Snow cleaning route optimization method for rapidly recovering traffic in region

Abstract

The invention relates to a snow removal route optimization method for rapidly recovering traffic in a region, and the method comprises the following steps: S1, carrying out the rasterization of a selected region map, and marking an urban main road; s2, two pairs of mark points are arranged on two longitudinal boundary main road roads of the digital grid map and recorded as A1 and A2 and B1 and B2, and A1-B2 and A2-B1 are set as the roads which are traversed in the most preferential mode. S3, path optimization is carried out between the two pairs of mark points with A1 and A2 as starting points and B1 and B2 as terminal points, and two shortest paths (A1-B2 and A2-B1) are obtained. S4, an intersection point E of the two routes is reserved according to rules, and the digital map is divided into different areas A1EA2, A1EB1, B1EB2 and A2EB2. And recovering all roads to be 0 and obstacles to be 1 in the rasterized map matrix in each region, and obtaining traversal schemes in different regions by using a traversal algorithm. And S5, obtaining the cleaning sequence arrangement of all specific routes and the cleaning cost calculation of the optimal route.

Description

Technical Field

[0001] The present invention belongs to the field of path planning and logistics optimization, and specifically relates to a snow-clearing route optimization method based on rapid restoration of traffic in a region. The present invention can provide relevant urban departments with a sanitation work plan for snowy weather, improve cleaning efficiency, and achieve the purpose of rapid restoration of traffic and saving public resources. Background Art

[0002] Road cleaning is one of the core tasks of urban sanitation work. Snowfall in winter will cause snow and ice on the roads, which will increase the risk of traffic accidents and bring inconvenience to citizens' travel. In case of extreme weather, heavy snow or continuous snowfall, it will affect the daily life of citizens and the basic operation of the city. Therefore, municipal road cleaning in winter snowy weather has become the top priority of winter sanitation work. The current snow removal operation mainly relies on traditional experience guidance, and has not formed a scientific and effective systematic method. In order to further improve the efficiency of snow removal and quickly restore traffic order in the urban area, a set of scientific and systematic methods is urgently needed to guide the work. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] In view of this, in order to achieve the above-mentioned purpose, the purpose of the present invention is to provide a snow-clearing route optimization method for quickly restoring traffic in a region, which solves the problems raised in the background technology.

[0005] (II) Technical solution

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The method for optimizing snow removal routes for quickly restoring traffic in a region includes the following steps:

[0008] Step 1: Rasterize the selected area map to obtain a digital raster map. n×m According to the actual situation of the road, the main roads in the city are marked. The elements on the main roads are marked as 0, and the elements on the auxiliary roads and obstacles are marked as 1.

[0009] Step 2: Mark two pairs of different points from north to south on the two longitudinal boundary trunk roads of the digital grid map, and record them as A1, A2 and B1, B2 respectively.

[0010] Step 3: In the two pairs of marked points, set A1 and A2 as the starting points; B1 and B2 as the end points, and use the optimization algorithm to perform path optimization respectively to obtain two shortest paths (A1→B2 and A2→B1).

[0011] Step 4: After the above two routes intersect, the digital map is divided into different areas. Let the intersection be E, that is, A1EA2, A1EB1, B1EB2, A2EB2. When there are multiple intersections, the final intersection is selected according to the specific values ​​of i and j. Set A1→B2 and A2→B1 as the roads with the highest priority for traversal, and then restore all roads to 0 and obstacles to 1 in the grid in each area. Use the traversal algorithm to obtain the traversal plan in different areas.

[0012] Step 5: Obtain the cleaning sequence arrangement of all specific routes and the cleaning cost estimation of the optimal path.

[0013] Preferably, in step 1, digital rasterization is performed in the actual road map. The correspondence between the width of the grid in the raster map and the actual road is as follows: the unit grid is a square with the same length and width; the side length of the grid is the width of the trunk road. According to the grid division result, the number of rows and columns obtained by division are respectively set as the map matrix D n×m The row values ​​(n) and column values ​​(m) of the map matrix D n×m Each element in is d ij ,Through field investigation and according to traffic conditions and traffic requirements, the main roads, secondary roads and obstacles are marked separately.

