weight coefficients are set for α, β, γ, and δ in the three types of basic weight templates.

9. The multi-resource combined constraint's plan-guided two-point automatic routing system according to claim 6, wherein, Based on the comprehensive weight of each resource combination scenario for each edge, the edges and corresponding resource combination scenarios in the multi-resource combination graph model are filtered using an improved Dijkstra algorithm, including the following steps: Initialization: Initialize the weight of the starting node to 0, and the weights of other nodes to infinity, and build a priority queue; Constraint filtering: A three-stage filtering process is performed by introducing a constraint filtering layer in the improved Dijkstra algorithm. Edges that do not meet the constraints and their corresponding resource combinations are eliminated through basic constraint detection, resource constraint detection, and plan constraint detection. Priority calculation and path update: The priority queue is optimized based on dual-key sorting. The node with the smallest weight is selected from the priority queue in turn, and the path weight of the adjacent nodes is updated. In dual-key sorting, the primary key is the comprehensive weight W, and the secondary key is the resource utilization rate U. Path pruning: Set an optimal path deviation threshold. When the combined weight of the candidate paths for a node exceeds the deviation threshold of the current optimal path weight, the candidate path is pruned directly. Termination judgment: The calculation terminates when the destination is removed from the priority queue and the weights of all reachable destinations exceed the deviation threshold of the optimal path weight. The output includes the sequence of nodes corresponding to the path and the initial path scheme of the resource combination scenario of each segment.

10. The multi-resource combined constraint's plan-guided two-point automatic routing system according to claim 6, wherein, The dynamic iterative optimization module is used to perform the following operations: Real-time synchronization: Through dual listening interfaces, it connects to the resource status database and the construction plan management platform respectively, and synchronizes the updates of resource status data and construction plan indicators in real time; Trigger judgment: When the change in resource status exceeds the threshold or the adjustment of the construction plan exceeds the threshold, the departure path is recalculated; Recalculation optimization: Update the weights of relevant edges and re-solve for the optimal path using an improved Dijkstra algorithm; Verification output: Verify the constraints of the recalculated route to ensure that it meets the requirements of the current construction plan and resource status; Output results: The optimized path and resource usage list will be output as the results. The resource usage list includes the type of pipeline and optical cable for each path segment, cost estimate, construction period breakdown and risk level.

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