CN114722546A - Planning method, device and equipment for water supply pipe network layout path and storage medium - Google Patents

Planning method, device and equipment for water supply pipe network layout path and storage medium Download PDF

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CN114722546A
CN114722546A CN202210281202.2A CN202210281202A CN114722546A CN 114722546 A CN114722546 A CN 114722546A CN 202210281202 A CN202210281202 A CN 202210281202A CN 114722546 A CN114722546 A CN 114722546A
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path
water supply
node
planning
initial
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林凡
彭梓鑫
郭鑫
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GCI Science and Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/14—Pipes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Abstract

The invention discloses a planning method, a device, equipment and a storage medium for a water supply pipe network layout path, wherein the method comprises the following steps: rasterizing a layout area of the water supply network to obtain a grid map of the layout area; planning a path based on the number of obstacles around the selectable nodes in the grid map to obtain an initial water supply path; performing path optimization on the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; and evaluating the turning point of the primary optimized water supply path according to a preset water supply evaluation function, and searching paths between an upper turning point and a lower turning point of the turning point which does not meet the requirement again by adopting a first path planning algorithm and a second path planning algorithm to obtain a final water supply network distribution path. The method can reduce the blindness of path search and reasonably consider the terrain of the pipeline construction region so as to improve the reasonability of the water supply network layout path.

Description

Planning method, device and equipment for water supply pipe network layout path and storage medium
Technical Field
The invention relates to the technical field of urban water supply scheduling, in particular to a method and a device for planning a water supply network layout path, terminal equipment and a computer readable storage medium.
Background
The water supply pipeline system usually takes water from a water source, sends the water to a water plant through a main pipeline, is purified by the water plant and then is sent to water using areas through a main pipeline, the water supply pipeline system of the area is laid by each water using area according to the needs and conditions of the water using area, and the water is sent to each water using point or is sent to each water using point of each household through a branch pipeline by the water supply pipeline system. However, in the conventional water supply network arrangement, the path search of barriers such as cement walls and the like is blindness, and the terrain in the pipeline construction field cannot be reasonably considered, so that the arrangement path of the water supply network is unreasonable.
Disclosure of Invention
The embodiment of the invention provides a method and a device for planning water supply network layout paths, terminal equipment and a computer readable storage medium, which can reduce the blindness of path search and reasonably consider the pipeline construction regional terrain so as to improve the rationality of the water supply network layout paths.
The embodiment of the invention provides a method for planning a water supply pipe network layout path, which comprises the following steps:
rasterizing a layout area of a water supply network to obtain a grid map of the layout area;
planning a path based on the number of obstacles around the optional node in the grid map to obtain an initial water supply path;
performing path optimization on the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; wherein the first path planning algorithm is an adaptive mutation ant colony algorithm;
evaluating the turning points of the primary optimized water supply path according to a preset water supply evaluation function, and when the evaluation score of any turning point is less than or equal to a first threshold, searching the path between the last turning point and the next turning point of the turning point again by adopting the first path planning algorithm and the second path planning algorithm until the evaluation scores of all the turning points are greater than the first threshold, so as to obtain the final water supply network distribution path; the second path planning algorithm is obtained by introducing barrier influence factors and target influence factors to improve the heuristic function of the ant colony algorithm.
As an improvement of the above scheme, the planning a path based on the number of obstacles around the selectable node in the grid map to obtain an initial water supply path includes:
determining a starting point and an end point of the water supply network in the grid map;
starting from the starting point, sequentially judging whether the number of obstacles around the current node is greater than a second threshold value, if so, selecting the next node by adopting the second path planning algorithm; if not, selecting the next node according to the following formula to obtain an initial water supply path from the starting point to the end point:
Figure BDA0003557868530000021
wherein j is the current node, j' is the next node, NxAnd in the total row number of the grid map, amuunt is the number of selectable nodes in 8 nodes adjacent to the current node.
As an improvement of the above scheme, the performing path optimization on the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path includes:
step S31, calculating a path length of the initial water supply path;
step S32, determining a variation path length according to the path length of the initial water supply path;
step S33, randomly selecting two nodes from the initial water supply path as variation points according to the variation path length;
step S34, searching a path between the two variation points according to the first path planning algorithm to obtain a variation path;
step S35, comparing an initial path of the two change points in the initial water supply path with the variation path, and updating the initial path by using the variation path when the variation path is better than the initial path;
iteratively executing the steps S31 to S35 for M times, and taking the initial path obtained by updating the last iteration as a primary optimized water supply path; wherein M is a positive integer.
As an improvement of the above solution, the determining the length of the varied path according to the path length of the initial water supply path specifically includes:
rounding the 1/n path length of the initial water supply path to obtain a variable path length; wherein n is a positive integer.
