CN115578525A - Engineering route selection optimization system and method in complex environment - Google Patents
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Abstract
The application discloses a system and a method for optimizing engineering route selection in a complex environment, wherein the system comprises: the system comprises a multi-feature data set selection module, a model hypothesis module, a pheromone updating module and a state transition probability module; the multi-feature data set selection module is used for acquiring construction site data; the model assumption module is used for acquiring a construction site model according to construction site data; the pheromone updating module is used for acquiring a basic optimal route according to the construction site model; and the state transition probability module is used for optimizing the basic optimal route to obtain the optimal route. The method and the device obtain three-dimensional terrain data based on the three-dimensional terrain reconstruction technology of the unmanned aerial vehicle, use a line selection optimization improvement algorithm, and accurately express the process characteristics of finding the optimal path. By utilizing three-dimensional imaging, the optimal path is converted into a three-dimensional image which is more fit with an actual construction site, the accuracy of the construction site is improved, the cost of manpower and material resources is reduced, and the working efficiency is improved.
Description
Technical Field
The application relates to the technical field of path planning, in particular to a complex environment engineering route selection optimization system and method.
Background
Because China has wide country soil, complex terrain, few plains, more hills and mountainous areas and complex meteorological conditions, the existing path optimizing technology can not meet the requirements of rapidness and high efficiency for the path planning construction of large-scale projects such as extra-high voltage and trans-regional power grids.
At the present stage, domestic and foreign enterprises actively explore a method and a strategy for three-dimensional terrain modeling by using an unmanned aerial vehicle as an auxiliary tool for site selection in order to improve the accuracy and efficiency of site selection in engineering construction, and have achieved a lot of achievements. The electric power field of patrolling and examining is also comparatively urgent to unmanned aerial vehicle's demand.
Disclosure of Invention
In order to solve the great shortcoming of the artifical site selection degree of difficulty of surveying on the spot, this application provides the engineering route selection optimization system of complex environment, includes: the system comprises a multi-feature data set selection module, a model hypothesis module, a pheromone updating module and a state transition probability module;
the multi-feature data set selection module is used for acquiring construction site data;
the model assumption module is used for acquiring a construction site model according to the construction site data;
the pheromone updating module is used for acquiring a basic optimal route according to the construction site model;
and the state transition probability module is used for optimizing the basic optimal route to obtain an optimal route.
Preferably, the job site data includes: the method comprises the steps of early-stage data standard acquisition, terrain data acquisition and vegetation coverage characteristic acquisition.
Preferably, the preliminary data criteria include: an approach road and an in-field road design principle.
Preferably, the method for acquiring the vegetation coverage characteristics comprises the following steps:
obtaining a forest model estimation model by using an unmanned aerial vehicle aerial survey forest model;
and obtaining the covered features through the forest model estimation model.
Preferably, the method for obtaining the forest model estimation model comprises the following steps:
estimating a point cloud model by using an unmanned aerial vehicle aerial survey forest model, and generating point cloud data;
and post-processing the point cloud data to obtain a three-dimensional model, and establishing the forest model estimation model.
Preferably, the model assumption module includes: an unmanned aerial vehicle simplified model and an obstacle simplified model;
the simplified model of the unmanned aerial vehicle is a cuboid and is used for better judging the position of the unmanned aerial vehicle so as to avoid the risk of colliding with a mountain;
the simplified model of the obstacle is a unimodal function of different heights.
Preferably, the work flow of the pheromone updating module comprises: the base optimal route is obtained using an ant colony algorithm.
Preferably, the workflow of the state transition probability module includes: and optimizing the basic optimal route by adopting a state transition probability rule and using a pseudorandom method, so as to avoid entering a locally optimal trap and obtain the optimal route.
The application also provides an engineering route selection optimization method in a complex environment, which comprises the following steps:
collecting construction site data, wherein the site data comprises: early data standard, topographic data and vegetation coverage characteristics;
acquiring a construction site model according to the construction site data;
acquiring a basic optimal route according to the construction site model;
and optimizing the basic optimal route to obtain an optimal route.
