CN114093189B - Motorcade path optimization method and system - Google Patents
Motorcade path optimization method and system Download PDFInfo
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- CN114093189B CN114093189B CN202111632209.6A CN202111632209A CN114093189B CN 114093189 B CN114093189 B CN 114093189B CN 202111632209 A CN202111632209 A CN 202111632209A CN 114093189 B CN114093189 B CN 114093189B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention relates to a motorcade path optimization method and a motorcade path optimization system, wherein the method comprises the following steps: planning a route of a fleet for executing tasks based on an A algorithm to obtain a driving route of the fleet; determining a real-time position of a motorcade when the motorcade runs according to a running path based on a real-time position model of motorcade running; determining the departure time of the fleet based on the requirement of the fleet for executing the task; determining the time of the fleet to reach each node in the driving path; determining the observation time of the satellite on the ground area of the motorcade to execute tasks based on the perspective model of the satellite on the ground target point; based on a satellite avoidance algorithm, the fleet is adjusted to enter a gathering place at a path node to wait at the observation time or arrive at a working place before the observation time, the fleet running parameters in the running path are adjusted, and the running path is optimized. The invention can improve the survival ability of the motorcade and ensure that the motorcade task is successfully completed.
Description
Technical Field
The invention relates to the field of path planning, in particular to a fleet path optimization method and system.
Background
In the motorcade running path planning technology, the factors of shortest path and fastest arrival are mainly considered in the prior art, and the correlation analysis of complex terrain environment and guarantee conditions along the maneuvering route is lacked, wherein the observation capability of a satellite on the ground seriously influences the completion of tasks. In recent years, with the progress of sensor technology, the satellite earth observation capability is gradually improved. The number of loads carried by the satellite is continuously increased, so that the observation range of the satellite on the ground is continuously expanded, the observation time and frequency of the satellite on a fixed area are continuously increased, and the observation precision of the satellite on the area is also continuously improved. These increased observation capabilities present significant challenges to survival, leading to difficulty in fleet performance tasks.
Disclosure of Invention
The invention aims to provide a motorcade path optimization method and a motorcade path optimization system, so as to improve the survivability of a motorcade and ensure that motorcade tasks are smoothly completed.
In order to achieve the purpose, the invention provides the following scheme:
a fleet path optimization method, comprising:
planning a route of a fleet for executing tasks based on an A algorithm to obtain a running route of the fleet; the driving path is the shortest path from a starting point to a working place for executing a task of the motorcade, and comprises a plurality of nodes, wherein the plurality of nodes comprise the starting point, the working place and a plurality of path nodes;
determining the real-time position of the motorcade when the motorcade runs according to the running path based on the real-time position model of the motorcade running;
determining a departure time of the fleet of vehicles based on requirements of the fleet of vehicles to perform tasks;
determining the time of the fleet to reach each node in the driving path based on the departure time of the fleet and the real-time position of the fleet when the fleet drives according to the driving path;
determining the observation time of a satellite on a ground area of a motorcade for executing tasks based on a perspective model of the satellite on a ground target point;
according to the observation time of the satellite on the ground and the time of the fleet reaching each node in the running path, adjusting the fleet entering an assembly ground waiting at the node of the route of the fleet or reaching the working place before the observation time based on a satellite evasion algorithm at the observation time, adjusting the running parameters of the fleet in the running path, and optimizing the running path; the fleet travel parameters include a departure time, a travel speed, and a wait time for staging at each pathway node of the fleet.
Optionally, the planning a route of a fleet for executing tasks based on the a-star algorithm to obtain a driving route of the fleet specifically includes:
acquiring a road network of a ground area where the motorcade executes tasks; the road network comprises a plurality of road nodes and road information;
and planning a route of a fleet executing tasks by adopting an A-star algorithm based on the road network, the starting point and the working place to obtain a running route of the fleet.
Optionally, the determining the observation time of the satellite on the ground based on the perspective model of the satellite on the ground target point specifically includes:
determining a through-view model of the satellite to a ground target point according to the type of the satellite; the perspective model of the satellite to the ground target point comprises a cone perspective model without a yaw angle and a perspective model with a yaw angle;
and determining the observation time of the satellite on the ground area of the motorcade for executing tasks based on the through-view model of the satellite on the ground target point.
Optionally, the adjusting, according to the observation time of the satellite on the ground and the time of the fleet reaching each node in the travel path, a collective ground wait at a node of the fleet access is adjusted at the observation time or the fleet reaches the work place before the observation time based on a satellite avoidance algorithm, adjusting a parameter of the fleet travel in the travel path, and optimizing the travel path specifically includes:
when the observation time t of the satellite to the ground and the departure time t of the motorcade are obtained 1 Satisfy t 1 When the vehicle moves to t, advancing or delaying the departure time of the vehicle team, and adjusting the driving speed of the vehicle after the vehicle team departs;
when the observation time t of the satellite to the ground and the time t of the fleet reaching the ith node in the driving path are obtained i Satisfy t i When the vehicle belongs to t, the running speed of the fleet is improved, and the vehicle arrives at the aggregation place at the ith node in advance to wait; or wait at a previous staging area; i is more than 1 and less than k, and k is the number of nodes on the driving path;
when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the driving path i And time t of the i-1 st node i-1 Satisfy t i-1 ＜t＜t i When the node is in the I-th node, the starting time of the previous node is advanced, and the node waits at the I-th node;
when the observation time t of the satellite to the ground and the arrival time t of the motorcade are obtained k Satisfy t k-1 ＜t＜t k And adjusting the time of the fleet reaching the k-1 node in the driving path, waiting at the aggregation ground at the k-1 node, and adjusting the driving speed after the k-1 node.
Optionally, the method further includes:
when the observation time t of the satellite to the ground and the arrival time t of the motorcade are obtained k Satisfy t = t k And determining the execution condition which is not in line with the execution task of the motorcade, and adjusting the execution task of the motorcade.