As an improvement of the above solution, the searching a path between two of the variation points according to the first path planning algorithm to obtain a variation path includes:
taking one of the variation points as a source node and the other variation point as a target node, and sequentially selecting the next node from the source node according to the following formula until obtaining a variation path from the source node to the target node:
Figure BDA0003557868530000031
where s is the next node, (i, j) is the path from node i to node j, τi,j(t) intensity of pheromone on path (i, j) at time t, vi,j(t) Water flow velocity, delay, of path (i, j)i,j(t) the water flow transit time of path (i, j), (i, u) the path from node i to node u, τi,u(t) intensity of pheromone on path (i, u) at time t, vi,u(t) Water flow velocity, delay, of path (i, u)i,u(t) is the water flow transit time of the path (i, u), α is the weight of pheromone intensity, β is the weight of water flow transit speed, and γ is the weight of water flow transit time delayNodeSum (1, mu) is the number of times node mu is passed, allowkAnd rand is a preset parameter for the node set which can be selected by the current ant.
As an improvement of the above scheme, the heuristic function of the second path planning algorithm specifically includes:
Figure BDA0003557868530000032
Figure BDA0003557868530000033
wherein etai,jIs heuristic information of the path (i, j), di,GAs a target influence factor, di,GFor indicating the distance, Σ, from node i to end point Gobs∈Pdi,obsIs an obstacle influence factor, sigmaobs∈Pdi,obsFor representing the sum of distances, k, of all obstacles to the node i within the obstacle influence range P1Is the weight of the target impact factor, k2As weights for obstacle influencing factors, allowkSet of nodes selectable for a current ant, NxFor the total number of lines of the grid map, NyFor the total number of columns of the grid map
As an improvement of the above scheme, the preset water supply evaluation function G (v, ω) specifically includes:
Figure BDA0003557868530000041
wherein v is the linear velocity of water flow transmission, ω is the angular velocity of water flow transmission, angle (v, ω) is the direction angle evaluation function, angle (v, ω) is used to represent the deviation of the direction angle between two turning points, vel (v, ω) is the velocity magnitude evaluation function, ξ is the weighting coefficient of the direction angle evaluation function,
Figure BDA0003557868530000042
the weighting coefficient of the velocity magnitude evaluation function is denoted by σ, which is a smoothing coefficient.
Accordingly, another embodiment of the present invention provides a planning device for water supply pipe network layout path, including:
the rasterization module is used for rasterizing the layout area of the water supply network to obtain a grid map of the layout area;
the initial path planning module is used for planning paths based on the number of obstacles around the selectable nodes in the grid map to obtain an initial water supply path;
the primary path optimization module is used for optimizing the path of the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; wherein the first path planning algorithm is an adaptive mutation ant colony algorithm;
the secondary path optimization module is used for evaluating turning points of the primary optimized water supply path according to a preset water supply evaluation function, and when the evaluation score of any turning point is smaller than or equal to a first threshold value, the path between the last turning point and the next turning point of the turning point is searched again by adopting the first path planning algorithm and the second path planning algorithm until the evaluation scores of all the turning points are larger than the first threshold value, so that a final water supply network layout path is obtained; the second path planning algorithm is obtained by introducing barrier influence factors and target influence factors to improve the heuristic function of the ant colony algorithm.
Another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the processor implements the method for planning a water supply network layout path as described in any one of the above items.
Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, where when the computer program runs, the apparatus on which the computer-readable storage medium is located is controlled to execute the method for planning a water supply network layout path as described in any one of the above items.
Compared with the prior art, the planning method, the planning device, the planning equipment and the computer-readable storage medium for the water supply network layout path disclosed by the embodiment of the invention have the advantages that on one hand, the path search is enabled to jump out of the local optimization by utilizing the self-adaptive mutation ant colony algorithm, the possibility that the algorithm is trapped in the local optimization is reduced, and the optimization capability and the convergence speed of the algorithm are improved; on the other hand, the turning points of the constructed water supply paths are evaluated by using the preset water supply evaluation function, the blindness of path search is reduced, the terrain of the pipeline construction region can be reasonably considered, and the water supply paths are adjusted by the ant colony algorithm and the self-adaptive variation ant colony algorithm obtained by improving the heuristic function based on the target influence factor and the barrier influence factor, so that the global search capability of path planning is enhanced, and the reasonability of the water supply network layout paths is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for planning a water supply pipe network layout path according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a grid map provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of another grid map provided by embodiments of the present invention;
FIG. 4 is a block diagram of a water supply network layout path planning apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart diagram of a method for planning a water supply pipe network layout path according to an embodiment of the present invention.
The method for planning the water supply pipe network layout path provided by the embodiment of the invention comprises the following steps:
step S11, rasterizing a layout area of the water supply network to obtain a grid map of the layout area;
step S12, planning a path based on the number of obstacles around the selectable nodes in the grid map to obtain an initial water supply path;
s13, performing path optimization on the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; wherein the first path planning algorithm is an adaptive mutation ant colony algorithm;
step S14, evaluating the turning points of the primary optimized water supply path according to a preset water supply evaluation function, and when the evaluation score of any turning point is less than or equal to a first threshold, searching the path between the last turning point and the next turning point of the turning point again by adopting the first path planning algorithm and the second path planning algorithm until the evaluation scores of all the turning points are greater than the first threshold, so as to obtain the final water supply network layout path; the second path planning algorithm is obtained by introducing barrier influence factors and target influence factors to improve the heuristic function of the ant colony algorithm.