Compared with the prior art, the beneficial effects of this application are as follows:
the method and the device for obtaining the three-dimensional terrain data based on the unmanned aerial vehicle three-dimensional terrain reconstruction technology use a line selection optimization improvement algorithm to accurately express the process characteristics of finding the optimal path. The optimal path is converted into a stereo image which is more fitted with an actual construction site by utilizing three-dimensional imaging, so that the accuracy of the construction site is improved, the cost of manpower and material resources is reduced, and the working efficiency is improved.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a schematic diagram of the system architecture of the present application;
FIG. 2 is a schematic view of a process for acquiring topographic data according to the present application;
FIG. 3 is a schematic diagram of a point cloud generated from data collected by an unmanned aerial vehicle aerial survey forest;
FIG. 4 is a simplified model diagram of the unmanned aerial vehicle of the present application;
FIG. 5 is a schematic diagram illustrating a relative distance between an unmanned aerial vehicle and a peak according to the present application;
fig. 6 is a schematic flow chart of the method of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
As shown in fig. 1, a schematic diagram of the application system structure includes: the system comprises a multi-feature data set selection module, a model hypothesis module, a pheromone updating module and a state transition probability module; the multi-feature data set selection module is used for acquiring construction site data; the model assumption module is used for acquiring a construction site model according to construction site data; the pheromone updating module is used for acquiring a basic optimal route according to the construction site model; and the state transition probability module is used for optimizing the basic optimal route to obtain the optimal route.
Firstly, a multi-feature data set selection module is used for collecting construction site data, and the method comprises the following steps: prophase data standards, terrain data, and vegetation coverage characteristics. The multi-feature dataset selection module chooses the measurement device to be a drone equipped with a 2000-megapixel zoom camera, a 1200-megapixel wide-angle camera, a 1200-meter laser range finder, a 640 x 512 thermal imaging camera, such as, for example, H20T, ca.
And the early-stage data standard acquisition comprises the design principles of approach roads, roads in the field, road longitudinal slopes and road curved surfaces. The off-site roads mainly use the existing national, provincial (autonomous region, direct prefecture city), city, county, village and town grade roads and municipal roads, and follow the wind power plant road design standard of China national electric group company.
As shown in fig. 2, the method for collecting topographic data includes utilizing the oblique photogrammetry technique of unmanned aerial vehicle, which includes two major fields: the field operation part comprises the steps of surveying a working area in advance, arranging image control point icons and image control point data acquisition, designing a flight path of the unmanned aerial vehicle, setting related flight parameters, and checking the quality of pictures acquired by flight to ensure that the quality is qualified; the internal work part comprises the preparation work of photo format conversion and the like, the import of photos and POS data, the space-three encryption by utilizing image control points distributed by the external work, the three-dimensional modeling, the generation of DOM/DSM/point cloud data, the three-dimensional mapping, the production of DLG, the precision evaluation, the external work adjustment and drawing, the internal work editing and the result submission.
And (4) estimating a point cloud model for the unmanned aerial vehicle aerial survey forest model according to vegetation coverage characteristics. The laser radar matched with the unmanned aerial vehicle platform is matched with the optical camera to rapidly fly on a route drawn in the designated flying range, point cloud data is generated, and a three-dimensional model is obtained through post-processing as shown in fig. 3. And establishing an unmanned aerial vehicle aerial survey forest model estimation model to realize the statistics of the number of trees in the area. And obtaining data quantity measurement and calculation including forest distribution range, tree height, breast diameter, biomass, accumulation, carbon reserve and the like. By carrying multi-source unmanned aerial vehicle loads, forest management information factors, such as tree height, crown width, canopy closure degree and other tree measuring factors, are extracted. The forest resource information in the target area range can be acquired quickly, efficiently and at low cost. Based on the texture measurement driving extraction method, different scenes are distinguished from the image by using a texture measurement algorithm, and a target area is extracted, so that the tree species division marking effect in the area is realized. And extracting the forest trees in a layering manner, fitting the contour of the target object and finishing target extraction.
