Pipeline routing line selection method and system based on path planning algorithm and analytic hierarchy process

Abstract

The invention belongs to the field of submarine routing planning, particularly relates to a pipeline routing line selection method and system based on a path planning algorithm and analytic hierarchy process, and aims to improve the routing line selection efficiency and precision and reduce the routing risk in the pipeline routing line selection process. The method comprises the following steps: establishing an evaluation sample matrix X based on measured value data of evaluation indexes, and converting pixel points of a target sea area topographic map into nodes of a two-dimensional matrix to obtain a grid obstacle map; establishing a submarine pipeline routing risk quantitative evaluation model; converting the sea area topographic map into a risk level grey-scale map through the risk quantitative evaluation model of the submarine pipeline; and carrying out route planning through a route planning algorithm based on the grid obstacle map, and finding a route with the minimum accumulated gray value in the target sea area risk level gray-scale map through a greedy algorithm to serve as a final route. According to the method, risk parameters are taken into consideration for the generated path, and finally, the obtained route is a routing path with small length and low overall risk.

Description

Background Art

[0002] The route selection of submarine pipelines will affect the construction technology plan and overall investment. At present, the route selection of submarine pipelines is mostly done by manual work on the map, which depends largely on the personal experience of engineers. At the same time, with the increasing importance and scarcity of marine resources, the route selection of submarine pipelines will also have a certain impact on social benefits.

[0003] The route selection of submarine pipelines will affect the engineering technology plan and the overall investment. At present, the route selection of submarine pipelines is mostly done by manual work on the map, which depends largely on the experience level of the engineer. At the same time, with the increasing importance and scarcity of marine resources, the route selection of submarine pipelines will also have a certain impact on social benefits.

[0004] As a classic algorithm for path planning, Dijkstra algorithm can find the optimal solution from a global perspective, and its advantage lies in its high exploration accuracy. Dijkstra algorithm research has been widely involved in more than ten fields, including computer, transportation, control engineering, surveying and mapping, electrical engineering, logistics, safety, communication and mining. There are curvature restrictions in pipeline line selection, and the curvature verification of the route cannot be achieved by the Dijkstra algorithm itself. In order to solve the problem of multiple inflection points in the path solved by the graphic grid, the Bresenham algorithm is used to generate straight lines and make judgments in the graphics, and the new straight lines are used to replace the original multi-inflection point line segments.

[0005] In order to scientifically evaluate the potential risks of submarine pipeline route selection, a risk level assessment method is established based on the grey correlation degree, combined with the level variable characteristic value and the importance weight of each risk factor assessed by experts. And with the risk level as the base map, the route with the lowest risk is planned through the path finding algorithm. Summary of the invention

[0006] In order to solve the above-mentioned problem in the prior art, namely pipeline routing selection, the present invention provides a pipeline routing selection method based on a path planning algorithm and hierarchical analysis, the method comprising:

[0007] Step S100, collecting a topographic map of the target sea area and measurement data of evaluation indicators at various locations in the target sea area; the evaluation indicators are pre-defined m evaluation parameters required for the target sea area;

[0008] Step S200, establishing an evaluation sample matrix X based on the measurement value data of the evaluation index, converting the pixel points of the target sea area topographic map into nodes of a two-dimensional matrix to obtain a two-dimensional matrix, and the two-dimensional matrix constitutes a grid obstacle map;

[0009] Step S300, establishing an evaluation index matrix S according to pre-defined evaluation indexes and grade classification standards;

[0010] Step S400, by comparing and normalizing the evaluation sample matrix X with the evaluation index matrix S, the risk level of each location is obtained, and an evaluation standard matrix B is constructed;

[0011] Step S500, constructing a weight matrix W through a hierarchical analysis method based on the evaluation index;

[0012] Step S600, assigning a weight matrix W to the evaluation standard matrix B, obtaining a weighted average value, and establishing a quantitative assessment model for submarine pipeline routing risks;

[0013] Step S700, converting the sea area topographic map into a risk level grayscale map through the submarine pipeline routing risk quantitative assessment model, wherein the grayscale value of the image pixel in the risk level grayscale map is proportional to the risk level H;

[0014] Step S800, performing route planning based on the grid obstacle map through a path planning algorithm and a correction algorithm to obtain a corrected route;

[0015] Step S900, using a greedy algorithm to find the path with the least cumulative grayscale value in the revised route in the target sea area risk level grayscale map as the final route.