Optionally, the observation time t of the satellite to the ground and the time t of the fleet reaching the ith node in the driving path are respectively calculated i Satisfy t = t i When the vehicle arrives at the gathering place of the ith node in advance, the running speed of the vehicle fleet is increased, and the vehicle fleet arrives at the gathering place of the ith node in advance to wait; or waiting at the previous aggregation site, specifically comprising:
when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the driving path i Satisfy t = t i Judging whether the driving time of the motorcade from a previous node to an ith node is crossed with the observation time of the satellite on the ground or not;
if the travel time of the motorcade from the previous node to the ith node is crossed at the observation time of the satellite on the ground, returning to the step of i And time t of the i-1 st node i-1 Satisfy t i-1 ＜t＜t i Advancing the starting time of the previous rendezvous point, and waiting for the rendezvous point at the ith node;
if the traveling time of the motorcade from the previous node to the ith node is not crossed in the observation time of the satellite on the ground, judging whether the motorcade waits at the assembly place of the previous node or not;
if the motorcade waits at the gathering place of the previous node, improving the running speed of the motorcade, advancing the time of the motorcade arriving at the previous node, arriving at the ith node before the observation time, and waiting at the gathering place of the ith node;
if the motorcade does not wait at the gathering place of the previous node, improving the running speed of the motorcade according to the standard that the motorcade reaches the ith node from the previous node before the observation time, and judging whether the improved running speed exceeds the speed limit or not;
if the increased driving speed exceeds the speed limit, waiting at the aggregation place at the previous node;
and if the increased running speed does not exceed the speed limit, running from the previous node to the ith node according to the increased running speed, and waiting at the aggregation place of the ith node.
The invention also provides a fleet path optimization system, comprising:
the driving path planning module is used for planning the path of the fleet for executing tasks based on an A-x algorithm to obtain the driving path of the fleet; the driving path is the shortest path from a starting point to a working place for executing a task of the motorcade, and comprises a plurality of nodes, wherein the plurality of nodes comprise the starting point, the working place and a plurality of path nodes;
the real-time position determining module is used for determining the real-time position of the motorcade when the motorcade runs according to the running path based on the real-time position model of the motorcade running;
the departure time determining module is used for determining the departure time of the motorcade based on the requirement of the motorcade for executing the task;
the arrival time determining module is used for determining the time of the fleet reaching each node in the driving path based on the departure time of the fleet and the real-time position of the fleet when the fleet drives according to the driving path;
the observation time determining module is used for determining the observation time of the satellite on the ground area of the motorcade for executing tasks based on the perspective model of the satellite on the ground target point;
the motorcade path optimization module is used for adjusting the staging waiting at the motorcade entry node at the observation time or the working place before the observation time based on a satellite evasion algorithm according to the observation time of the satellite on the ground and the time of the motorcade reaching each node in the running path, adjusting the running parameters of the motorcade in the running path and optimizing the running path; the fleet travel parameters include a departure time, a travel speed, and a wait time for staging at each pathway node of the fleet.
Optionally, the driving path planning module specifically includes:
the road network acquisition unit is used for acquiring a road network of a ground area where the motorcade executes tasks; the road network comprises a plurality of road nodes and road information;
and the path planning unit is used for planning the path of the fleet for executing tasks by adopting an A-star algorithm based on the road network, the starting point and the working place to obtain the running path of the fleet.
Optionally, the fleet path optimization module specifically includes:
a first adjusting unit for adjusting the observation time t of the satellite to the ground and the departure time t of the motorcade 1 Satisfy t 1 When the vehicle moves to t, advancing or delaying the departure time of the vehicle team, and adjusting the driving speed of the vehicle after the vehicle team departs;
a second adjusting unit, configured to adjust the time t when the satellite observes the ground and the time t when the fleet reaches the ith node in the travel path i Satisfy t i When the vehicle belongs to t, the running speed of the fleet is improved, and the vehicle arrives at the aggregation place at the ith node in advance to wait; or wait at a previous staging area; i is more than 1 and less than k, and k is the number of nodes on the driving path;
third stepAn adjusting unit, configured to adjust the time t when the satellite observes the ground and the time t when the fleet reaches the ith node in the travel path i And time t of the i-1 st node i-1 Satisfy t i-1 ＜t＜t i When the node is in the I-th node, the starting time of the previous node is advanced, and the node waits at the I-th node;
a fourth adjusting unit, for adjusting the observation time t of the satellite on the ground and the arrival time t of the fleet k Satisfy t k-1 ＜t＜t k Adjusting the time of the fleet reaching the k-1 node in the driving path, waiting at the aggregation ground at the k-1 node, and adjusting the driving speed after the k-1 node;
a reporting unit, for reporting the observation time t of the satellite to the ground and the arrival time t of the fleet k Satisfy t = t k And determining the execution conditions which do not accord with the execution task of the motorcade, and adjusting the execution task of the motorcade.