And then simulating construction according to the construction site data collected by the multi-feature data set selection moduleAnd on site, manufacturing a construction site model by using the model hypothesis module. The model assumption module comprises: unmanned aerial vehicle simplified model and obstacle simplified model. The model simplification of the unmanned aerial vehicle is shown in fig. 4, thirteen parameters are required in the whole model simplification process, and the parameters are respectively as follows: x (unmanned aerial vehicle length), y (unmanned aerial vehicle width), z (unmanned aerial vehicle height) (unit: cm); the included angle (unit: degree) between the straight line formed by the theta pole and any point and the positive direction of the z axis;
is an angle (unit: degree) formed by a plane passing through the z-axis and an arbitrary point in space and a coordinate plane ZOX; (x, y) represents the height (unit: m) at the upper point of the first peak; k is a radical of 1 And k 2 2 adjustable parameters for changing the shape and size of the peak; x is the number of i And y i The abscissa and ordinate representing the center of the peak; x is the number of pi And y pi Then the slope of the peak i in the x-direction and the y-direction is represented.
Then, a relative position model of the unmanned aerial vehicle and the mountain peak in the operation process is established by referring to the field situation, as shown in fig. 5.
S1, inputting real parameters: x, y and z are the length, width and height (unit: cm) of the unmanned aerial vehicle; the included angle (unit: degree) between the straight line formed by the theta pole and any point and the positive direction of the z axis;
is an angle (unit: degree) formed by a plane passing through the z-axis and an arbitrary point in space and a coordinate plane ZOX; k is a radical of 1 And k 2 2 adjustable parameters for changing the shape and size of the peak; x is a radical of a fluorine atom i And y i The abscissa and ordinate representing the center of the peak; x is the number of pi And y pi Then the slope of the peak i in the x-direction and the y-direction is represented.
S2, calculating the diagonal length of the unmanned aerial vehicle model, wherein a formula is calculated as follows:
s3, placing the unmanned aerial vehicle in a sphere with a half of a diagonal line of the sphere as a radius, establishing a polar coordinate system with the current position of a central coordinate of the unmanned aerial vehicle as an origin, and introducing a polar coordinate formula of the sphere. Calculating the formula:
s4, calculating the height (x, y) of the ith peak, namely the vertical coordinate, and calculating the formula:
s5, when the formula 4 is met, any point (x, y, z) on the sphere is not on the surface of or inside the peak, and the unmanned aerial vehicle can be guaranteed not to collide with the peak under any condition. Calculating the formula:
R(x，y，z)＞Z i (x，y) (4)
and the pheromone updating module acquires a basic optimal route by utilizing an ant colony algorithm according to the construction site model and the relative position relation between the unmanned aerial vehicle and the mountain peak. Pheromones are signals communicated among ants, and the concentration of the pheromones is in negative correlation with the length of a path, namely, the higher concentration of the pheromones indicates that the path is shorter. The routes from the origin to the destination have different paths, each path being selected with the same probability. I.e. the pheromone concentration on each path is equal at the initial moment. Each ant can release the pheromone in the process of going to the destination, and meanwhile, the ants can feel the pheromone released by other ants. Subsequent ants will have a higher probability of selecting a path of high concentration pheromone. Thus, a positive feedback mechanism is formed, and the optimal path is searched in an iterative loop mode.
The process of selecting the path is accompanied by the release of pheromone and the volatilization of the pheromone. After all ants complete one cycle, the pheromone concentration between each node needs to be updated.