It should be noted that, in the process of laying the water supply pipe network, the pipeline is often easily blocked by static barriers such as cement walls, and the actual conditions of the landform and the building in the construction region lack global search capability, so that the laying process is blindness, and the economic and reasonable requirements are difficult to achieve. In order to improve the service efficiency of the urban water supply network project, the pipe network project is optimized by strengthening, so that the construction cost of the urban water supply network project is reduced under the condition of ensuring normal water supply. The optimization principle of the water supply network layout path mainly comprises the following steps: 1. the safety principle is that the distributed pipe network cannot collide with the barrier; 2. the optimality principle is that the path laid by the pipe network is shortest; 3. the time principle, i.e. the time to find the optimal path, is to be as short as possible.
It is worth explaining that the grid graph method is a simple and effective modeling method, has strong adaptability to obstacles and is beneficial to reducing the complexity of the environment; the cartesian coordinate system is an intuitive modeling method that can clearly represent each point in the environment. As shown in FIG. 2, the method combines the advantages of two modeling methods, divides the arrangement area of the water supply network from the upper left corner to the lower right corner, from left to right, from top to bottom, and combines a grid map method and a Cartesian coordinate system method to form a grid map of the arrangement area of the water supply network, wherein the black area represents an obstacle, the grid where the obstacle is located cannot be laid, and the white area is a laying area, so that the grid map is beneficial to path planning and can assist in improving the algorithm.
Specifically, the coordinates (x) of the ith grid of the grid mapi,yi) The following relation is satisfied:
Figure BDA0003557868530000071
wherein x isiIs the abscissa, y, of the ith gridiIs the ordinate of the ith grid, a is the length of each grid, NxFor the total number of lines of the grid map, NyFor the total number of columns in the grid map, mod is the remainder function, ceil (i/N)y) Is greater than or equal to i/NyIs the smallest integer function of (1).
Preferably, the node of the pipe lay is located at the centre point of the grid.
Specifically, the grid coordinates are represented by the center point of the grid.
For example, in step S12, the initial water supply path may be obtained by performing path planning using a path planning algorithm such as a D-algorithm, a fast search random number (RRT), or a simulated evolution algorithm.
As an optional embodiment, the planning a path based on the number of obstacles around the optional node in the grid map to obtain an initial water supply path includes:
determining a starting point and an end point of the water supply network in the grid map;
starting from the starting point, sequentially judging whether the number of obstacles around the current node is greater than a second threshold value, if so, selecting the next node by adopting the second path planning algorithm; if not, selecting the next node according to the following formula to obtain an initial water supply path from the starting point to the end point:
Figure BDA0003557868530000072
wherein j is the current node, j' is the next node, NxAnd for the total row number of the grid map, amount is the number of selectable nodes in 8 nodes adjacent to the current node.
In the present invention, the blank grid can be divided into two cases: one is a global blank grid, i.e. a blank grid without obstacles; the second is a partially blank grid, i.e., a portion of the grid map has no obstacles. A taboo table and an optional node set are generated in the process of pipeline layout, the barrier and the previous node (selected node) are placed in the taboo table, and the blank node is used as the optional node. As shown in fig. 3, the above formula
Figure BDA0003557868530000081
It can be understood that: the number of nodes adjacent to the current node of the pipeline is 8, if the number of the selectable nodes is 7, the current area is regarded as a blank area, and the position of the next node is directly determined according to the direction of the target; if the number of the optional nodes is not 7, determining the next node by using a pseudo-random strategy and roulette, thereby improving the searching efficiency. According to the invention, for the simple terrain with the number of obstacles around the current node smaller than the second threshold, a simpler algorithm is adopted to select the next node, so that the path searching time can be saved, and the path searching efficiency can be effectively improved.
In some preferred embodiments, the performing, based on a first path planning algorithm, path optimization on the initial water supply path to obtain a primary optimized water supply path includes:
step S31, calculating a path length of the initial water supply path;
step S32, determining a variation path length according to the path length of the initial water supply path;
step S33, randomly selecting two nodes from the initial water supply path as variation points according to the variation path length;
step S34, searching a path between the two variation points according to the first path planning algorithm to obtain a variation path;
step S35, comparing an initial path of the two change points in the initial water supply path with the variation path, and updating the initial path by using the variation path when the variation path is better than the initial path;
iteratively executing the steps S31 to S35 for M times, and taking the initial path obtained by updating the last iteration as a primary optimized water supply path; wherein M is a positive integer.