[0016] In some preferred embodiments, the two-dimensional matrix specifically includes:

[0017] Each pixel point of the target sea area topographic map corresponds to a node in a two-dimensional matrix, and each node unit is called a grid cell;

[0018] Each node in the two-dimensional matrix has 4 or 8 adjacent nodes, and the 8-neighborhood connection method is used to expand the successor node.

[0019] In some preferred embodiments, the evaluation index specifically includes:

[0020] Modules on engineering geological conditions, marine environmental conditions and marine facilities and development activities;

[0021] The engineering geological conditions include topographic and geomorphic features, bed instability, adverse engineering geology, seismic activity and regional stability. Specific indicators include average bed slope, friction angle, undrained shear strength, and annual bed erosion and deposition amplitude.

[0022] Marine environmental conditions include meteorological factors, marine hydrology, seawater and seabed corrosion environment. Specific indicators include wind speed, waves, ocean currents and sea ice, etc.

[0023] The status of marine facilities and development activities includes obstacles or hazards in the pipeline routing area, existing and planned submarine pipelines, cable conditions, fisheries and port channels.

[0024] In some preferred implementations, the step S500 specifically includes:

[0025] Step S510, stratifying the evaluation indicators to determine the parent item of each evaluation parameter in the evaluation indicators;

[0026] Step S520, compare the importance of each indicator, and construct a binary judgment matrix P according to the fuzzy mood operator and the fuzzy scale;

[0027] Step S530, perform consistency judgment on the binary judgment matrix P according to the consistency index CR. If the consistency ratio is less than the preset threshold, the consistency index and weight vector W sorted from small to large in importance are obtained through inspection; wherein, the consistency index λ max is the maximum characteristic root of the judgment matrix, RI is the correction coefficient;

[0028] If the consistency ratio is greater than the preset threshold, the test fails and the process goes to step S540;

[0029] Step S540, repeating step S520, re-comparing until passing the test;

[0030] Step S550: construct a weight matrix W=ω based on the consistency indexes and weight vector W sorted in ascending order of importance. i (ω1,ω2,...ω m ).

[0031] In some preferred implementations, the step S800 specifically includes:

[0032] Step S810, performing route exploration on the grid obstacle map through a path-finding algorithm to obtain an initial route;

[0033] Step S820, smoothing the initial route using a path optimization algorithm to obtain an optimized route;

[0034] Step S830, completing curvature correction by adding arc obstacles to the turning points in the optimized path to obtain a corrected route.

[0035] In some preferred embodiments, the smoothing process specifically includes:

[0036] Reduce the inflection points on the initial route and shorten the overall route length.

[0037] In some preferred implementations, the step S810 specifically includes:

[0038] Step S811, randomly select two points on the initial route to generate a straight line, and the points on the straight line are all pixel points on the grid obstacle map;

[0039] Step S812, establish coordinates for the grid obstacle map, and the straight line formula between the starting point (x0, y0) and the end point (x1, y1) is:

[0040] According to the Bresenham algorithm, when x moves on the grid obstacle map, the y value is calculated according to the formula and moved to the nearest node to obtain the grid obstacle map coordinates and determine whether it crosses the obstacle; if it does not cross the obstacle, the new straight line can be used to replace the original multi-inflection point line segment.

[0041] In some preferred implementations, the step S830 specifically includes:

[0042] Step S831, adding an arc obstacle at a position where the curvature radius of the path turning point in the optimized path does not exceed the minimum allowable curvature radius, replacing the path turning point;

[0043] Step S832, obtaining the arc curvature at the additional arc obstacle by using a curvature calculation formula;

[0044] Step S833, compare the distance difference between two adjacent arc obstacles. If the two arc routes meet the requirement of being connected, complete the curvature correction of the inflection point and enter step S834; if the two arc routes do not meet the requirement of being connected, return to step S820 to start re-obtaining the optimized route;

[0045] Step S834, repeating steps S831 to S833 until all inflection points are traversed to obtain a corrected route.