[0014]

[0015] Preferably, in step 4, the two shortest routes obtained in step 3 can be used to divide the digital map into multiple different areas through the intersection of the two paths. The rule for selecting the intersection E is: if the two paths have only one intersection, then this intersection is the final intersection. If there are p intersections (p>1), E c (x c ,y c ),c=1,2,...,p,x c Represents the row number corresponding to the intersection point; y c Represents the number of columns corresponding to the intersection point. The final intersection point is selected according to the following rules:

[0016] If the intersection points have the same horizontal or vertical coordinates, the intersection point with the smaller coordinate value is preferred, that is, x c If they are equal, select y c Smaller; c When they are equal, choose x c Smaller.

[0017] If the intersection point does not have the same horizontal or vertical coordinates, calculate The intersection point corresponding to the minimum value, that is, the point closest to the center of the map, is selected as the final intersection point.

[0018] The above two criteria can be cycled to select the only intersection point E. If there are disconnected paths in the sub-blocks divided by point E, the disconnected sub-areas are traversed separately.

[0019] (III) Beneficial effects

[0020] The present invention provides a method for optimizing snow-clearing routes based on rapid restoration of traffic in a region. It can provide solid theoretical support for the intelligentization of urban cleaning routes under special weather conditions and has broad application prospects and scalability. The optimized routes and plans can improve the efficiency of sanitation work, help to quickly restore traffic, reduce economic costs, and achieve efficient use of human and material resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 The map matrix after marking obstacles;

[0023] Figure 2 The count matrix F corresponding to path 1 14×12 Schematic diagram of initialization results;

[0024] Figure 3 The corresponding F is the end of the traversal of path 1 14×12 result;

[0025] Figure 4 The count matrix F corresponding to path 2 14×12 Schematic diagram of initialization results;

[0026] Figure 5 The corresponding F is the end of the traversal of path 2 14×12 result;

[0027] Figure 6 Shown for two shortest paths;

[0028] Figure 7 The final traversal result. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] 1. In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0031] See also Figure 1-7 , a snow removal route optimization method for quickly restoring traffic in an area includes the following steps:

[0032] Step 1: Rasterize the selected area map to obtain a digital raster map. n×m According to the actual situation of the road, the main roads in the city are marked. The elements on the main roads are marked as 0, and the elements on the auxiliary roads and obstacles are marked as 1.

[0033] Step 2: Mark two pairs of different points from north to south on the two longitudinal boundary trunk roads of the digital grid map, and record them as A1, A2 and B1, B2 respectively.

[0034] Step 3: In the two pairs of marked points, set A1 and A2 as the starting points; B1 and B2 as the end points, and use the optimization algorithm to perform path optimization respectively to obtain two shortest paths (A1→B2 and A2→B1).

[0035] Step 4: After the above two routes intersect, the digital map is divided into different areas. Let the intersection be E, that is, A1EA2, A1EB1, B1EB2, A2EB2. When there are multiple intersections, the final intersection is selected according to the specific values ​​of i and j. Set A1→B2 and A2→B1 as the roads with the highest priority for traversal, and then restore all roads to 0 and obstacles to 1 in the grid in each area. Use the traversal algorithm to obtain the traversal plan in different areas.

[0036] Step 5: Obtain the cleaning sequence arrangement of all specific routes and the cleaning cost estimation of the optimal path.

[0037] 2. Preferably, in step 1, digital rasterization is performed in the actual road map. The correspondence between the width of the grid in the raster map and the actual road is as follows: the unit grid is a square with the same length and width; the side length of the grid is the width of the trunk road. According to the grid division result, the number of rows and columns obtained by division are respectively set as the map matrix D n×mThe row values ​​(n) and column values ​​(m) of the map matrix D n×m Each element in is d ij ,Through field investigation and according to traffic conditions and traffic requirements, the main roads, secondary roads and obstacles are marked separately.