Optionally, the second adjusting unit specifically includes:
a first judging subunit, configured to judge, when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the driving path are the same i Satisfy t = t i Judging whether the driving time of the motorcade from a previous node to an ith node is crossed with the observation time of the satellite on the ground or not;
the return subunit is used for returning to the third adjusting unit if the running time of the motorcade from the previous node to the ith node is crossed at the observation time of the satellite on the ground;
the second judgment subunit is used for judging whether the motorcade waits at the aggregation ground of the previous node or not if the driving time of the motorcade from the previous node to the ith node is not crossed at the observation time of the satellite on the ground;
a first adjusting subunit, configured to increase a driving speed of the fleet, advance a time when the fleet arrives at a previous node, arrive at the ith node before the observation time, and wait at the rendezvous point of the ith node if the fleet waits at the rendezvous point of the previous node;
a third judging subunit, configured to, if the fleet is not waiting at the aggregation site of the previous node, improve the traveling speed of the fleet according to a criterion that the fleet reaches the ith node from the previous node before the observation time, and judge whether the improved traveling speed exceeds a speed limit;
a second adjustment subunit, configured to wait at the aggregation point at the previous node if the increased traveling speed exceeds the speed limit;
and the third adjusting subunit is used for driving from the previous node to the ith node according to the increased driving speed and waiting at the aggregation place of the ith node if the increased driving speed does not exceed the speed limit.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
after the shortest driving path is obtained, a satellite evasion algorithm is adopted based on analysis of a satellite observation process, so that a motorcade waits at a gathering place of a path node, observation of a satellite is evaded, smooth execution of tasks is guaranteed, and the viability of the motorcade is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow diagram of a fleet path optimization method of the present invention;
FIG. 2 is a schematic diagram of a road network structure according to the present invention;
FIG. 3 is a schematic view of a driving route obtained based on a road network according to the present invention;
FIG. 4 is a diagram illustrating a valuation function of the present invention;
FIG. 5 is a schematic view of a geocentric spherical coordinate system of the present invention;
FIG. 6 is a schematic diagram of a rectangular coordinate system of the earth according to the present invention;
FIG. 7 is a schematic diagram of a real-time location model of the present invention;
FIG. 8 is a schematic diagram of a satellite performing area observation in a cone visibility model without a yaw angle according to the present invention;
FIG. 9 is a schematic view of instantaneous imaging in the perspective model with yaw angle of the present invention;
FIG. 10 is a first schematic view of a regional observation of the perspective model with yaw angle of the present invention;
FIG. 11 is a second schematic view of a region observation of the perspective model with yaw angle of the present invention;
FIG. 12 shows the present invention t i The schematic diagram of the satellite departure point when the epsilon is t;
FIG. 13 shows the present invention t i A driving parameter adjusting flow chart when the element belongs to t;
FIG. 14 shows the present invention t i A schematic diagram of satellite observation set area when the epsilon is t;
FIG. 15 shows the present invention t i A driving parameter adjusting flow chart when the element belongs to t;
FIG. 16 shows the present invention t i-1 ＜t＜t i A schematic diagram of a time satellite observation staging area;
FIG. 17 shows the present invention t i-1 ＜t＜t i A real-time driving parameter adjusting flow chart;
FIG. 18 shows t = t according to the present invention k A schematic view of a time satellite observation workspace.
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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a fleet path optimization method of the present invention, and as shown in fig. 1, the fleet path optimization method of the present invention includes the following steps:
step 100: and planning the route of the fleet for executing the task based on the A algorithm to obtain the driving route of the fleet. The invention relates to a method for processing a vehicle fleet, which comprises the following steps of firstly processing a vehicle fleet, judging whether a driving path is the shortest path from a starting point to a working place for executing a task, and if so, judging whether the driving path is the shortest path from the starting point to the working place for executing the task, wherein the driving path comprises a plurality of nodes.
The invention adopts A-algorithm to plan the shortest path, and the shortest path algorithm is all established on the basis of the graph. The graph is made up of a finite, nonempty set of vertices and a set of edges between vertices, generally expressed as: g = { V, E, a }. Where G represents a graph, V is the set of vertices in G, E is the set of edges in G, and A is the attribute set of edges. Therefore, road network information of a ground area where a fleet executes tasks is firstly constructed, a network graph is constructed, all topological points of the whole regional road network are constructed based on a graph structure, road nodes and side data are obtained by loading and processing shp-format road network data, the road network data support the loading, analysis and fusion processing of multi-level roads including national roads, provincial roads, high-speed roads, county roads and the like, and the constructed road network is shown in fig. 2 and is formed by fusing the multi-level roads.
Then, according to the network graph of the road nodes, a corresponding adjacency matrix A = a is constructed ijk+p+q+r×k+p+q+r Wherein
Let the coordinate of the ith point be (X) i ,Y i ) I =1,2, \8230;, k + p + q + r, with d ij Representing the distance between any two points i, j. If the two points are not communicated, the distance d between the two points is set ij ＝∞。
Then, based on the constructed adjacency matrix, an a-x algorithm is adopted to solve the optimal path based on the road network data set, and the obtained path is shown in fig. 3. The algorithm a is an intelligent search algorithm or an optimal priority algorithm, which means that the algorithm solves the problem by selecting the node with the lowest cost (shortest distance, least time consumption, etc.) among all possible path points, which is first considered to be the closest solution. In the graph structure the algorithm can be summarized as: starting from a specified node in the graph, a path node tree is constructed starting from this node, and paths are expanded one step at a time until stopping when one of the nodes of his paths is a predetermined target node. The evaluation function is a core part in heuristic search, a proper evaluation function is selected, the number of searched nodes is reduced when path search is carried out, the search time can be saved, and the optimal path can be obtained quickly. The algorithm needs to select a node to expand in each iteration of the main loop, and the decision is based on the evaluation of the node cost until a target node is found. The evaluation function of the algorithm A is as follows:
f(n)＝g(n)+h(n) (2)
where n refers to a node on the current path. g (n) refers to the actual cost from the starting node to node n along the generated path. And h (n) is an estimation cost for obtaining an optimal path from the node n to a target node, and is usually specified according to a practical problem, so h (n) is called a heuristic function. f (n) is the total cost of node n to reach the target node.
As shown in FIG. 4, assume A is the start node and D is the target node. The coordinates of the points are given in fig. 4. Assuming node B is selected as the current path node, g (B) is the distance or time from the originating node A to node B:
h (B) is the value of D from the node B to the target node, and the Manhattan distance is selected as a heuristic function in h (B). Then h (B) = | x 4 -x 2 |+|y 4 -y 2 |。
f (B) is the total cost of the node B to reach the target node D, then:
the algorithm chooses the node with the smallest f (n) value each time to expand to find the actual shortest path for the algorithm, the valuation function f (n) must be admissible and reasonable. This means that the function estimate must never be higher than the actual cost value to reach the target node.
The algorithm a is a heuristic search algorithm, and selects the node with the smallest cost value from the nodes which are likely to be path nodes when searching the path. The algorithm first creates two empty tables, named OPEN table, which stores nodes that have not been accessed and are ready to be examined, and CLOSE table, which stores accessed nodes from the OPEN table. When the algorithm searches, the minimum cost value node in the current OPEN table is expanded or whether the minimum cost value node is a target node is judged.