[0046] In some preferred implementations, the step S833 specifically includes:

[0047] The distance between the tangent point of the arc to the line connecting two adjacent inflection points and the inflection point is Where R is the minimum radius of curvature, and θ is the angle formed by the two pipeline routes at the inflection point;

[0048] The length of the line segment between two adjacent inflection points is L. The distances between the two end points and the tangent point of the line segment are D1 and D2 respectively. The distance difference ε is ε=D1+D2-L. If ε<<0, it means that there is a certain distance between the two arcs, which meets the requirement that the two arc routes are connected. If ε>0, it means that there is an intersection between the two arcs, which does not meet the requirement that the two arc routes are connected, and the route needs to be redesigned.

[0049] Another aspect of the present invention provides a pipeline routing system based on a path planning algorithm and hierarchical analysis, which includes:

[0050] A grid obstacle map acquisition module is configured to collect a topographic map of the target sea area and measurement data of evaluation indicators at various locations in the target sea area; the evaluation indicators are pre-defined m evaluation parameters required for the target sea area;

[0051] An evaluation sample matrix X is established based on the measurement value data of the evaluation index, and the pixel points of the target sea area topographic map are converted into nodes of a two-dimensional matrix to obtain a two-dimensional matrix, and the two-dimensional matrix constitutes a grid obstacle map;

[0052] An evaluation index matrix acquisition module is configured to establish an evaluation index matrix S according to pre-defined evaluation indexes and grade classification standards;

[0053] An evaluation standard matrix construction module is configured to obtain the risk level of each location by comparing and normalizing the evaluation sample matrix X with the evaluation index matrix S, and construct an evaluation standard matrix B;

[0054] A weight matrix construction module, configured to construct a weight matrix W based on the evaluation index by using a hierarchical analysis method;

[0055] The risk quantitative assessment module is configured to assign a weight matrix W to the evaluation standard matrix B, obtain a weighted average value, and establish a submarine pipeline routing risk quantitative assessment model;

[0056] A risk level grayscale image conversion module is configured to convert the sea area topographic map into a risk level grayscale image through a submarine pipeline routing risk quantitative assessment model, wherein the grayscale value of the image pixel point in the risk level grayscale image is proportional to the risk level H;

[0057] A corrected route acquisition module is configured to perform route planning based on a grid obstacle map through a path planning algorithm and a correction algorithm to obtain a corrected route;

[0058] The final route selection module is configured to use a greedy algorithm to find the path with the least cumulative grayscale value in the corrected route for the grayscale map of the risk level of the target sea area as the final route.

[0059] Beneficial effects of the present invention:

[0060] (1) The present invention reduces the overall length of the generated pipeline route and reduces the project cost through systematic design; the manual operation mode on the map is changed to a systematized one, so that the line selection process no longer depends on the individual experience level of the engineer, and the risk parameters are taken into consideration for the generated path. The final route obtained is a route path with a short length and low construction difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0062] Figure 1 It is a flow chart of a pipeline routing method based on a path planning algorithm and hierarchical analysis in an embodiment of the present invention;

[0063] Figure 2 It is a schematic diagram of the risk assessment of pipeline routing method based on path planning algorithm and hierarchical analysis;

[0064] Figure 3 It is the pseudo code of Dijkstra algorithm for pipeline routing selection method based on path planning algorithm and hierarchical analysis;

[0065] Figure 4 This is a comparison diagram of the 4-neighborhood and 8-neighborhood connection relationships of the pipeline routing method based on the path planning algorithm and hierarchical analysis;

[0066] Figure 5 It is a case of connecting adjacent curved pipes based on the pipeline routing method based on the path planning algorithm and the hierarchical analysis;

[0067] Figure 6 It is a grayscale image of the proposed pipeline construction area based on the pipeline routing selection method based on the path planning algorithm and hierarchical analysis. DETAILED DESCRIPTION

[0068] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0069] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0070] The pipeline routing method based on path planning algorithm and hierarchical analysis of the present invention comprises:

[0071] In order to more clearly illustrate the pipeline routing method based on the path planning algorithm and hierarchical analysis of the present invention, each step in the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0072] Step S100, collecting a topographic map of the target sea area and measurement data of evaluation indicators at various locations in the target sea area; the evaluation indicators are pre-defined m evaluation parameters required for the target sea area;

[0073] Step S200, establishing an evaluation sample matrix X based on the measurement value data of the evaluation index, converting the pixel points of the target sea area topographic map into nodes of a two-dimensional matrix to obtain a two-dimensional matrix, and the two-dimensional matrix constitutes a grid obstacle map;

[0074] In this embodiment, the two-dimensional matrix specifically includes:

[0075] Each pixel point of the target sea area topographic map corresponds to a node in a two-dimensional matrix, and each node unit is called a grid cell;

[0076] like Figure 4 As shown, each node in the two-dimensional matrix has 4 or 8 adjacent nodes, and the 8-neighborhood connection method is used to expand the successor node.

