Water network path automatic planning identification method based on spatial data

Abstract

The invention discloses a water network path automatic planning and identifying method based on spatial data, belongs to an electronic information technology, and particularly relates to a water network path algorithm. The method comprises the steps of identifying multi-dimensional factors, formulating a unified quantitative standard, performing unified cost model reduction, formulating the unified quantitative standard of the multi-dimensional factors, multiplying a quantitative value of each factor by a corresponding weight, calculating comprehensive cost, performing regional grid consistency division, performing cost reduction, and generating a comprehensive cost surface. And generating a comprehensive cost surface, automatically planning and identifying a path and the like. According to the method, a water network path is generated based on an intelligent path of spatial analysis, multi-source data fusion, dynamic adaptability enhancement, dynamic verification and a self-adaptive optimization mechanism are achieved, and the technical bottlenecks of factor collaborative quantization, dynamic optimization and efficient decision making are solved.

Description

Technical Field

[0001] The present invention belongs to the field of electronic information technology, and in particular is an algorithm for water network paths. Background Art

[0002] The scientific and efficient planning of water networks is the key to optimizing water resource allocation. Currently, water network routing mainly relies on traditional manual surveys and empirical judgments, which has the following problems:

[0003] Traditional methods lack scientific and systematic validity. Existing water network routing plans are often based on two-dimensional blueprints and empirical models of local regions, lacking the ability to comprehensively analyze multidimensional spatial data such as topography, ecological red lines, and geological fault zones. For example, Yunnan Province, located on the Yunnan-Guizhou Plateau, has a complex and varied terrain (a blend of plateaus, mountains, and basins). Traditional methods struggle to coordinate ecological protection with project feasibility, leading to high economic costs and significant ecological risks in planning solutions.

[0004] The coordinated quantification of multiple factors is difficult. Water network routing is constrained by multiple factors, including construction restrictions (ecological red lines, prime farmland), vertical terrain (slope, elevation), and cost differences (land use type, population density). However, existing technologies lack unified quantification standards and dynamic weighting mechanisms. For example, the impact of slope on construction costs is often corrected using a fixed coefficient, which fails to reflect the continuous changes in actual terrain.

[0005] Computational efficiency and adaptability are insufficient. Traditional path planning relies on manual trial and error or simple algorithms (such as the shortest path model), which struggle to handle massive amounts of spatial data and complex constraints. In large-scale watershed and network planning, computational time is lengthy and real-time path optimization is difficult. Furthermore, existing solutions lack the ability to dynamically respond to unexpected factors (such as new protected areas and temporary construction restrictions), resulting in delayed adjustments.

[0006] Visualization and decision-making support are weak; existing research results are mostly presented in two-dimensional charts or single data layers, lacking three-dimensional spatial visualization and multi-factor overlay analysis capabilities. This makes it difficult for decision-makers to intuitively assess the comprehensive impact of pathways on the ecological environment and socioeconomics, which can easily lead to controversy. Summary of the Invention

[0007] The purpose of this invention is to propose a water network path automatic planning method based on spatial data fusion and intelligent algorithm to solve the problems of multi-factor collaborative quantification, dynamic optimization and efficient decision-making.

[0008] The method for automatic planning and identification of water network paths based on spatial data is characterized in that the method comprises the following steps:

[0009] S1, multi-dimensional factor identification and unified quantitative standard formulation, as well as unified cost model calculation, specifically including:

[0010] S1-1, multi-dimensional factor identification, including the construction prohibition factor (Fnbz), the vertical coefficient factor (Fvc), and the cost difference factor (Fcd);

[0011] S1-2, the unified quantitative standard of multi-dimensional factors is established, specifically:

[0012] S1-2-1, the prohibition of construction factors adopts the binary judgment method to formulate quantitative standards and determine the cost value of the prohibition of construction factors (C nbz );

[0013] In the prohibited construction zone C nbz = +∞, indicating that it cannot be crossed; if it is not in the prohibited construction area, then C nbz = 0, indicating free traversal;

[0014] S1-2-2, the vertical coefficient factor (Fvc) adopts a continuous function or a piecewise function to formulate a quantitative standard, specifically:

[0015] The continuous function is to establish the correlation function between the slope value and the construction cost, and calculate the vertical coefficient cost C vc , C vc The calculation formula is as follows:

[0016]

[0017] represents the slope value, h (h>0) is the real construction cost coefficient, which is used to fit the real construction cost of different slopes under real construction conditions. The coefficient h is selected so that the slope of the function curve is similar to the slope of the real construction conditions. If the coefficient value cannot make the slope of the function curve similar to the slope of the real construction conditions, a piecewise function is used to formulate the quantitative standard;