The path-finding flow of the A-star algorithm is as follows:
two empty tables are defined and named respectively: OPEN and CLOSE; the nodes which are not visited and are to be investigated are stored in the OPEN table, and the nodes which are visited are stored in the CLOSE table. The starting node is set as S, and the target node is set as T.
The start node S is put in the OPEN table.
It is determined whether the length of the OPEN table is 0. If the value is 0, the path cannot be found, and the exit is failed.
And if the length of the OPEN table is not 0, selecting the node N with the minimum total cost value f from the OPEN table.
The node N in the move OPEN table is placed in the CLOSE table.
And judging whether the node N is the target node T or not, and if so, indicating that a path is found and exiting.
If node N is not the target node, node N is extended. Let its adjacent node be M i (i is not more than k, k is adjacent to the node NThe number of connected nodes). For each adjacent node M i Calculating their total cost value f (M) i ). If node M i And if the two tables are not in the OPEN and CLOSE tables, the table is put into the OPEN table. Adding a pointer to the node N to point to the node N, so that a path can be conveniently obtained according to the return of the pointer after a target node is found; if node M i In the OPEN table, then the total cost value f (M) just calculated i ) And the total cost f' (M) of the node in the OPEN table i ) The values are compared, if f (M) i ) Smaller, indicating that a more optimal path was found, using f (M) i ) Instead of f' (M) i ) Changing the pointer of the node in the OPEN table to point to the current node N; if node M i In the CLOSE table, the node is skipped and the access to the node N and other adjacent nodes M is continued i 。
And jumping to the step3, and continuing to loop until an optimal path is found or a path cannot be found to exit.
The method can be used for obtaining the driving path of the motorcade.
And on the basis of obtaining the driving path by adopting an A-star algorithm, obtaining the maneuvering real-time position on the task path by using the driving real-time position model. And calculating the real-time position through a satellite to ground target visibility model, judging when and where the real-time position can be observed by the satellite, if the real-time position is observed by the satellite, making a decision and controlling the time quantum, entering a gathering ground in advance for gathering or passing through the area in advance, and finally obtaining a feasible driving scheme. The specific process is shown as step 200-step 600.
Step 200: and determining the real-time position of the motorcade when the motorcade runs according to the running path based on the real-time position model of the motorcade running. Usually, the route node information of the road network is longitude and latitude, and the longitude and latitude need to be converted into a geodetic rectangular coordinate for the convenience of resolving the real-time position. As shown in fig. 5 and 6, the geodetic rectangular coordinates of a node are (x, y, z), and the following equation is provided according to the spherical coordinate equation:
the relationship between the geodesic latitude and the geocentric latitude is as follows:
tgφ＝(1-e 2 )tgB (6)
and obtaining the geocentric latitude:
φ＝atg((1-e 2 )tgB) (7)
because the earth is a rotating ellipsoid, a section from the geographical north pole to the geographical south pole passing through the node is taken to obtain an ellipse:
from equation (8), the parametric equation can be derived:
from formulae (8) and (9):
considering again the effect of elevation:
ρ＝R+H (11)
in summary, the geodetic rectangular coordinates of the nodes can be obtained as:
and obtaining the geodetic rectangular coordinate of each node in the road network, and calculating the distance between any two nodes by using a two-point distance formula. Because the distance of each road is far less than the radius of the earth, the influence of the curvature of the earth surface can be ignored. Therefore, the distance between two points is directly regarded as a straight-line distance, and is obtained by the following formula:
after the distance of each short road is obtained, the maneuvering time on each short road can be obtained by using the following formula.
After the geodetic rectangular coordinates of each node are obtained, the real-time geodetic rectangular coordinates of driving need to be solved before the earth target point through-view model is analyzed by the satellite.
The average speed of the vehicle running on a section of road is v, the distance of the section of road is s, and the coordinates of the nodes 1 and 2 at the two ends of the road are respectively (x) 1 ,y 1 ,z 1 ) And (x) 2 ,y 2 ,z 2 ) The real-time location on the road is (x) s ,y s ,z s ) And travels from node 1 to node 2 as shown in fig. 7.
From the coordinates of the two nodes, a vector describing this way can be derived:
a vector can be obtained from the real-time position coordinates and the first node:
the principle according to which the ratio of vectors equals the ratio of vector modulo length is:
wherein t is the time spent on maneuvering from the node 1 to the real-time position, and the geodetic rectangular coordinate of the real-time position can be obtained by solving the formula (17):
step 300: the departure time of the fleet is determined based on the requirements of the fleet to perform the task. And (4) calculating the optimal path (comprising n roads) by using an algorithm given by A according to the set starting point and the working place. And determining the average driving speed according to the grade of each road in the path, and further calculating the passing time g (k) of each road. And presetting the time te for arriving at the working place according to the transmitting time required by the task. The initial value of the departure time is obtained in such a way that, taking into account the various possible emergencies during the travel, a processing time is set aside for the arrival at the work place on time, and the departure time is shifted further forward, namely:
in the formula, W is a constant.
Step 400: and determining the time of the fleet reaching each node in the driving path based on the departure time of the fleet and the real-time position of the fleet when the fleet drives according to the driving path.
Step 500: and determining the observation time of the satellite on the ground area of the fleet for executing the task based on the perspective model of the satellite on the ground target point. According to the specific situation of the satellite, the perspective model is divided into two cases: firstly, a simple cone visibility model without a side swing angle; secondly, because the sensors of some satellites for observing loads by radars have a yaw angle during observation, a visibility model with the yaw angle is considered.
(1) Simple cone visibility model without side swing angle
Because the height of the observation satellite is generally more than 300km, the influence of ground fluctuation on the observation effect is not considered on the basis of the rotational ellipsoid model. When the satellite observes above a target area, a certain coverage area is provided for the ground, and the coverage area can be judged by using the field angle of the satellite sensor. In the establishment of the satellite visibility model, all vector representations are in the earth fixed connection coordinate system.