[0077] Since the 4-neighborhood connection has fewer exploration nodes, its exploration time is fast but the accuracy is not enough. The 8-neighborhood connection relationship has higher accuracy.

[0078] In this embodiment, the evaluation index specifically includes:

[0079] Modules on engineering geological conditions, marine environmental conditions and marine facilities and development activities;

[0080] The engineering geological conditions include topographic and geomorphic features, bed instability, adverse engineering geology, seismic activity and regional stability. Specific indicators include average bed slope, friction angle, undrained shear strength, and annual bed erosion and deposition amplitude.

[0081] Marine environmental conditions include meteorological factors, marine hydrology, seawater and seabed corrosion environment. Specific indicators include wind speed, waves, ocean currents and sea ice, etc.

[0082] The status of marine facilities and development activities, including obstacles or hazards in pipeline routing areas, existing and planned submarine pipelines, cable conditions, fisheries and port channels;

[0083] Each module can be further refined into secondary indicators. For example, under the marine environment conditions, there are waves, currents, tides, typhoons and other items. The evaluation level of each indicator is determined according to pipeline accidents or paper data as shown in Table 1: Table 1

[0084] In this embodiment, the step S500 specifically includes:

[0085] Step S510, stratifying the evaluation indicators to determine the parent item of each evaluation parameter in the evaluation indicators;

[0086] Step S520, compare the importance of each indicator, and construct a binary judgment matrix P according to the fuzzy mood operator and the fuzzy scale;

[0087] The fuzzy scale is assigned using a 1-9 scale method;

[0088] Step S530, perform consistency judgment on the binary judgment matrix P according to the consistency index CR. If the consistency ratio is less than the preset threshold, the consistency index and weight vector W sorted from small to large in importance are obtained through inspection; wherein, the consistency index λ max is the maximum characteristic root of the judgment matrix, RI is the correction coefficient;

[0089] If the consistency ratio is greater than the preset threshold, the test fails and the process goes to step S540;

[0090] Step S540, repeating step S520, re-comparing until passing the test;

[0091] Step S550: construct a weight matrix W=ω based on the consistency indexes and weight vector W sorted in ascending order of importance. i (ω1,ω2,...ω m ).

[0092] In this embodiment, the risk level grayscale map is as follows: Figure 6 As shown, specifically including:

[0093] The grayscale value of the image pixel corresponds to the risk level, and the darker the color, the greater the risk.

[0094] Step S600, assigning a weight matrix W to the evaluation standard matrix B, obtaining a weighted average value, and establishing a quantitative assessment model for submarine pipeline routing risks;

[0095] Step S700, converting the sea area topographic map into a risk level grayscale map through the submarine pipeline routing risk quantitative assessment model, wherein the grayscale value of the image pixel in the risk level grayscale map is proportional to the risk level H;

[0096] Step S800, performing route planning based on the grid obstacle map through a path planning algorithm to obtain a corrected route;

[0097] In this embodiment, the step S800 specifically includes:

[0098] Step S810, performing route exploration on the grid obstacle map through a path-finding algorithm to obtain an initial route;

[0099] In this embodiment, the step S810 specifically includes:

[0100] Step S811, randomly select two points on the initial route to generate a straight line, and the points on the straight line are all pixel points on the grid obstacle map;

[0101] Step S812: According to the Bresenham algorithm, coordinates are established for the grid obstacle map. The straight line formula between the starting point (x0, y0) and the end point (x1, y1) is:

[0102] When x moves on the grid obstacle map, the y value is calculated according to the formula and moved to the nearest node to obtain the grid obstacle map coordinates and determine whether it crosses the obstacle; if it does not cross the obstacle, the new straight line can be used to replace the original multi-inflection point line segment. Figure 5 shown.

[0103] Step S820, smoothing the initial route using a path optimization algorithm to obtain an optimized route;

[0104] In this embodiment, the smoothing process specifically includes:

[0105] Reduce the inflection points on the initial route and shorten the overall route length.