[0038]

[0039] 3. Preferably, in step 4, the two shortest routes obtained in step 3 can be used to divide the digital map into multiple different areas through the intersection of the two paths. The rule for selecting the intersection E is: if the two paths have only one intersection, then this intersection is the final intersection. If there are p intersections (p>1), E c (x c ,y c ),c=1,2,...,p,x c Represents the row number corresponding to the intersection point; y c Represents the number of columns corresponding to the intersection point. The final intersection point is selected according to the following rules:

[0040] If the intersection points have the same horizontal or vertical coordinates, the intersection point with the smaller coordinate value is preferred, that is, x c If they are equal, select y c Smaller; c When they are equal, choose x c Smaller.

[0041] If the intersection point does not have the same horizontal or vertical coordinates, calculate The intersection point corresponding to the minimum value, that is, the point closest to the center of the map, is selected as the final intersection point.

[0042] The above two criteria can be cycled to select the only intersection point E. If there are disconnected paths in the sub-blocks divided by point E, the disconnected sub-areas are traversed separately.

[0043] Implementation examples:

[0044] Taking the snow removal operation in a certain area of ​​the city as an example, the specific implementation steps are as follows:

[0045] Step 1: Rasterize the selected area map to obtain a digital raster map. In the raster map, mark the main roads in the city according to the actual road conditions, with the main roads marked as 0 and other roads and obstacles marked as 1.

[0046] Map matrix row value: n = 14;

[0047] Map matrix column value: m = 12;

[0048] Map Matrix:D 14×12; Initialize to all zero array D = zeros(n,m);

[0049] Obstacle position array: zhangai_point = [14263850628698110122134146162840521001121241606183066789012613815032445668 80929410611813014215416616715514313111910795715947352311];

[0050] Traverse the obstacle positions, calculate the coordinates of the obstacles and set the corresponding values ​​of the map matrix to 1, indicating that there are obstacles or non-cleaned areas at this location. 14×12 See Figure 1 .

[0051] Step 2: Mark two different points on the two longitudinal boundary trunk roads of the digital grid map, divided into two pairs, recorded as A1, A2 and B1, B2.

[0052] The starting point 1 of the shortest path: A1=1;

[0053] Destination point 1 of the shortest path: B2 = n × m;

[0054] The starting point 2 of the shortest path: A2 = (n-1)*m+1;

[0055] Destination point 2 of the shortest path: B1 = m;

[0056] Initialize the count matrix F according to different starting points 14×12 , the obtained F 14×12 See Figure 2 , Figure 4 .

[0057] Step 3: Between two pairs of marked points, take A1 as the starting point and B2 as the end point; take A2 as the starting point and B1 as the end point, use the optimization algorithm to perform path optimization respectively, and you can get two shortest paths (A1→B2 and A2→B1).

[0058] Path A1→B2:

[0059] Call the PSO_inter function to get the reference point calculated by the PSO algorithm, calculate the legal points of the current point in four directions, compare the angle between the current point and the reference point obtained by PSO, select the point with the smallest angle to add to the path, and update the corresponding b ij , until the added point is B2; compare the length of each iterative path and select the shortest path. 14×12 like Figure 3 .

[0060] Path A2→B1:

[0061] Call the PSO_inter function to get the reference point calculated by the PSO algorithm, calculate the legal points of the current point in four directions, compare the angle between the current point and the reference point obtained by PSO, select the point with the smallest angle to add to the path, and update the corresponding b ij , until the added point is B1; compare the length of each iterative path and select the shortest path. 14×12 like Figure 5 .

[0062] Step 4: After the above two routes intersect, the digital map is divided into different areas. Let the intersection be E, that is, A1EA2, A1EB1, B1EB2, A2EB2. Set A1→B2 and A2→B1 as the roads with the highest priority for traversal, and then restore all roads to 0 and obstacles to 1 in the grid in each area. Use the traversal algorithm to obtain the traversal plan in different areas.