It is worth to be noted that the basic idea of the adaptive mutation ant colony algorithm is as follows: the method comprises the steps of determining a variation length according to the length of an initial water supply path, randomly generating two variation points in the initial water supply path according to the variation length, executing a secondary algorithm between the two variation points, searching other feasible paths, and finally replacing an original path between the two variation points with a newly searched feasible path between the two variation points so as to generate a new water supply path, wherein a new solution generated in such a way is possible to enable the algorithm to jump out of a current local extreme value and accelerate the convergence speed. Therefore, by generating variation points and secondary search, the possibility that the algorithm is trapped in local optimization can be reduced, and the optimizing capability and the convergence speed of the algorithm are improved.
In some preferred embodiments, the determining a variant path length according to the path length of the initial water supply path includes:
rounding the 1/n path length of the initial water supply path to obtain a variable path length; wherein n is a positive integer.
In a specific implementation process, in the process of planning the path, one node is addedUsing a counter NodeSum (1, N), the initialization value of each node in the counter is 0, i.e., NodeSum (1, j) ═ 0,
Figure BDA0003557868530000091
every time a node passes through, the count value of the node is increased by 1, i.e., nodosum (1, j) ═ nodosum (1, j) + 1. And (3) when the pipeline selects the next node s from the current node i at the moment t, taking the node use counter into consideration, and selecting the node with the minimum node count value by pipeline laying personnel under a certain probability, so that the node which is not accessed has the possibility of being accessed and jumps out of a local extreme value to a certain extent.
In a specific embodiment, the searching a path between two of the variation points according to the first path planning algorithm to obtain a variation path includes:
taking one of the variation points as a source node and the other variation point as a target node, and sequentially selecting the next node from the source node according to the following formula until obtaining a variation path from the source node to the target node:
Figure BDA0003557868530000092
where s is the next node, (i, j) is the path from node i to node j, τi,j(t) intensity of pheromone on path (i, j) at time t, vi,j(t) Water flow velocity, delay, of path (i, j)i,j(t) the water flow transit time of path (i, j), (i, u) the path from node i to node u, τi,u(t) intensity of pheromone on path (i, u) at time t, vi,u(t) Water flow velocity, delay, of path (i, u)i,u(t) is the water flow transit time of the path (i, u), α is the weight of pheromone intensity, β is the weight of water flow transit speed, γ is the weight of water flow transit delay, NodeSum (1, μ) is the number of times a node μ passes, allowkAnd rand is a preset parameter for the node set which can be selected by the current ant.
Specifically, in step S35, when the path length of the mutated path is smaller than the path length of the original path, it is determined that the mutated path is better than the original path; or, when the water flow transmission time of the variant path is less than that of the original path, determining that the variant path is superior to the original path. It can be understood that the purpose of the path optimization is mainly to reduce the pipeline material, accelerate the transportation of the water supply, and the like, and if the optimized path (the variant path) is closer to the target point than the initial path, and can effectively avoid the obstacle, the pipeline material can be saved more by laying the water supply pipe network, and the water can be delivered more efficiently, so that the optimized path can replace the initial path, that is, whether the variant path is better than the initial path or not is judged, and the judgment needs to be performed according to the specific water supply pipe network laying requirements, and no specific limitation is made herein.
The following describes the adaptive mutation ant colony algorithm provided in this embodiment with a specific embodiment:
assuming that a certain qualified path searched for last time is path (1,3,11,6,20,10), and the path length is length (path) 6, the variant path length is defined as: l ═ round (length), (path)/3), i.e., the length of the mutated path is an integer close to one third of the total length, then L (path) ═ 2, i.e., the length of the mutated path is equal to 2. Assuming that the iteration number of the self-adaptive mutation ant colony algorithm is a constant M, two mutation points R are randomly generated according to L (path) each time1And R2So that R is2＝R1+ L, then for the variation point R1And R2And searching paths among the paths again to obtain a mutation path. And comparing the mutated path with the initial path, if the mutated path is superior to the initial path, updating and replacing the initial path by using the mutated path, otherwise, still adopting the initial path before mutation, and performing the next iteration until M iterations are performed.
Preferably, in step S34, when the path between two of the variation points is searched according to the first path planning algorithm, before the next node is selected by the first path planning algorithm, the pheromone of the current node is updated according to the following formula:
Figure BDA0003557868530000101
Figure BDA0003557868530000111
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003557868530000112
the pheromone concentration of the new path (i, j) at time t +1,
Figure BDA0003557868530000113
the pheromone concentration, Δ τ, of the original pathway (i, j)i,jThe number of unit length track pheromones left on the path (i, j) for the group of individuals, ω' is a preset parameter, 0<ω'<1，LbestFor the currently optimal laying length, PathbestFor the best path, Q is a fixed constant.
Specifically, in the laying process of the water supply pipeline, the group is all construction units optimized for the pipeline, the pheromone is data such as engineering drawings in the construction process, and the concentration of the pheromone is the proportion of the data which can be reused in the original data. It is understood that as the surrounding environment evolves, the effective utilization of the material gradually decreases.
Illustratively, taking a conventional ant colony algorithm as an example, the basic flow of the conventional ant colony algorithm is as follows:
1. the ant colony is initialized, and the number of individuals in the colony and the distribution of pheromones are determined.