[0106] Step S830, completing curvature correction by adding arc obstacles to the turning points in the optimized path to obtain a corrected route.

[0107] In some preferred implementations, the step S830 specifically includes:

[0108] Step S831, adding an arc obstacle at a position where the curvature radius of the path turning point in the optimized path does not exceed the minimum allowable curvature radius, replacing the path turning point;

[0109] Step S832, obtaining the arc curvature at the additional arc obstacle by using a curvature calculation formula;

[0110] Step S833, compare the distance difference between two adjacent arc obstacles. If the two arc routes meet the requirement of being connected, complete the curvature correction of the inflection point and enter step S834; if the two arc routes do not meet the requirement of being connected, return to step S820 to start re-obtaining the optimized route;

[0111] In this embodiment, the step S833 specifically includes:

[0112] The distance between the tangent point of the arc to the line connecting two adjacent inflection points and the inflection point is Where R is the minimum radius of curvature, and θ is the angle formed by the two pipeline routes at the inflection point;

[0113] The length of the line segment between two adjacent inflection points is L. The distances between the two end points and the tangent point of the line segment are D1 and D2 respectively. The distance difference ε is ε=D1+D2-L. If ε<<0, it means that there is a certain distance between the two arcs, which meets the requirement that the two arc routes are connected. If ε>0, it means that there is an intersection between the two arcs, which does not meet the requirement that the two arc routes are connected, and the route needs to be redesigned.

[0114] Step S834, repeating steps S831 to S833 until all inflection points are traversed to obtain a corrected route.

[0115] In this embodiment, the path finding algorithm specifically includes:

[0116] Dijkstra's algorithm: Repeatedly detect unvisited nodes adjacent to the current path, update the tentative value by assigning the smaller of the two values ​​to the node; then take the node with the smallest value in the adjacent set as the current node, and repeat the process until the target node is reached;

[0117] A* algorithm: Based on the Dijkstra algorithm, a heuristic function is added to represent the estimated cost of the best path from the last node of the explored path to the target node.

[0118] The difference between the two is that the Dijkstra algorithm must explore the entire area, while the A* algorithm reduces the exploration area compared to the Dijkstra algorithm, reducing the time required for searching but cannot guarantee the shortest route. This is because it cannot cover all possible nodes accessible by the path at the same time.

[0119] In this embodiment, the pseudo code of the path-finding algorithm Dijkstra is as follows: Figure 3 , the digital obstacle image is specified as G, the weight of each edge is w, and the starting point is s; the shortest distance d[u] from the starting point s to each vertex v in the graph G is calculated and retained, where the function EXTRACT-MIN(Q) deletes the vertex u with the smallest d[v] value in the vertex set Q from Q and returns u.

[0120] In this embodiment, in step S700, when determining the pipeline route selection in the grid-based graph, a sharp bend may occur. The initial route generated by the Dijkstra algorithm usually does not meet the requirements. To solve this problem, the initial "original" optimal path can be smoothed by spline technology. Laplace smoothing is an iterative algorithm that can be used to smooth polylines or polygonal networks, but the pipeline does not simply meet the "smooth" requirement. The problem can be solved by the Bresenham algorithm.

[0121] The second embodiment of the present invention discloses a pipeline routing system based on a path planning algorithm and hierarchical analysis, the system comprising:

[0122] A grid obstacle map acquisition module is configured to collect a topographic map of the target sea area and measurement data of evaluation indicators at various locations in the target sea area; the evaluation indicators are pre-defined m evaluation parameters required for the target sea area;

[0123] An evaluation sample matrix X is established based on the measurement value data of the evaluation index, and the pixel points of the target sea area topographic map are converted into nodes of a two-dimensional matrix to obtain a two-dimensional matrix, and the two-dimensional matrix constitutes a grid obstacle map;

[0124] An evaluation index matrix acquisition module is configured to establish an evaluation index matrix S according to pre-defined evaluation indexes and grade classification standards;

[0125] An evaluation standard matrix construction module is configured to obtain the risk level of each location by comparing and normalizing the evaluation sample matrix X with the evaluation index matrix S, and construct an evaluation standard matrix B;

[0126] A weight matrix construction module, configured to construct a weight matrix W based on the evaluation index by using a hierarchical analysis method;