[0018] The piecewise function is divided into several grade intervals according to the slope size. Each grade interval corresponds to a cost coefficient. The greater the slope, the higher the cost. The vertical coefficient cost C of different slope intervals vc Listed in the table below:

[0019]

[0020] S1-2-3, the cost difference factor is quantified based on the cost and degree of difference of different land area types;

[0021] S2 calculates the comprehensive cost by multiplying the quantitative value of each factor by its corresponding weight. The specific algorithm is as follows:

[0022]

[0023] Among them, C total is the comprehensive cost, Pvc 、P cd are the weights of the vertical coefficient factor and the cost difference factor, C vc 、C cd are the quantitative cost values ​​of these two factors respectively;

[0024] The weight distribution is based on the importance and influence of each factor in the water network path selection, and is determined by expert scoring, analytic hierarchy process (AHP), entropy weight method or other multi-criteria decision-making methods; the prohibition barrier cost C nbz A binary decision method is used, with a value of 0 or +∞, so there is no weighting;

[0025] S3, regional grid consistency division, cost reduction, and generation of comprehensive cost surface, specifically:

[0026] S3-1, Regional Grid Division: Determine the grid width based on the size and accuracy requirements of the site selection range. Use geographic information system (GIS) software or programming tools to extract grid attribute information. Divide the entire site selection range into a series of grids according to the determined grid size. Set a unique identifier for each grid.

[0027] S3-2, cost reduction: create a comprehensive cost matrix corresponding to each square, store the comprehensive cost value of each square, and set the initial value to -1;

[0028] S3-3, calculate each square C total , and obtain the comprehensive cost matrix within the site selection range;

[0029] S4, comprehensive cost surface generation and automatic path planning and identification, specifically:

[0030] S4-1, comprehensive cost surface generation: Using GIS software or visualization tools, the comprehensive cost matrix is ​​graphically stored and displayed in the actual geographic space through coordinate registration, projection, and grid mosaic, forming a comprehensive cost distribution surface and distribution map;

[0031] S4-2, automatic path planning and identification: Using the path optimization algorithm of distance accumulation value, the minimum cost path from the starting point to the end point is found on the comprehensive cost surface, which is the optimal water network path.

[0032] Once the optimal water network path is identified, verification and adjustment are performed. The initially selected water network path is verified along the route using imagery and on-site verification. This approach takes into account unquantified local factors and unexpected circumstances, and fine-tunes the costing model to ensure that the path plan aligns with theoretical optimality and actual conditions. Furthermore, a feedback mechanism is established to collect data during implementation, providing a more accurate basis for costing future projects.

[0033] The path optimization algorithm of the distance accumulation value is Dijkstra or A* path optimization algorithm.

[0034] The continuous function The coefficient h (h>0) is used to fit the actual construction cost of different slopes under actual construction conditions. The value of the coefficient h makes the slope of the function curve similar to the slope of the actual construction conditions.

[0035] The smaller the h value is, the greater the construction cost will be as the slope increases. Tables 1 and 2 below list the vertical coefficient costs when h=10 and h=20 respectively. Figure 12 and 13 The corresponding curve graph.

[0036] Table 1

[0037]

[0038] Table 2

[0039]

[0040] From Table 1 and Table 2, we can see that when the slope is 0, C vc =1, indicating that the construction cost on flat land is 1 times the coefficient; when the coefficient h remains unchanged, the greater the slope θ, the higher the vertical coefficient cost C vc The value of shows an exponential growth trend, that is, as the slope increases, the construction difficulty and cost show an exponential growth trend; when the slope θ remains unchanged, the larger the coefficient h is, the higher the vertical coefficient cost C is. vc The smaller the value, the different projects have different construction costs at the same slope.

[0041] The aforementioned construction-prohibition factors refer to areas where construction activities are explicitly prohibited or extremely restricted due to legal, ecological, geological, and other reasons. These areas are generally of extremely high conservation value or potential risks. Any construction activities crossing or within them may cause irreversible environmental damage, ecological imbalance, or safety hazards. Their characteristics are:

[0042] Absolute: No-construction zones are usually clearly defined by law or policy, and once established, no exceptions are allowed.

[0043] High Cost: Attempting to build within these zones will incur extremely high legal risks and actual costs, and may not even be feasible.