2. And randomly obtaining the moving direction of the next node by a heuristic function, wherein the heuristic function is defined as follows:
Figure BDA0003557868530000114
Figure BDA0003557868530000115
wherein etai,jIs heuristic information of the path (i, j), di,jIs the Euclidean distance, allow, from node i to node jkSet of nodes, x, selectable for the current antiIs the abscissa, y, of node iiIs the ordinate, x, of node ijIs the abscissa, y, of node jjIs the ordinate of node j.
3. After all population individuals complete one iteration, the pheromone of each path is updated according to the following formula:
Figure BDA0003557868530000116
wherein the content of the first and second substances,
Figure BDA0003557868530000117
for the pheromone concentration of the new pathway (i, j),
Figure BDA0003557868530000118
the pheromone concentration of the original route (i, j), ρ is the volatilization coefficient of pheromone, m is the number of population individuals,
Figure BDA0003557868530000119
the number of single-bit-length track pheromones left on the path (i, j) for the kth group of individuals,
Figure BDA0003557868530000121
the calculation formula is as follows:
Figure BDA0003557868530000122
wherein Q is a constant of the number of tracks left by the group individual, LkThe length of a path taken by the kth group individual in the iteration is obtained;
assuming that the current location of the kth group individual is i, the next node j is selected by a pseudo-random strategy, and the formula is as follows:
Figure BDA0003557868530000123
Figure BDA0003557868530000124
wherein, taui,jIs the intensity of the pheromone of the path (i, j), τi,uIntensity of pheromone, η, of path (i, u)i,jIs heuristic information of the path (i, j) (. eta.)i,uHeuristic information for the path (i, u), α is the weight of the intensity of the pheromone,
Figure BDA0003557868530000125
q is a random number in the interval (0,1) for the significance factor of the heuristic function, q0In order to be a transition probability threshold value,
Figure BDA0003557868530000126
the probability of being selected for the node j, u being any node in the selectable nodes; it can be understood that if the random number generated by the system is less than or equal to the transition probability threshold, the node with the largest product of the pheromone concentration and the heuristic information in the optional nodes is selected as the next node, otherwise, the probability is used as the next node
Figure BDA0003557868530000127
Selection is performed.
It is worth to be noted that the heuristic function of the conventional ant colony algorithm only considers the distance between the current grid and the optional node, which results in the poor global search capability of the algorithm. In order to improve the global search capability of the algorithm, the distance between an optional node and a terminal point (target influence factor) is introduced into a heuristic function, so that the heuristic function gives consideration to local search and global search, and simultaneously, the peripheral obstacle information (obstacle influence factor) of the current node is added into the heuristic function, so that the heuristic function changes along with the difference of node environments.
Further, the heuristic function of the second path planning algorithm specifically includes:
Figure BDA0003557868530000131
Figure BDA0003557868530000132
wherein eta isi,jIs heuristic information of the path (i, j), di,GAs a target influence factor, di,GFor representing the distance, Σ, from node i to end point Gobs∈Pdi,obsIs an obstacle influence factor, ∑obs∈Pdi,obsFor representing the sum of distances, k, of all obstacles to the node i within the obstacle influence range P1Is the weight of the target impact factor, k2As weights for obstacle influencing factors, allowkSet of nodes selectable for a current ant, NxFor the total number of lines of the grid map, NyThe total number of columns of the grid map.
It can be understood that the target influence factor is the distance from the current node to the target point, which is the direction of pipeline laying determined globally, such as from the water source to the water plant, from the water plant to the water area; the obstacle influence factor represents the sum of the distances from the obstacle to the current node within the obstacle influence range, and is the direction of laying the pipeline from the local part, such as a water source to a certain part of the area in the process of a water plant, and the two factors respectively represent a global search and a local search. In the early stage of pipeline laying, the target influence factor is large, and the weight coefficient is relatively small, so that the global search capability and the local search capability are balanced; in the later stage of pipeline laying, the target influence factor is small, and the weight coefficient is relatively large, so that the capacity of global search and local search is balanced.
It is worth explaining that in the process of pipeline water transmission, due to the reasons that the water consumption area is more in flow, part of terrains are more complex and the like, the pressure of the pipeline water can be correspondingly changed, in order to ensure that the water flow speed is kept stable in the next water consumption area, speed sensors are arranged at nodes of the water consumption area, the complex terrains and the like, a speed monitoring system is formed, the speed of the water flow is monitored, and then the primary optimized water supply path is evaluated through a preset water supply evaluation function; the evaluation criterion of the preset water supply evaluation function is as follows: the selection of the next node by the path can help avoid obstacles as much as possible in the pipeline laying process, prevent the water flow speed from being limited, and continue to move towards the destination.