As shown in FIG. 8, the target half-cone angle is set to be alpha, and the satellite, the target point and the geocenter are determinedIn a fixed plane, the satellite S is used as an origin point, and the earth center O and the ground target P1 are respectively connected as vectors
And
from the satellite sensor's field of view 2 θ, the first satisfied condition that an object can be observed is:
α≤θ (20)
and has the following components:
considering that conditions are satisfied on the other side of the earth, another constraint is added. As can be seen from fig. 8, when SP1 is tangent to the earth, the satellite sensor reaches a critical detection angle, and if the half cone angle is larger than this angle, the satellite cannot detect the target. Then another criterion can be derived from the hypotenuse of the triangle being larger than the cathetus:
(2) Perspective model with side swing angle
Because the sensors of some satellites for observing loads by radars have yaw angles during observation, the models are different when the visibility is judged, and improvement is carried out on the basis of an optical satellite visibility model. For the satellite-borne SAR, the instantaneous imaging is in an elliptical shape, and considering that the height of the instantaneous imaging is hundreds of kilometers from the ground and the beam width is 1-5 degrees, the instantaneous imaging range can be simplified into a circumscribed rectangle of the ellipse, as shown in FIG. 9.
First, the yaw angle θ can be defined as negative when the lateral view direction and the orbital angular momentum
When the two are on the same side, the two are positive,otherwise, it is negative, i.e
After determining whether the target point is in the side view direction, as shown in fig. 10, the yaw angle is θ, the beam width Δ θ, and the target point for the coverage area
In the first two steps, the ground target is determined to be in a semi-ring shape, and after the coverage area is simplified into a circumscribed rectangle, whether the target is in the rectangular area needs to be determined. As shown in fig. 10 and 11, a plane SOP is defined: the satellite-ground connection SO is within the SOP, and the SOP is perpendicular to the orbit plane. The normal vector of the plane SOP can be obtained
The angle Δ α of the ground target point P1 from the plane SOP should be less than Δ θ, i.e., with the satellite as the origin, i.e., the angle Δ α should be less than Δ θ
The use of this model requires geodetic rectangular coordinates of the local world time and the location of the work party. And the visibility analysis result, namely the time of the satellite observing the ground can be obtained through the calculation of the model.
Step 600: according to the observation time of the satellite on the ground and the time of the motorcade reaching each node in the driving path, adjusting the motorcade entering the assembly ground waiting at the node of the path or reaching the working ground before the observation time based on a satellite evasion algorithm, adjusting the driving parameters of the motorcade in the driving path, and optimizing the driving path. The parameters of fleet travel include departure time of the fleet, travel speed, and the waiting time of the staging area at each pathway node.
The motorcade needs to avoid satellite observation when maneuvering on a task path, and the motorcade is dangerous to be observed by the satellite on a highway, so that a gathering ground needs to be arranged at the position of a path node along the road, and the motorcade enters the gathering ground for gathering when necessary, so that the task is successfully executed. When the gathering area is set, the driving path and the surrounding geographical environment are observed in advance, and places where nodes are located and convenient to find, such as villages or towns, are selected as much as possible.
Since there are many situations that need to be considered in the observation of a driving area by a satellite, all the possible situations need to be analyzed. First, let t be the observation time of the satellite to a certain place, t i And the time when the working group reaches each node when the task is normally executed is shown (wherein i is the serial number of the node, i is more than or equal to 2 and less than or equal to k, and k is the serial number of the workplace). Then the fleet crew may travel the route in several situations:
1)t＝t 1
when t = t 1 When the satellite is used, the departure point is observed by the satellite, and the departure time needs to be advanced or delayed when the satellite is used, but the maneuvering speed needs to be increased. The schematic diagram of the starting point of satellite observation is shown in fig. 12. The adjustment flow of the driving parameters is shown in fig. 13, and the steps are as follows:
step1 satellite observation starting point t = t 1 The departure time is advanced or delayed. If the Step2 is advanced, calculating Step 2; if so, the Step3 calculation is performed.
Step2, the departure time is advanced, so that the departure time t = t-1 (min), and then the visibility is checked through a satellite visibility model. If it is visible, continue Step2, if not, proceed to Step 4.
Step3 delays the departure time, let the departure time t = t +1 (min), and then checks the visibility through the satellite visibility model. If it is visible, step3 is continued, and if not, step5 is calculated.
Step4, the departure time is estimated, and the waiting time at the aggregation site is calculated.
Step5 estimates the traveling speed of the next link.
2)t＝t i
When t = t i When the time comes, it shows that the satellite will observe when reaching the gathering place, and if the time comes, it needs to change the speed to reach the gathering place in advance, and after some time of gathering, it will continue to execute according to the original plan, or the time is advanced, and the last gathering place will wait for some time. Fig. 14 is a schematic view of a satellite observation set map, and fig. 15 is a flow chart of adjusting driving parameters, which includes the following steps:
step1 satellite observes t = t at the time of arrival at the rendezvous point i It is determined whether to wait at the last aggregation site. If waiting, calculating Step 2; if not, the Step5 calculation is performed.
Step2, reversely deducing the time of reaching the last ground array according to the time, judging whether the road section is observed or not, and if so, entering a corresponding decision flow according to four decision conditions; if not, step3 is calculated.
Step3, the time t = t-1 (min) of arriving at the last aggregation ground, the visibility is checked through a satellite visibility model, if the visibility is visible, step3 is continued, and if the visibility is not visible, step4 is calculated.
Step4 deduces the time of arrival at two aggregation sites and calculates the waiting time at the aggregation site.
Step5, reversely deducing the time for reaching the previous aggregation area according to the time, judging whether the road section is observed or not, and if so, entering a corresponding decision flow according to four decision conditions; if not, step6 is calculated.
Step6, judging whether the traveling speed v = v +5 (km/h) exceeds the highway speed limit, and if so, selecting the previous aggregation area to perform aggregation. If not, calculating Step 7.