[0044] Ecological or geological sensitivity: factors such as ecological red line areas, basic farmland protection areas, geological fault zones, etc.

[0045] Impact on distance costs: The prohibited construction factor directly determines the infeasibility of the route. Therefore, it must be excluded as a hard constraint in the early stages of route planning to avoid unnecessary planning costs and waste of resources.

[0046] The vertical coefficient factors refer to factors that affect the difficulty and cost of construction in the vertical direction of the terrain, namely elevation, slope, and aspect. These factors indirectly affect the choice and cost of water network paths by changing the physical characteristics of the terrain surface. Its characteristics are:

[0047] Continuity: Unlike the prohibited construction factor, the vertical coefficient factor changes continuously in geographic space, such as the gradual transition of slope from gentle to steep.

[0048] Influence coefficient: Affects the distance cost through a coefficient. For example, the greater the slope, the higher the construction difficulty and cost. Therefore, it is necessary to multiply by the corresponding coefficient to reflect this increased cost.

[0049] Topographic dependence: These factors are closely dependent on the topography and are an important part of terrain analysis.

[0050] Impact on distance cost: The vertical coefficient factor adjusts the cost calculation in path selection, making path selection more cautious and reasonable under conditions such as high slopes and complex terrain, avoiding cost overruns and increased risks caused by ignoring terrain factors.

[0051] The cost difference factor refers to the factors that cause significant differences in the cost of construction activities under different categories or conditions. These differences may be due to various socio-economic factors such as land use type, population concentration, and economic development level. Its characteristics are:

[0052] Categorization: Cost difference factors are usually differentiated according to certain classification standards, such as land use type, and the cost differences between different categories are significant.

[0053] Economics: These factors are directly related to the economic costs of construction activities, such as the land acquisition costs for urban and rural land, and the construction disruption costs for densely populated and sparsely populated areas.

[0054] Flexibility: Compared with the prohibited construction obstacle factor and the vertical coefficient factor, the cost difference factor is more flexible in path planning and can optimize path selection through cost-benefit analysis.

[0055] Impact on Distance Cost: The cost variance factor provides a more refined economic consideration for route planning by detailing cost variances under different conditions. During the planning process, the most economically reasonable route can be selected by comparing the total costs of different routes traversing areas with varying cost variances.

[0056] Each square in the grid is processed as follows:

[0057] Extracting grid attribute information, such as topography, slope, ecological red line occupancy, and land use type data;

[0058] According to the unified cost calculation model, calculate the prohibition barrier cost C in the grid. nbz , vertical coefficient cost C vc and cost difference cost C cd , and finally calculate the comprehensive cost value C total .

[0059] The method of the present invention has the following advantages:

[0060] (1) Enhanced multi-source data fusion and dynamic adaptability integrate multi-dimensional spatial data such as terrain, ecology, and engineering to build a unified analysis framework that supports dynamic weight allocation and real-time obstacle response. Compared with traditional two-dimensional planning methods, this method can quantitatively assess the conflict between ecological protection and engineering feasibility, avoid fixed coefficient correction errors, and improve the scientific and economic efficiency of path selection under complex constraints.

[0061] (2) Intelligent path generation technology based on spatial analysis integrates GIS spatial analysis, grid cost calculation, and path search algorithms. Through regional grid division, comprehensive cost matrix construction, and automatic optimization strategies, it can achieve efficient identification of water network paths. Compared with traditional manual surveys or simple shortest path models, this technology supports parallel computing of millions of grids and can quickly respond to sudden obstacles or constraint changes (such as the addition of new protected areas) through dynamic weight adjustment, reducing the frequency of manual intervention.

[0062] (3) Dynamic verification and adaptive optimization mechanism: Through field image comparison, constraint sensitivity analysis, and dynamic adjustment of model parameters, a closed-loop process of "planning-verification-correction" is established. For example, in response to new ecological protection areas or construction restrictions, this method can perform local path replanning (local grid cost matrix reconstruction) to ensure that the final solution is both theoretically optimal and fits the actual engineering conditions. This mechanism effectively improves the efficiency of local route replanning and identification caused by incomplete early data or missing constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a schematic diagram of the starting and ending points of the water network in Example 1.

[0064] Figure 2 Schematic diagram of the prohibited construction obstacle factors in Example 1.

[0065] Figure 3 This is an example diagram of the vertical coefficient factor (slope) of Example 1.

[0066] Figure 4 This is an example diagram of the cost difference factor in Example 1.

[0067] Figure 5 This is an example diagram of regional grid consistency division.