[0127] The risk quantitative assessment module is configured to assign a weight matrix W to the evaluation standard matrix B, obtain a weighted average value, and establish a submarine pipeline routing risk quantitative assessment model;

[0128] A risk level grayscale image conversion module is configured to convert the sea area topographic map into a risk level grayscale image through a submarine pipeline routing risk quantitative assessment model, wherein the grayscale value of the image pixel point in the risk level grayscale image is proportional to the risk level H;

[0129] A corrected route acquisition module is configured to perform route planning based on a grid obstacle map through a path planning algorithm and a correction algorithm to obtain a corrected route;

[0130] The final route selection module is configured to use a greedy algorithm to find the path with the least cumulative grayscale value in the corrected route for the grayscale map of the risk level of the target sea area as the final route.

[0131] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0133] It should be noted that the pipeline routing system based on path planning algorithm and hierarchical analysis provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations of the present invention.

[0134] The terms "first", "second", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0135] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0136] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A pipeline routing method based on path planning algorithm and hierarchical analysis, characterized in that: The method comprises: Step S100, collecting a topographic map of the target sea area and measurement data of evaluation indicators at various locations in the target sea area; the evaluation indicators are pre-defined m evaluation parameters required for the target sea area; Step S200, establishing an evaluation sample matrix X based on the measurement value data of the evaluation index, converting the pixel points of the target sea area topographic map into nodes of a two-dimensional matrix to obtain a two-dimensional matrix, and the two-dimensional matrix constitutes a grid obstacle map; Step S300, establishing an evaluation index matrix S according to pre-defined evaluation indexes and grade classification standards; Step S400, by comparing and normalizing the evaluation sample matrix X with the evaluation index matrix S, the risk level of each location is obtained, and an evaluation standard matrix B is constructed; Step S500, constructing a weight matrix W through a hierarchical analysis method based on the evaluation index; Step S600, assigning a weight matrix W to the evaluation standard matrix B, obtaining a weighted average value, and establishing a quantitative assessment model for submarine pipeline routing risks; Step S700, converting the sea area topographic map into a risk level grayscale map through the submarine pipeline routing risk quantitative assessment model, wherein the grayscale value of the image pixel in the risk level grayscale map is proportional to the risk level H; Step S800, performing route planning based on the grid obstacle map through a path planning algorithm and a correction algorithm to obtain a corrected route; Step S900, using a greedy algorithm to find the path with the least cumulative grayscale value in the revised route in the target sea area risk level grayscale map as the final route.

2. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 1 is characterized in that: The two-dimensional matrix specifically includes: Each pixel point of the target sea area topographic map corresponds to a node in a two-dimensional matrix, and each node unit is called a grid cell; Each node in the two-dimensional matrix has 4 or 8 adjacent nodes, and the 8-neighborhood connection method is used to expand the successor node.

3. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 1 is characterized in that: The evaluation indicators specifically include: Modules on engineering geological conditions, marine environmental conditions and marine facilities and development activities; The engineering geological conditions include topographic and geomorphic features, bed instability, adverse engineering geology, seismic activity and regional stability. Specific indicators include the average slope of the bed, friction angle, undrained shear strength, and annual scouring and silting amplitude of the bed. Marine environmental conditions include meteorological factors, marine hydrology, seawater and seabed corrosion environment. Specific indicators include wind speed, waves, ocean currents and sea ice, etc. The status of marine facilities and development activities includes obstacles or hazards in the pipeline routing area, existing and planned submarine pipelines, cable conditions, fisheries and port channels.

4. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 1 is characterized in that: The step S500 specifically includes: Step S510, stratifying the evaluation indicators to determine the parent item of each evaluation parameter in the evaluation indicators; Step S520, compare the importance of each indicator, and construct a binary judgment matrix P according to the fuzzy mood operator and the fuzzy scale; Step S530, perform consistency judgment on the binary judgment matrix P according to the consistency index CR. If the consistency ratio is less than the preset threshold, the consistency index and weight vector W sorted from small to large in importance are obtained through inspection; wherein, the consistency index λ max is the maximum characteristic root of the judgment matrix, RI is the correction coefficient; If the consistency ratio is greater than the preset threshold, the test fails and the process goes to step S540; Step S540, repeating step S520, re-comparing until passing the test; Step S550: construct a weight matrix W=ω based on the consistency indexes and weight vector W sorted in ascending order of importance. i (ω1,ω2,…ω m ).

5. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 1 is characterized in that: The step S800 specifically includes: Step S810, performing route exploration on the grid obstacle map through a path-finding algorithm to obtain an initial route; Step S820, smoothing the initial route using a path optimization algorithm to obtain an optimized route; Step S830, completing curvature correction by adding arc obstacles to the turning points in the optimized path to obtain a corrected route.

6. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 5 is characterized in that: The smoothing process specifically includes: Reduce the inflection points on the initial route and shorten the overall route length.

7. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 5 is characterized in that: The step S810 specifically includes: Step S811, randomly select two points on the initial route to generate a straight line, and the points on the straight line are all pixel points on the grid obstacle map; Step S812, establish coordinates for the grid obstacle map, and the straight line formula between the starting point (x0, y0) and the end point (x1, y1) is: According to the Bresenham algorithm, when x moves on the grid obstacle map, the y value is calculated according to the formula and moved to the nearest node to obtain the grid obstacle map coordinates and determine whether it crosses the obstacle; if it does not cross the obstacle, the new straight line can be used to replace the original multi-inflection point line segment.

8. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 5 is characterized in that: The step S830 specifically includes: Step S831, adding an arc obstacle at a position where the curvature radius of the path turning point in the optimized path does not exceed the minimum allowable curvature radius, replacing the path turning point; Step S832, obtaining the arc curvature at the additional arc obstacle by using a curvature calculation formula; Step S833, compare the distance difference between two adjacent arc obstacles. If the two arc routes meet the requirement of being connected, complete the curvature correction of the inflection point and enter step S834; if the two arc routes do not meet the requirement of being connected, return to step S820 to start re-obtaining the optimized route; Step S834, repeating steps S831 to S833 until all inflection points are traversed to obtain a corrected route.

9. The pipeline routing method based on path planning algorithm and hierarchical analysis according to claim 8 is characterized in that: The step S833 specifically includes: The distance between the tangent point of the arc to the line connecting two adjacent inflection points and the inflection point is Where R is the minimum radius of curvature, and θ is the angle formed by the two pipeline routes at the inflection point; The length of the line segment between two adjacent inflection points is L. The distances between the two end points and the tangent point of the line segment are D1 and D2 respectively. The distance difference ε is ε=D1+D2-L; if ε<<0, it means that there is a certain distance between the two arcs, which meets the requirement that the two arc routes are connected; if ε>0, it means that there is an intersection between the two arcs, which does not meet the requirement that the two arc routes are connected, and the route needs to be redesigned.

10. The pipeline routing system based on path planning algorithm and hierarchical analysis is characterized by: The system comprises: A grid obstacle map acquisition module is configured to collect a topographic map of the target sea area and measurement data of evaluation indicators at various locations in the target sea area; the evaluation indicators are pre-defined m evaluation parameters required for the target sea area; An evaluation sample matrix X is established based on the measurement value data of the evaluation index, and the pixel points of the target sea area topographic map are converted into nodes of a two-dimensional matrix to obtain a two-dimensional matrix, and the two-dimensional matrix constitutes a grid obstacle map; An evaluation index matrix acquisition module is configured to establish an evaluation index matrix S according to pre-defined evaluation indexes and grade classification standards; An evaluation standard matrix construction module is configured to obtain the risk level of each location by comparing and normalizing the evaluation sample matrix X with the evaluation index matrix S, and construct an evaluation standard matrix B; A weight matrix construction module, configured to construct a weight matrix W based on the evaluation index by using a hierarchical analysis method; The risk quantitative assessment module is configured to assign a weight matrix W to the evaluation standard matrix B, obtain a weighted average value, and establish a submarine pipeline routing risk quantitative assessment model; A risk level grayscale image conversion module is configured to convert the sea area topographic map into a risk level grayscale image through a submarine pipeline routing risk quantitative assessment model, wherein the grayscale value of the image pixel point in the risk level grayscale image is proportional to the risk level H; A corrected route acquisition module is configured to perform route planning based on a grid obstacle map through a path planning algorithm and a correction algorithm to obtain a corrected route; The final route selection module is configured to use a greedy algorithm to find the path with the least cumulative grayscale value in the corrected route for the grayscale map of the risk level of the target sea area as the final route.

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