And Step7, checking visibility through the satellite visibility model, and if the visibility is visible, entering a corresponding decision flow according to four decision conditions. If not, the aggregate latency is calculated and executed according to the calculated scheme.
3)t i-1 ＜t＜t i
When t is i-1 ＜t＜t i Time, indicates from the previous cluster to the next clusterThe satellites will make observations on the stretch of land, in which case two cases should be considered.
In the first case: the observed path is the last segment of the task path, i = k, and the work team is about to arrive at the workplace. It is not possible to wait at the staging area and to arrive at the workplace in advance. Therefore, it is necessary to adjust the time to reach the previous aggregation site and adjust the traveling speed, and if the adjustment fails to satisfy the condition of arriving at the workplace on time, it is necessary to report to the upper stage and change the traveling plan.
In the second case: the observed path is not the last segment of the task path, i.e., i < k. When such a request is met, the departure time of the previous rendezvous point is advanced, and the next rendezvous point is waited for, and at this time, a schematic diagram of the path between the satellite observation rendezvous points is shown in fig. 16, and a flow of adjusting the driving parameters is shown in fig. 17, and the method includes the following steps:
step1 satellite makes observation t on the road section between arrival gathering areas i-1 ＜t＜t i And judging whether the road is the last section of road or not. If the road is the last road section, calculating Step2, and if not, calculating Step 4.
Step2, reversely deducing the waiting time for reaching the previous ground, enabling the time t = t-5 (min) for reaching the previous ground, and calculating the running speed according to the time which is 5min more than the time before. And then determine whether the highway speed limit is exceeded. If so, report to the upper stage and reformulate the solution. If not, step3 is calculated.
And Step3, obtaining a new real-time position of the maneuvering of the working team according to the new maneuvering speed, checking the visibility through a satellite visibility model, if the visibility is visible, continuing Step2, and if the visibility is invisible, outputting the driving speed.
Step4, reversely deducing the time for reaching the last rendezvous area according to the time, enabling the time t = t-1 (min) for reaching the last rendezvous area, and checking the visibility through a satellite visibility model. If it is visible, step4 is continued, and if not, step5 is calculated.
Step5, calculating the waiting time required for reaching the next gathering place, and executing the next driving scheme according to the plan.
4)t＝t k
When t = t k When the work team moves to the workplace, the workplace is observed, and the situation is reported to the superior and the scheme is adjusted if the workplace does not accord with the normal execution condition of the task. The satellite observation work field is shown schematically in fig. 18.
Based on the above scheme, the present invention further provides a fleet path optimization system, including:
the driving path planning module is used for planning the path of a fleet for executing tasks based on an A-x algorithm to obtain the driving path of the fleet; the driving path is the shortest path from the starting point to the work place for executing the task of the motorcade, and comprises a plurality of nodes, wherein the plurality of nodes comprise the starting point, the work place and a plurality of path nodes.
And the real-time position determining module is used for determining the real-time position of the motorcade when the motorcade runs according to the running path based on the real-time position model of the motorcade running.
And the departure time determining module is used for determining the departure time of the motorcade based on the requirement of the motorcade for executing the task.
And the arrival time determining module is used for determining the time of the fleet reaching each node in the running path based on the departure time of the fleet and the real-time position of the fleet running according to the running path.
And the observation time determining module is used for determining the observation time of the satellite on the ground area of the motorcade for executing the task based on the perspective model of the satellite on the ground target point.
The motorcade path optimization module is used for adjusting the staging waiting at the motorcade entry node at the observation time or the working place before the observation time based on a satellite evasion algorithm according to the observation time of the satellite on the ground and the time of the motorcade reaching each node in the running path, adjusting the running parameters of the motorcade in the running path and optimizing the running path; the parameters of the fleet travel include a departure time, a travel speed, and a wait time to aggregate at each access node of the fleet.
As a specific embodiment, in the fleet path optimization system of the present invention, the driving path planning module specifically includes:
the road network acquisition unit is used for acquiring a road network of a ground area where the motorcade executes tasks; the road network comprises a plurality of road nodes and road information.
And the path planning unit is used for planning the path of the fleet for executing tasks by adopting an A-star algorithm based on the road network, the starting point and the working place to obtain the running path of the fleet.
As a specific embodiment, in the fleet path optimization system of the present invention, the fleet path optimization module specifically includes:
a first adjusting unit for adjusting the observation time t of the satellite to the ground and the departure time t of the motorcade 1 Satisfy t 1 When the vehicle belongs to t, advancing or delaying the departure time of the vehicle team, and adjusting the driving speed of the vehicle team after departure.
A second adjusting unit for adjusting the time t when the satellite observes the ground and the time t when the fleet reaches the ith node in the driving path i Satisfy t i When the vehicle belongs to t, the running speed of the fleet is improved, and the vehicle arrives at the aggregation place at the ith node in advance to wait; or wait at a previous staging area; i is more than 1 and less than k, and k is the number of the nodes on the driving path.
A third adjusting unit, configured to adjust the time t when the satellite observes the ground and the time t when the fleet reaches the ith node in the travel path i And time t of the i-1 st node i-1 Satisfy t i-1 ＜t＜t i And the departure time of the previous aggregation ground is advanced, and the aggregation ground at the ith node waits.
A fourth adjusting unit, for adjusting the observation time t of the satellite on the ground and the arrival time t of the fleet k Satisfy t k-1 ＜t＜t k Adjusting the time of the motorcade reaching the k-1 node in the driving path, wherein the k-1 nodeAnd (4) waiting at the aggregation ground and adjusting the driving speed after the k-1 node.
A reporting unit, configured to report the time t of the satellite observing the ground and the time t of the fleet k Satisfy t = t k And determining the execution conditions which do not accord with the execution task of the motorcade, and adjusting the execution task of the motorcade.