[0068] Figure 6 This is the comprehensive cost distribution diagram of Example 1, where:

[0069] The legend in the lower left corner shows the color and Figure 6 The display of comprehensive cost is consistent, and the numerical value represents the comprehensive cost value corresponding to the color. Figure 6 The squares correspond to the colors.

[0070] Figure 7 This is the path automatic planning and recognition result diagram of Example 1.

[0071] Figure 8 This is a schematic diagram of fine-tuning the line section across the building in Example 1.

[0072] Figure 9 Schematic diagram of automatic line selection considering line length priority (blue line) and line descent priority (red line) in Example 1 and Example 2.

[0073] Figure 10 A schematic diagram is generated for the integrated cost distribution surface and distribution map of the present invention.

[0074] Figure 11 This is a schematic diagram of the distribution diagram after verification and adjustment of the present invention, wherein the red X represents the comprehensive cost of the grid that needs to be adjusted according to actual conditions.

[0075] Figure 12 This is a graph showing the changes in Cvc and θ when the coefficient h=10.

[0076] Figure 13 This is a graph showing the changes in Cvc and θ when the coefficient h=20. DETAILED DESCRIPTION

[0077] Example 1: Figure 1 As shown, the starting and ending locations of a certain place are clearly defined, and the method of the present invention is used to automatically plan and identify the water network route. The specific steps are as follows:

[0078] S1, identify the prohibited construction barrier factor Fnbz and calculate the prohibited construction barrier cost C nbz ;

[0079] like Figure 2 As shown in the figure, the ecological red line, basic farmland, and urban development boundary are selected to form the prohibition barrier factor Fnbz, and the binary judgment method is used to determine the prohibition barrier cost C nbz If the region crosses the ecological red line, then Cnbz = +∞; if the region does not cross the ecological red line, then C nbz = 0.

[0080] S2, identify the vertical coefficient factor Fvc and calculate the vertical coefficient cost C vc ;

[0081] The slope is selected to form the vertical coefficient factor Fvc, and the vertical coefficient cost C is calculated by splitting different intervals according to the slope value. vc ,like Figure 3 As shown, set the buildable slope value The range is between 0° and 60°. The slope value is divided into three intervals: 0°-20°, 20°-40°, and 40°-60°. The vertical coefficient cost C of each interval is determined based on function calculation and construction experience. vc , as shown in Table 3 below:

[0082] Table 3 Vertical coefficient cost C in different slope intervals vc Calculation table

[0083]

[0084] S3, identify the cost difference factor Fcd and calculate the cost difference cost C cd ;

[0085] Different land use types are selected to form the cost difference factor Fcd, such as Figure 4 As shown in the figure, the cost difference C is calculated based on the water network construction price of the path crossing the land use type under unit distance. cd , as shown in Table 4 below.

[0086] Table 4. Cost difference of water network construction price for different land use types under unit distance. cd Calculation table

[0087]

[0088] S4, unified cost imputation model construction;

[0089] The unified cost calculation model is as follows:

[0090]

[0091] In this embodiment, the vertical coefficient factor weight P is set vc =1, cost difference factor weight P cd =1, that is, the importance of each factor is considered equally, then C total The algorithm becomes:

[0092] .

[0093] S5, regional grid consistency division, cost reduction and generation of comprehensive cost surface;

[0094] After completing the construction of the unified cost reduction model, the site selection range is divided into regional grids with a side length of 10 meters, such as Figure 5 As shown, the unified cost reduction model is used to reduce the comprehensive cost C of each grid. total Finally, the comprehensive cost matrix within the site selection range is obtained. The comprehensive cost matrix is ​​stored and displayed in a graphical manner in the actual geographic space using GIS software to form a comprehensive cost distribution surface, such as Figure 6 shown.

[0095] S6, automatic path planning and recognition;

[0096] The Dijkstra algorithm based on distance accumulation is used to automatically find the minimum comprehensive cost C from the starting point to the end point on the comprehensive cost surface. total Path, that is, the optimal water network path from the starting point to the end point, such as Figure 7 shown.

[0097] S7, calibration and adjustment;

[0098] Perform image verification along the automatically identified water network path, fine-tune the lines that do not match the actual topography, and finally obtain the optimal water network path identified, such as Figure 8 As shown, red is the automatic identification line and yellow is the fine-tuning line.