In a specific embodiment, the preset water supply evaluation function G (v, ω) is specifically:
Figure BDA0003557868530000133
wherein v is the linear velocity of water flow transmission, ω is the angular velocity of water flow transmission, angle (v, ω) is the direction angle evaluation function, angle (v, ω) is used to represent the deviation of the direction angle between two turning points, vel (v, ω) is the velocity magnitude evaluation function, ξ is the weighting coefficient of the direction angle evaluation function,
Figure BDA0003557868530000141
the weighting coefficient of the velocity magnitude evaluation function is denoted by σ, and the smoothing coefficient is denoted by σ.
Note that xi and
Figure BDA0003557868530000142
the value of (c) depends on the deviation of the initial point angle and the length of the initial point linear distance, the value of the two is 1 normally, but if the deviation angle is large, xi can be properly small, and if the linear distance is short, xi can be properly small
Figure BDA0003557868530000143
Can be properly large.
It can be understood that, because the transmission speed of the pipeline water cannot be attenuated too fast in the transmission process, the turning angle of the turning node cannot be too large.
Further, the direction angle evaluation function angle (v, ω) is specifically:
Figure BDA0003557868530000144
the speed magnitude evaluation function vel (v, ω) specifically includes:
Figure BDA0003557868530000145
where V is the linear velocity of water flow, ω is the angular velocity of water flow, Vi+1Is the propagation velocity, V, of the water flow at the i +1 th turning pointiThe propagation velocity of the ith turning point.
It should be noted that, in step S14, the first threshold may be set according to the actual water supply condition of the water supply pipeline, and the turning point with a score larger than the first threshold should meet the normal water supply requirement.
Fig. 4 is a block diagram of a planning apparatus for a water supply pipe network layout path according to an embodiment of the present invention.
The planning device for the water supply pipe network layout path provided by the embodiment of the invention comprises:
the rasterization module 21 is used for rasterizing the layout area of the water supply network to obtain a grid map of the layout area;
the initial path planning module 22 is configured to perform path planning based on the number of obstacles around the selectable node in the grid map to obtain an initial water supply path;
a primary path optimization module 23, configured to perform path optimization on the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; wherein the first path planning algorithm is an adaptive mutation ant colony algorithm;
the secondary path optimization module 24 is configured to evaluate turning points of the primarily optimized water supply path according to a preset water supply evaluation function, and when an evaluation score of any one turning point is smaller than or equal to a first threshold, re-search a path between a last turning point and a next turning point of the turning point by using the first path planning algorithm and the second path planning algorithm until the evaluation scores of all the turning points are greater than the first threshold, so as to obtain a final water supply network distribution path; the second path planning algorithm is obtained by introducing barrier influence factors and target influence factors to improve the heuristic function of the ant colony algorithm.
As an improvement of the above scheme, the initial path planning module 22 is specifically configured to:
determining a starting point and an end point of the water supply pipe network in the grid map;
starting from the starting point, sequentially judging whether the number of obstacles around the current node is greater than a second threshold value, if so, selecting the next node by adopting the second path planning algorithm; if not, selecting the next node according to the following formula to obtain an initial water supply path from the starting point to the end point:
Figure BDA0003557868530000151
wherein j is the current node, j' is the next node, NxAnd for the total row number of the grid map, amount is the number of selectable nodes in 8 nodes adjacent to the current node.
As an optional implementation manner, the primary path optimization module 23 is specifically configured to execute the following steps:
step S31, calculating a path length of the initial water supply path;
step S32, determining a variation path length according to the path length of the initial water supply path;
step S33, randomly selecting two nodes from the initial water supply path as variation points according to the variation path length;
step S34, searching a path between the two variation points according to the first path planning algorithm to obtain a variation path;
step S35, comparing an initial path of the two change points in the initial water supply path with the variation path, and updating the initial path by using the variation path when the variation path is better than the initial path;
iteratively executing the steps S31 to S35 for M times, and taking the initial path obtained by updating the last iteration as a primary optimized water supply path; wherein M is a positive integer.
Preferably, in the primary path optimization module 23, the determining a variant path length according to the path length of the initial water supply path specifically includes:
rounding the 1/n path length of the initial water supply path to obtain a variable path length; wherein n is a positive integer.
Further, in the primary path optimization module 23, the searching a path between two of the variation points according to the first path planning algorithm to obtain a variation path includes:
taking one of the variation points as a source node and the other variation point as a target node, and sequentially selecting the next node from the source node according to the following formula until obtaining a variation path from the source node to the target node:
Figure BDA0003557868530000161
where s is the next node, (i, j) is the path from node i to node j, τi,j(t) intensity of pheromone on path (i, j) at time t, vi,j(t) Water flow velocity, delay, of path (i, j)i,j(t) the water flow transit time of path (i, j), (i, u) the path from node i to node u, τi,u(t) intensity of pheromone on path (i, u) at time t, vi,u(t) Water flow velocity, delay, of path (i, u)i,u(t) is the water flow transmission time of the path (i, u), α is the weight of pheromone intensity, β is the weight of water flow transmission speed, γ is the weight of water flow transmission delay, NodeSum (1, μ) is the number of times the node μ passes, allowkAnd the rand is a preset parameter for the node set which can be selected by the current ant.