As a specific embodiment, in the fleet path optimization system of the present invention, the second adjusting unit specifically includes:
a first judging subunit, configured to judge, when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the driving path are the same i Satisfy t = t i And judging whether the running time of the motorcade from the previous node to the ith node is crossed with the observation time of the satellite on the ground.
And the return subunit is used for returning to the third adjusting unit if the driving time of the fleet from the previous node to the ith node is crossed at the observation time of the satellite on the ground.
And the second judging subunit is used for judging whether the motorcade waits at the gathering place of the previous node or not if the running time from the previous node to the ith node does not intersect with the observation time of the satellite on the ground.
The first adjusting subunit is configured to, if the fleet waits at the rendezvous point of the previous node, increase the traveling speed of the fleet, advance the arrival time of the fleet at the previous node, arrive at the ith node before the observation time, and wait at the rendezvous point of the ith node.
And the third judging subunit is used for improving the running speed of the motorcade according to the standard that the motorcade reaches the ith node from the previous node before the observation time if the motorcade does not wait at the aggregation place of the previous node, and judging whether the improved running speed exceeds the speed limit.
A second adjustment subunit for waiting at the consolidation area at the previous node if the increased travel speed exceeds the speed limit.
And the third adjusting subunit is used for driving from the previous node to the ith node according to the increased driving speed and waiting at the aggregation place of the ith node if the increased driving speed does not exceed the speed limit.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. A fleet path optimization method, comprising:
planning a route of a fleet for executing tasks based on an A algorithm to obtain a running route of the fleet; the driving path is the shortest path from a starting point to a working place for executing a task of the motorcade, and comprises a plurality of nodes, wherein the plurality of nodes comprise the starting point, the working place and a plurality of path nodes;
determining a real-time position of the motorcade when the motorcade runs according to the running path based on a real-time position model of the motorcade running;
determining a departure time of the fleet of vehicles based on requirements of the fleet of vehicles to perform tasks;
determining the time of the fleet to reach each node in the driving path based on the departure time of the fleet and the real-time position of the fleet when the fleet drives according to the driving path;
determining the observation time of a satellite on a ground area of a motorcade for executing tasks based on a perspective model of the satellite on a ground target point;
according to the observation time of the satellite to the ground and the time of the fleet reaching each node in the running path, adjusting a gathering ground waiting at the nodes of the fleet access at the observation time or reaching the working ground before the observation time based on a satellite avoidance algorithm, adjusting the running parameters of the fleet in the running path, and optimizing the running path; the parameters of the fleet travel include a departure time, a travel speed, and a waiting time for staging at each access node of the fleet;
the method for determining the ground observation time of the satellite based on the perspective model of the satellite to the ground target point specifically comprises the following steps:
determining a through-view model of the satellite to a ground target point according to the type of the satellite; the perspective model of the satellite to the ground target point comprises a cone perspective model without a yaw angle and a perspective model with a yaw angle;
and determining the observation time of the satellite on the ground area of the motorcade for executing tasks based on the through-view model of the satellite on the ground target point.
2. The method according to claim 1, wherein the planning of the route of the fleet for executing the task based on the a-algorithm to obtain the driving route of the fleet comprises:
acquiring a road network of a ground area where the motorcade executes tasks; the road network comprises a plurality of road nodes and road information;
and planning the path of the fleet for executing tasks by adopting an A-star algorithm based on the road network, the starting point and the working place to obtain the running path of the fleet.
3. The method for optimizing the route of the fleet of claim 1, wherein said optimizing the route of travel according to the observation time of the satellite on the ground and the arrival time of the fleet at each node in the route of travel is based on a satellite avoidance algorithm, adjusting the fleet access to a staging wait at a node in the route of travel at the observation time or to the worksite before the observation time, adjusting parameters of travel of the fleet in the route of travel, and specifically comprises:
when the observation time t of the satellite to the ground and the departure time t of the motorcade are obtained 1 Satisfy t 1 When the vehicle moves to t, advancing or delaying the departure time of the vehicle team, and adjusting the driving speed of the vehicle after the vehicle team departs;
when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the driving path i Satisfy t i When the vehicle belongs to t, the running speed of the fleet is improved, and the vehicle arrives at the aggregation place at the ith node in advance to wait; or wait at a previous staging area; i is more than 1 and less than k, and k is the number of nodes on the driving path;
when the observation time t of the satellite to the ground and the time t of the fleet reaching the ith node in the driving path are obtained i And time t of the i-1 st node i-1 Satisfy t i-1 ＜t＜t i When the node I is started, the starting time of the previous node I is advanced, and the node I waits for the node I;
when the satellite earth observation time t and the fleet arrival time t k Satisfy t k-1 ＜t＜t k And adjusting the time of the fleet reaching the k-1 node in the driving path, waiting at the aggregation ground at the k-1 node, and adjusting the driving speed after the k-1 node.
4. The fleet path optimization method of claim 3, further comprising:
when the observation time t of the satellite to the ground and the arrival time t of the motorcade are obtained k Satisfy t = t k And determining the execution condition which is not in line with the execution task of the motorcade, and adjusting the execution task of the motorcade.
5. The fleet path optimization method of claim 3, wherein said time t observed at said satellite to ground and said time t of said fleet to reach an ith node in said travel path are determined by a time t i Satisfy t = t i When the vehicle arrives at the gathering place of the ith node in advance, the running speed of the vehicle fleet is improved, and the vehicle fleet arrives at the gathering place of the ith node in advance to wait; or waiting at the previous aggregation site, specifically comprising:
when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the driving path i Satisfy t = t i Judging whether the driving time of the motorcade from a previous node to an ith node is crossed with the observation time of the satellite on the ground or not;
if the travel time of the motorcade from the previous node to the ith node is crossed at the observation time of the satellite on the ground, returning the observation time t of the satellite on the ground and the time t of the motorcade reaching the ith node in the travel path i And time t of the i-1 st node i-1 Satisfy t i-1 ＜t＜t i Advancing the departure time of the previous aggregation place, and waiting for the aggregation place at the ith node;
if the traveling time of the motorcade from the previous node to the ith node is not crossed in the observation time of the satellite on the ground, judging whether the motorcade waits at the assembly place of the previous node or not;
if the motorcade waits at the gathering place of the previous node, improving the running speed of the motorcade, advancing the time of the motorcade arriving at the previous node, arriving at the ith node before the observation time, and waiting at the gathering place of the ith node;
if the fleet does not wait at the gathering place of the previous node, improving the running speed of the fleet according to the standard that the fleet reaches the ith node from the previous node before the observation time, and judging whether the improved running speed exceeds the speed limit;
if the increased driving speed exceeds the speed limit, waiting at the aggregation place at the previous node;
and if the increased running speed does not exceed the speed limit, running from the previous node to the ith node according to the increased running speed, and waiting at the aggregation place of the ith node.