[0099] Example 2: Based on Example 1, in the process of building the unified cost reduction model in step S4, the vertical coefficient factor weight P is set. vc =3, cost difference factor weight P cd =1, its practical engineering significance is to give priority to the vertical descent of the line and secondly consider the construction cost per unit distance, then C total The algorithm becomes:

[0100]

[0101] Under other set conditions and with the steps unchanged, a new water network path that prioritizes vertical descent is finally obtained, such as Figure 9 The red line path is shown.

Claims

1. Automatic water network path planning and identification method based on spatial data, characterized by The method comprises the following steps: S1, multi-dimensional factor identification and unified quantitative standard formulation, as well as unified cost model calculation, specifically including: S1-1, multi-dimensional factor identification, including the construction prohibition factor (Fnbz), the vertical coefficient factor (Fvc), and the cost difference factor (Fcd); S1-2, the unified quantitative standard of multi-dimensional factors is established, specifically: S1-2-1, the prohibition of construction factors adopts the binary judgment method to formulate quantitative standards and determine the cost value of the prohibition of construction factors (C nbz ); In the prohibited construction zone C nbz = +∞, indicating that it cannot be crossed; if it is not in the prohibited construction area, then C nbz = 0, indicating free traversal; S1-2-2, the vertical coefficient factor (Fvc) adopts a continuous function or a piecewise function to formulate a quantitative standard, specifically: The continuous function is to establish the correlation function between the slope value and the construction cost, and calculate the vertical coefficient cost C vc , C vc The calculation formula is as follows: represents the slope value, h (h>0) is the real construction cost coefficient, which is used to fit the real construction cost of different slopes under real construction conditions. The coefficient h is selected so that the slope of the function curve is similar to the slope of the real construction conditions. If the coefficient value cannot make the slope of the function curve similar to the slope of the real construction conditions, a piecewise function is used to formulate the quantitative standard; The piecewise function is divided into several grade intervals according to the slope size. Each grade interval corresponds to a cost coefficient. The greater the slope, the higher the cost. The vertical coefficient cost C of different slope intervals vc Listed in the table below: S1-2-3, the cost difference factor is quantified based on the cost and degree of difference of different land area types; S2 calculates the comprehensive cost by multiplying the quantitative value of each factor by its corresponding weight. The specific algorithm is as follows: Among them, C total is the comprehensive cost, P vc 、P cd are the weights of the vertical coefficient factor and the cost difference factor, C vc 、C cd are the quantitative cost values ​​of these two factors respectively; The weight distribution is based on the importance and influence of each factor in the water network path selection, and is determined by expert scoring, analytic hierarchy process (AHP), entropy weight method or other multi-criteria decision-making methods; the prohibition barrier cost C nbz A binary decision method is used, with a value of 0 or +∞, so there is no weighting; S3, regional grid consistency division, cost reduction, and generation of comprehensive cost surface, specifically: S3-1, Regional Grid Division: Determine the grid width based on the size and accuracy requirements of the site selection range. Use geographic information system (GIS) software or programming tools to extract grid attribute information. Divide the entire site selection range into a series of grids according to the determined grid size. Set a unique identifier for each grid. S3-2, cost reduction: create a comprehensive cost matrix corresponding to each square, store the comprehensive cost value of each square, and set the initial value to -1; S3-3, calculate each square C total , and obtain the comprehensive cost matrix within the site selection range; S4, comprehensive cost surface generation and automatic path planning and identification, specifically: S4-1, comprehensive cost surface generation: Using GIS software or visualization tools, the comprehensive cost matrix is ​​graphically stored and displayed in the actual geographic space through coordinate registration, projection, and grid mosaic, forming a comprehensive cost distribution surface and distribution map; S4-2, automatic path planning and identification: Using the path optimization algorithm of distance accumulation value, the minimum cost path from the starting point to the end point is found on the comprehensive cost surface, which is the optimal water network path.

2. The method for automatic planning and identification of water network paths based on spatial data according to claim 1, characterized in that After the optimal water network path is identified, verification and adjustment are carried out. The initially selected water network path is verified along the line by image verification and on-site verification. Unquantified local factors and emergencies are taken into consideration, and the cost reduction model is fine-tuned to ensure that the path plan conforms to the theoretical optimum and the actual situation.

3. The method for automatic planning and identification of water network paths based on spatial data according to claim 1, characterized in that Each square in the grid is processed as follows: (1) Extracting grid attribute information, such as topography, slope, ecological red line occupancy, and land use type data; (2) According to the unified cost calculation model, calculate the construction prohibition barrier cost C in each grid. nbz , vertical coefficient cost C vc and cost difference cost C cd , and finally calculate the comprehensive cost value C total .

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