[0063] According to the path obtained in step 3, the intersection points are: 103, 81, 82, where 81 is in the 7th row and 9th column; 82 is in the 7th row and 10th column; 103 is in the 9th row and 7th column. It can be seen that the two routes partially overlap, there are multiple intersection points and they have the same horizontal or vertical coordinates; 81 and 82 are in the same row, so the point 81 with a smaller column number is selected; 81 and 103 are in different rows and columns, so the point 103 closer to the center of the map is selected as the final intersection point, dividing the map into three major areas, one of which is divided into three small areas by obstacles, for a total of five areas:

[0064] za1 = [1325374961];

[0065] za2 = [15273951];

[0066] za3 =

[5365] ;

[0067] za4=[43311978209213345571022344658];

[0068] za5=[15888878899111123135147159136148113:117125137149161:164127:129139:141151:153158165];

[0069] Reset the map matrix for each area, use the particle swarm optimization algorithm (PSO) to optimize the path, call the PSO_inter function to get the reference point calculated by the PSO algorithm, calculate the legal points of the current point in four directions, compare the angle between the current point and the reference point obtained by PSO, select the point with the smallest angle to add to the path, and update the corresponding b ij , until all b ij If all are greater than or equal to 1 and less than or equal to 210, the traversal ends and the shortest path is obtained. The length of each iterative path is compared, the shortest path is selected and the path is compressed to obtain the final result.

[0070] Step 5: Get the cleaning order of all specific routes and the cleaning cost estimate of the optimal path. Mark the map coordinates and set the drawing boundaries and grid lines, dynamically draw the path, and finally output the path length. The final result is as follows Figure 7 . Assume that the cleaning cost per unit is k, then the cleaning cost is k*final path length.

[0071] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary examples, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0072] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for optimizing snow removal routes for rapid restoration of traffic in a region, characterized in that: The following steps are involved: Step 1: Rasterize the selected area map to obtain a digital raster map. n×m According to the actual situation of the road, the main roads in the city are marked. The elements on the main roads are marked as 0, and the elements on the auxiliary roads and obstacles are marked as 1. Step 2: Mark two pairs of different points from north to south on the two longitudinal boundary trunk roads of the digital grid map, and record them as A1, A2 and B1, B2. Step 3: In the two pairs of marked points, set A1 and A2 as the starting points; B1 and B2 as the end points, and use the optimization algorithm to perform path optimization respectively to obtain two shortest paths (A1→B2 and A2→B1). Step 4: After the above two routes intersect, the digital map is divided into different areas. Let the intersection be E, that is, A1EA2, A1EB1, B1EB2, A2EB2. When there are multiple intersections, the final intersection is selected according to the specific values ​​of i and j. Set A1→B2 and A2→B1 as the roads with the highest priority for traversal, and then restore all roads to 0 and obstacles to 1 in the grid in each area. Use the traversal algorithm to obtain the traversal plan in different areas. Step 5: Obtain the cleaning sequence arrangement of all specific routes and the cleaning cost estimation of the optimal path.

2. A snow removal route optimization method for quickly restoring traffic in a region, characterized by: In the step 1, digital rasterization is performed in the actual road map. The correspondence between the width of the grid in the raster map and the actual road is as follows: the unit grid is a square with the same length and width; the side length of the grid is the width of the trunk road. According to the grid generation results, the number of rows and columns obtained by the generation are set as the map matrix D n×m The row values ​​(n) and column values ​​(m) of the map matrix D n×m Each element in is d ij ,Through field investigation and according to traffic conditions and traffic requirements, the main roads, secondary roads and obstacles are marked separately.

3. A snow removal route optimization method for quickly restoring traffic in a region, characterized by: In step 4, the two shortest routes obtained in step 3 can be used to divide the digital map into multiple different areas through the intersection of the two paths. The rule for selecting the intersection E is: if the two paths have only one intersection, then this intersection is the final intersection. If there are p intersections (p>1), E c (x c ,y c ),c=1,2,...,p,x c Represents the row number corresponding to the intersection point; y c Represents the number of columns corresponding to the intersection point. The final intersection point is selected according to the following rules: If the intersection points have the same horizontal or vertical coordinates, the intersection point with the smaller coordinate value is preferred, that is, x c If they are equal, select y c Smaller; c When they are equal, choose x c Smaller. If the intersection point does not have the same horizontal or vertical coordinates, calculate The intersection point corresponding to the minimum value, that is, the point closest to the center of the map, is selected as the final intersection point. The above two criteria can be cycled to select the only intersection point E. If there are disconnected paths in the sub-blocks divided by point E, the disconnected sub-areas are traversed separately.

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