6. A fleet path optimization system, comprising:
the driving path planning module is used for planning the path of a fleet for executing tasks based on an A-x algorithm to obtain the driving path of the fleet; the driving path is the shortest path from a starting point to a working place for executing a task of the motorcade, and comprises a plurality of nodes, wherein the plurality of nodes comprise the starting point, the working place and a plurality of path nodes;
the real-time position determining module is used for determining the real-time position of the motorcade when the motorcade runs according to the running path based on a real-time position model of the motorcade running;
the departure time determining module is used for determining the departure time of the motorcade based on the requirement of the motorcade for executing the task;
the arrival time determining module is used for determining the time of the fleet reaching each node in the driving path based on the departure time of the fleet and the real-time position of the fleet when the fleet drives according to the driving path;
the observation time determining module is used for determining the observation time of the satellite on the ground area of the motorcade for executing tasks based on the perspective model of the satellite on the ground target point; the method specifically comprises the following steps: determining a through-view model of the satellite to a ground target point according to the type of the satellite; the perspective model of the satellite to the ground target point comprises a cone perspective model without a yaw angle and a perspective model with a yaw angle; determining the observation time of the satellite on the ground area of the motorcade for executing tasks based on the visibility model of the satellite on the ground target point;
the motorcade path optimization module is used for adjusting the staging waiting at the motorcade entry node at the observation time or the working place before the observation time based on a satellite evasion algorithm according to the observation time of the satellite on the ground and the time of the motorcade reaching each node in the running path, adjusting the running parameters of the motorcade in the running path and optimizing the running path; the fleet travel parameters include a departure time, a travel speed, and a wait time for staging at each pathway node of the fleet.
7. The fleet path optimization system of claim 6, wherein said travel path planning module comprises:
the road network acquisition unit is used for acquiring a road network of a ground area where the motorcade executes tasks; the road network comprises a plurality of road nodes and road information;
and the path planning unit is used for planning the path of the motorcade executing tasks by adopting an A-star algorithm based on the road network, the departure point and the working place to obtain the running path of the motorcade.
8. The fleet path optimization system of claim 6, wherein said fleet path optimization module comprises:
a first adjusting unit, for adjusting the satellite observation time t to the ground and the departure time t of the motorcade 1 Satisfy t 1 When the vehicle belongs to t, advancing or delaying the departure time of the vehicle team, and adjusting the driving speed of the vehicle team after departure;
a second adjusting unit for adjusting the time t when the satellite observes the ground and the time t when the fleet reaches the ith node in the driving path i Satisfy t i When the vehicle belongs to t, the running speed of the fleet is improved, and the vehicle arrives at the aggregation place at the ith node in advance to wait; or wait at a previous staging area; i is more than 1 and less than k, and k is the number of nodes on the driving path;
a third adjusting unit, configured to adjust the time t when the satellite observes the ground and the time t when the fleet reaches the ith node in the travel path i And time t of the i-1 st node i-1 Satisfy the requirement oft i-1 ＜t＜t i When the node is in the I-th node, the starting time of the previous node is advanced, and the node waits at the I-th node;
a fourth adjusting unit, for adjusting the observation time t of the satellite on the ground and the arrival time t of the fleet k Satisfy t k-1 ＜t＜t k Adjusting the time of the motorcade reaching the (k-1) th node in the driving path, waiting at the aggregation place at the (k-1) th node, and adjusting the driving speed after the (k-1) th node;
a reporting unit, configured to report the time t of the satellite observing the ground and the time t of the fleet k Satisfy t = t k And determining the execution condition which is not in line with the execution task of the motorcade, and adjusting the execution task of the motorcade.
9. The fleet path optimization system of claim 8, wherein said second tuning unit comprises:
a first judging subunit, configured to judge, when the observation time t of the satellite on the ground and the time t of the fleet reaching the ith node in the travel path i Satisfy t = t i Judging whether the driving time of the motorcade from a previous node to an ith node is crossed with the observation time of the satellite on the ground or not;
the return subunit is used for returning to the third adjusting unit if the driving time of the motorcade from the previous node to the ith node is crossed at the observation time of the satellite on the ground;
the second judgment subunit is used for judging whether the motorcade waits at the aggregation ground of the previous node or not if the driving time of the motorcade from the previous node to the ith node is not crossed at the observation time of the satellite on the ground;
a first adjusting subunit, configured to, if the fleet waits at an aggregation site of a previous node, increase a traveling speed of the fleet, advance a time when the fleet arrives at the previous node, arrive at the ith node before the observation time, and wait at the aggregation site of the ith node;
a third judging subunit, configured to, if the fleet does not wait at an aggregation site of a previous node, improve a traveling speed of the fleet according to a criterion that the fleet reaches the ith node from the previous node before the observation time, and judge whether the improved traveling speed exceeds a speed limit;
a second adjustment subunit, configured to wait at the aggregation place at the previous node if the increased traveling speed exceeds the speed limit;
and the third adjusting subunit is used for driving from the previous node to the ith node according to the increased driving speed and waiting at the aggregation place of the ith node if the increased driving speed does not exceed the speed limit.
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| CN113406682A (en) * | 2021-06-22 | 2021-09-17 | 腾讯科技（深圳）有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
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