The pheromone update formula is as follows:
in the formula, parameter rho is a volatilization factor of pheromone;
the pheromone concentration released by the kth ant on the connection path of the node i and the node j is obtained; delta tau ij The sum of the concentration of pheromones released by all ants on the connection path of the node i and the node j; q is a constant and represents the total amount of pheromones released by the ants in one cycle; l is a radical of an alcohol k The length of the path taken by the kth ant.
Usually, ants select paths with large pheromone concentration as the roads for the next step to go forward, in the iterative process, ants select paths according to the pheromone concentration on each path, and the transfer direction determines the quality of the final optimization result. But this may trap into locally optimal traps. Therefore, when the probability of each node which can be removed is calculated, the selection of the path adopts a state transition probability rule, and a pseudo-random method is used for avoiding the situation.
Utilizing a state transition probability module to perform transition probability, wherein the workflow comprises: suppose that the distance between node i and node j is d ij And the concentration of pheromone on the connecting path of the node i and the node j at the time t is tau ij (t) and at the initial time, the pheromone concentration on the connection path of each node is the same, i.e., τ ij (0)＝τ 0 。
Calculating probability of ant k from node i to node j at time t
The calculation formula comprises:
in the formula eta ij (t) is a heuristic function; all k (k =1,2.., m) is a set of selectable nodes, α is an information factor, the larger the value of which, the more the concentration of the pheromone plays a role in the transition; beta is a heuristic factor, which means that the heuristic function plays a larger role in the transfer, i.e. ants select nodes with short distance with high probability.
Thereby selecting the optimal route of the complex environmental engineering.
Example two
As shown in fig. 6, is a schematic flow chart of the method of the present application, and includes the steps of:
s1, collecting construction site data, wherein the site data comprises: prophase data standards, terrain data, and vegetation coverage characteristics.
In this embodiment, the multi-feature dataset selection module is used to collect field data, which includes: early data standards, topographic data and vegetation coverage characteristics; the preliminary data criteria include: the design principle of the approach road, the in-site road, the road longitudinal slope and the road curved surface. The off-site roads mainly use the existing national, provincial (autonomous region, direct district city), city, county, village and town grade roads and municipal roads and follow the wind power plant road design standard of China national electric group company.
Afterwards, the terrain data is collected by utilizing the oblique photogrammetry technology of the unmanned aerial vehicle, and the oblique photogrammetry technology of the unmanned aerial vehicle comprises two major parts of the field and the field: the field operation part comprises the steps of surveying a working area in advance, arranging image control point icons and image control point data acquisition, designing a flight path of the unmanned aerial vehicle, setting related flight parameters, and checking the quality of pictures acquired by flight to ensure that the quality is qualified; the internal work part comprises the preparation work of photo format conversion and the like, the import of photos and POS data, the space-three encryption by utilizing image control points distributed by the external work, the three-dimensional modeling, the generation of DOM/DSM/point cloud data, the three-dimensional mapping, the production of DLG, the precision evaluation, the external work adjustment and drawing, the internal work editing and the result submission.
And estimating a point cloud model by using an unmanned aerial vehicle aerial survey forest model to obtain vegetation coverage characteristics. The laser radar matched with the unmanned aerial vehicle platform is used for fast flying on a route planned in the designated flying range, point cloud data are generated, and a three-dimensional model is obtained through post-processing as shown in fig. 3. And establishing an unmanned aerial vehicle aerial survey forest model estimation model to realize the statistics of the number of trees in the area.
And S2, acquiring a construction site model according to the construction site data.
In the embodiment, a model assumption module is used for simulating a construction site according to collected construction site data, and then a relative position model of the unmanned aerial vehicle and a mountain peak in the operation process is established by referring to the field situation.
And S3, acquiring a basic optimal route according to the construction site model.