Preferably, the heuristic function of the second path planning algorithm specifically includes:
Figure BDA0003557868530000171
Figure BDA0003557868530000172
wherein eta isi,jIs heuristic information of the path (i, j), di,GAs a target influence factor, di,GFor indicating the distance, Σ, from node i to end point Gobs∈Pdi,obsIs an obstacle influence factor, sigmaobs∈Pdi,obsFor representing the sum of distances, k, of all obstacles to the node i within the obstacle influence range P1Is the weight of the target impact factor, k2As weights for obstacle influencing factors, allowkSet of nodes that can be selected for the current ant, NxFor the total number of lines of the grid map, NyThe total number of columns of the grid map.
Specifically, the preset water supply evaluation function G (v, ω) is specifically:
Figure BDA0003557868530000173
wherein v is the linear velocity of water flow transmission, ω is the angular velocity of water flow transmission, angle (v, ω) is the direction angle evaluation function, angle (v, ω) is used to represent the deviation of the direction angle between two turning points, vel (v, ω) is the velocity magnitude evaluation function, ξ is the weighting coefficient of the direction angle evaluation function,
Figure BDA0003557868530000174
the weighting coefficient of the velocity magnitude evaluation function is denoted by σ, which is a smoothing coefficient.
It should be noted that, for the specific description and the advantageous effects related to each embodiment of the planning device for water supply network layout path of this embodiment, reference may be made to the specific description and the advantageous effects related to each embodiment of the planning method for water supply network layout path described above, and details are not described here again.
Fig. 5 is a block diagram of a terminal device according to an embodiment of the present invention.
The terminal device provided by the embodiment of the invention comprises a processor 10, a memory 20 and a computer program stored in the memory 20 and configured to be executed by the processor 10, wherein the processor 10 executes the computer program to implement the method for planning the water supply network layout path according to any of the embodiments.
The processor 10, when executing the computer program, carries out the steps of the above-described embodiment of the method for planning a water supply network laying path, such as all the steps of the method for planning a water supply network laying path shown in fig. 1. Alternatively, the processor 10, when executing the computer program, performs the functions of the modules/units of the water supply network layout path planning apparatus embodiment described above, such as the modules of the water supply network layout path planning apparatus shown in fig. 4.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory 20 and executed by the processor 10 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor 10, a memory 20. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of a terminal device, and may include more or less components than those shown, or combine certain components, or different components, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 10 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 10 is the control center of the terminal device and connects the various parts of the whole terminal device by various interfaces and lines.
The memory 20 can be used for storing the computer programs and/or modules, and the processor 10 implements various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory 20 and calling data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement without inventive effort.
Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, where when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for planning water supply pipe network layout path according to any one of the above method embodiments.
In summary, according to the planning method, the planning device, the computer-readable storage medium and the terminal device for the water supply network layout path provided by the embodiment of the invention, on one hand, the path search is enabled to jump out of the local optimization by using the adaptive mutation ant colony algorithm, the possibility that the algorithm falls into the local optimization is reduced, and the optimization capability and the convergence speed of the algorithm are improved; on the other hand, the turning points of the constructed water supply paths are evaluated by using the preset water supply evaluation function, the blindness of path search is reduced, the terrain of the pipeline construction region can be reasonably considered, and the water supply paths are adjusted by the ant colony algorithm and the self-adaptive variation ant colony algorithm obtained by improving the heuristic function based on the target influence factor and the barrier influence factor, so that the global search capability of path planning is enhanced, and the reasonability of the water supply network layout paths is improved.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A method for planning a water supply network layout path is characterized by comprising the following steps:
rasterizing a layout area of a water supply network to obtain a grid map of the layout area;
planning a path based on the number of obstacles around the optional node in the grid map to obtain an initial water supply path;
performing path optimization on the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; wherein the first path planning algorithm is an adaptive mutation ant colony algorithm;
evaluating the turning points of the primary optimized water supply path according to a preset water supply evaluation function, and when the evaluation score of any turning point is less than or equal to a first threshold, searching the path between the last turning point and the next turning point of the turning point again by adopting the first path planning algorithm and the second path planning algorithm until the evaluation scores of all the turning points are greater than the first threshold, so as to obtain the final water supply network distribution path; the second path planning algorithm is obtained by introducing barrier influence factors and target influence factors to improve the heuristic function of the ant colony algorithm.
2. The method for planning a water supply network routing path of claim 1, wherein said planning a path based on the number of obstacles around the selectable nodes in the grid map to obtain an initial water supply path comprises:
determining a starting point and an end point of the water supply network in the grid map;
starting from the starting point, sequentially judging whether the number of obstacles around the current node is greater than a second threshold value, if so, selecting the next node by adopting the second path planning algorithm; if not, selecting the next node according to the following formula to obtain an initial water supply path from the starting point to the end point:
Figure FDA0003557868520000011
wherein j is the current node, j' is the next node, NxAnd for the total row number of the grid map, amount is the number of selectable nodes in 8 nodes adjacent to the current node.
3. The method for planning a water supply network routing path of claim 1, wherein the path optimizing the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path comprises:
step S31, calculating a path length of the initial water supply path;
step S32, determining a variation path length according to the path length of the initial water supply path;
step S33, randomly selecting two nodes from the initial water supply path as variation points according to the variation path length;
step S34, searching a path between the two variation points according to the first path planning algorithm to obtain a variation path;
step S35, comparing an initial path of the two change points in the initial water supply path with the variation path, and updating the initial path by using the variation path when the variation path is better than the initial path;
iteratively executing the steps S31 to S35 for M times, and taking the initial path obtained by updating the last iteration as a primary optimized water supply path; wherein M is a positive integer.
4. A method for planning a water supply network layout path according to claim 3, wherein the determining of the variant path length from the path length of the initial water supply path is:
rounding the 1/n path length of the initial water supply path to obtain a variable path length; wherein n is a positive integer.
5. The method for planning a water supply network routing path of claim 3, wherein said searching a path between two of said variation points according to said first path planning algorithm to obtain a variation path comprises:
taking one of the variation points as a source node and the other variation point as a target node, and sequentially selecting the next node from the source node according to the following formula until obtaining a variation path from the source node to the target node:
Figure FDA0003557868520000031
where s is the next node, (i, j) is the path from node i to node j, τi,j(t) intensity of pheromone on path (i, j) at time t, vi,j(t) Water flow velocity, delay, of path (i, j)i,j(t) the water flow transit time of path (i, j), (i, u) the path from node i to node u, τi,u(t) intensity of pheromone on path (i, u) at time t, vi,u(t) Water flow velocity, delay, of path (i, u)i,u(t) is the water flow transit time of the path (i, u), α is the weight of pheromone intensity, β is the weight of water flow transit speed, γ is the weight of water flow transit delay, NodeSum (1, μ) is the number of times a node μ passes, allowkAnd rand is a preset parameter for the node set which can be selected by the current ant.
6. The method for planning a water supply network layout path of claim 1, wherein the heuristic function of the second path planning algorithm is specifically:
Figure FDA0003557868520000032
Figure FDA0003557868520000033
wherein eta isi,jIs heuristic information of the path (i, j), di,GIs a target influence factor, di,GFor indicating the distance, Σ, from node i to end point Gobs∈Pdi,obsIs an obstacle influence factor, sigmaobs∈Pdi,obsK is used for representing the sum of distances of all obstacles to the node i in the influence range P of the obstacles1Is the weight of the target impact factor, k2As weights for obstacle influencing factors, allowkSet of nodes selectable for a current ant, NxFor the total number of lines of the grid map, NyThe total number of columns of the grid map.
7. The method for planning a water supply network layout path according to claim 1, wherein the preset water supply evaluation function G (v, ω) is specifically:
Figure FDA0003557868520000034
wherein v is the linear velocity of water flow transmission, ω is the angular velocity of water flow transmission, angle (v, ω) is the direction angle evaluation function, angle (v, ω) is used to represent the deviation of the direction angle between two turning points, vel (v, ω) is the velocity magnitude evaluation function, ξ is the weighting coefficient of the direction angle evaluation function,
Figure FDA0003557868520000041
the weighting coefficient of the velocity magnitude evaluation function is denoted by σ, and the smoothing coefficient is denoted by σ.
8. A planning device for water supply pipe network layout path is characterized by comprising:
the rasterization module is used for rasterizing the layout area of the water supply network to obtain a grid map of the layout area;
the initial path planning module is used for planning paths based on the number of obstacles around the selectable nodes in the grid map to obtain an initial water supply path;
the primary path optimization module is used for optimizing the path of the initial water supply path based on a first path planning algorithm to obtain a primary optimized water supply path; wherein the first path planning algorithm is an adaptive mutation ant colony algorithm;
the secondary path optimization module is used for evaluating turning points of the primary optimized water supply path according to a preset water supply evaluation function, and when the evaluation score of any turning point is smaller than or equal to a first threshold value, the path between the last turning point and the next turning point of the turning point is searched again by adopting the first path planning algorithm and the second path planning algorithm until the evaluation scores of all the turning points are larger than the first threshold value, so that a final water supply network layout path is obtained; the second path planning algorithm is obtained by introducing barrier influence factors and target influence factors to improve the heuristic function of the ant colony algorithm.
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor when executing the computer program implementing the method of planning a water supply network deployment path according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform a method of planning a water supply network deployment path as claimed in any one of claims 1 to 7.
CN202210281202.2A 2022-03-22 2022-03-22 Planning method, device and equipment for water supply pipe network layout path and storage medium Pending CN114722546A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117151577A (en) * 2023-10-31 2023-12-01 南京职豆豆智能科技有限公司 Combined path optimization method and system considering group heterogeneity
CN117151577B (en) * 2023-10-31 2024-02-06 南京职豆豆智能科技有限公司 Combined path optimization method and system considering group heterogeneity

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