Then, in the embodiment, the basic optimal route is obtained by using the ant colony algorithm according to the construction site model and the relative position relationship between the unmanned aerial vehicle and the mountain peak by using the pheromone updating module. Pheromones are signals communicated among ants, and the concentration of the pheromones is in negative correlation with the length of a path, namely, the higher concentration of the pheromones indicates that the path is shorter. The routes from the origin to the destination have different paths, each path being selected with the same probability. I.e. the pheromone concentration on each path is equal at the initial moment. Each ant can release the pheromone in the process of going to the destination, and meanwhile, the ants can feel the pheromones released by other ants. Subsequent ants will have a higher probability of selecting a path of high concentration pheromone. Thus, a positive feedback mechanism is formed, and the optimal path is searched in an iterative loop mode.
And S4, optimizing the basic optimal route to obtain the optimal route.
In this embodiment, ants generally select paths with high pheromone concentration as the path to go forward next, and in the iterative process, ants select paths according to the pheromone concentration on each path, and the transfer direction determines the quality of the final optimization result. But this may trap into locally optimal traps. Therefore, to avoid this, when calculating the probability of each node that can go, the state transition probability rule is used, and the pseudo-random method is used to select the path by the state transition probability module.
The above-described embodiments are merely illustrative of the preferred embodiments of the present application, and do not limit the scope of the present application, and various modifications and improvements made to the technical solutions of the present application by those skilled in the art without departing from the spirit of the present application should fall within the protection scope defined by the claims of the present application.
Claims (9)
1. A complex environment engineering route selection optimization system is characterized by comprising: the system comprises a multi-feature data set selection module, a model hypothesis module, a pheromone updating module and a state transition probability module;
the multi-feature data set selection module is used for acquiring construction site data;
the model assumption module is used for acquiring a construction site model according to the construction site data;
the pheromone updating module is used for acquiring a basic optimal route according to the construction site model;
and the state transition probability module is used for optimizing the basic optimal route to obtain an optimal route.
2. The complex environment project route selection optimization system of claim 1, wherein the job site data comprises: the method comprises the steps of early-stage data standard acquisition, terrain data acquisition and vegetation coverage characteristic acquisition.
3. The complex environment engineering route selection optimization system of claim 2, wherein the preliminary data criteria comprise: an approach road and an in-field road design principle.
4. The complex environment engineering route selection optimization system according to claim 2, wherein the method for obtaining the vegetation coverage features comprises:
obtaining a forest model estimation model by using an unmanned aerial vehicle aerial survey forest model;
and obtaining the covered features through the forest model estimation model.
5. The complex environment engineering route selection optimization system according to claim 4, wherein the method for obtaining the forest model estimation model comprises:
estimating a point cloud model by using an unmanned aerial vehicle aerial survey forest model, and generating point cloud data;
and post-processing the point cloud data to obtain a three-dimensional model, and establishing the forest model estimation model.
6. The complex environment engineering route selection optimization system of claim 1, wherein the model hypothesis module comprises: an unmanned aerial vehicle simplified model and an obstacle simplified model;
the simplified model of the unmanned aerial vehicle is a cuboid and is used for better judging the position of the simplified model so as to avoid the risk of colliding with a mountain;
the simplified model of the obstacle is a unimodal function of different heights.
7. The complex environment engineering route selection optimization system of claim 1, wherein the workflow of the pheromone updating module comprises: the base optimal route is obtained using an ant colony algorithm.
8. The complex environment engineering route selection optimization system of claim 7, wherein the workflow of the state transition probability module comprises: and optimizing the basic optimal route by adopting a state transition probability rule and using a pseudorandom method, so as to avoid entering a locally optimal trap and obtain the optimal route.
9. A method for optimizing engineering route selection in a complex environment is characterized by comprising the following steps:
collecting construction site data, wherein the site data comprises: early data standards, topographic data and vegetation coverage characteristics;
acquiring a construction site model according to the construction site data;
acquiring a basic optimal route according to the construction site model;
and optimizing the basic optimal route to obtain an optimal route.
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| CN117874895A (en) * | 2024-02-08 | 2024-04-12 | 广东省装配式建筑设计院有限公司 | Standardized and diversified design method and system for assembled agricultural house |
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