CN113390414A - Military oil delivery path planning method - Google Patents
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Abstract
One embodiment of the invention discloses a military oil delivery path planning method, which comprises the following steps: s10: calculating the shortest delivery path between the starting point and the end point according to the actual map condition; s20: and planning the oil blending sequence based on the shortest delivery path. The method has a dynamic obstacle avoidance searching function, can dynamically adjust the searching direction of the algorithm as required by reconstructing the weight coefficients with different costs, can expand the searching direction on the basis of improving the searching efficiency, and avoids trapping in a local optimal solution.
Description
Technical Field
The present invention relates to the field of path planning. And more particularly, to a military oil delivery path planning method.
Background
The oil delivery path planning difficulty is increased by the problems of wide amplitude personnel, different battlefield configuration positions, uneven distribution, huge road network, complex road conditions and the like in China. When oil is delivered to multiple delivery points, the allocation sequence and the path planning are carried out by depending on manual experience and in a manual arrangement mode without the participation of a high-level command control system, so that the oil resources cannot be delivered in time according to required time limit. At present, no systematic solution is provided for the military oil distribution problem in China, and most of the solutions focus on solving the problem of planning the shortest path between two points of a single distribution point.
Disclosure of Invention
In view of the above, a first embodiment of the present invention provides a military oil delivery path planning method, including:
s10: calculating the shortest delivery path between the starting point and the end point according to the actual map condition;
s20: and planning the oil blending sequence based on the shortest delivery path.
In a specific embodiment, the S10 includes:
s101: constructing a mathematical model for calculating the shortest path between two points;
s103: constructing a cost function;
s105: and calculating the shortest delivery path by using the mathematical model and the cost function.
In a specific embodiment, the S101 includes:
the map comprises n nodes, and the road distribution diagram is marked as G1＝(V1,A1) Wherein V is1Is a node set, denoted as V1＝{v1 1＝(x1 1,y1 1),...,v1 n＝(x1 n,y1 n) Wherein (x)1 i,y1 i) The coordinate of the vertex i is shown, and the two demand points are respectively marked as v1 startAnd v1 end，A1Representing the set of edges, the distance between vertex j and vertex k is noted
The shortest path is marked as point set P1＝{p1 1,p1 2,...,p1 l}(0<l is less than or equal to n), and the shortest oil delivery path optimization objective function is as follows:
is A1All edges within define a binary variable Oij
Constructing constraint conditions to ensure that the planned path does not pass through a special road section
In a specific embodiment, the S103 includes:
constructing a cost function containing the cost of the current state to the failed road segment,
f(n)＝α×g(n)+β×h(n)+γ×y(n)
wherein g (n) represents an initial state v1 startTo the current state v1 nH (n) represents the current state v1 nTo an end state v1 endY (n) represents the current state v1 nTo all faulty section v1 i(0<i is less than or equal to l); α, β, and γ are weighting coefficients of g (n), h (n), and y (n), respectively, and α + β + γ is 1.
In a specific embodiment, the S105 includes:
s1051: setting initial values alpha, beta and gamma of cost function influence factors;
s1053 generating P1', wherein P1' includes the elements of being in communication with a current road node and OijAll nodes of 1;
s1055: judging whether the cost is unbalanced according to the judgment basis, if so, resetting the values of the cost function influence factors alpha, beta and gamma, otherwise, performing S1057, wherein the judgment basis is as follows: judging whether the cost function satisfies the following formula, if yes, the cost is balanced, otherwise, the cost is unbalanced
S1057: computing a set P1' cost value of each element in P1' minimum cost value selection node v1 2Updating the Path Access sequence P1＝{v1 start,v1 2}；
S1059: repeating S1053-1057 until v is accessed1 endUntil now.
In a specific embodiment, the S20 includes:
s201: constructing an objective function of a transfer sequence plan, comprising the following steps:
denote the network distribution map of m points as G2＝(V2,A2) Wherein the m points include m-1 demand points and 1 distribution station, V2Is a demand point set, denoted as V2＝{v2 1＝(x2 1,y2 1),...,v2 m＝(x2 m,y2 m) P, the scheduling order is denoted as P2＝{p2 1,p2 2,...,p2 mCalculating the shortest distance D between each two points according to the S105ij 1Then the target function of the calling sequence planning is
Will demand point v2 iThe number of accesses is defined as
Constraint condition of 0<i≤m,Ni1 so that each demand point in the scheduling scheme is visited and only once;
s202, representing a scheduling sequence by using genes and chromosomes and generating an initial feasible solution;
s203: establishing a fitness function, and calculating the adaptability of all feasible solutions;
s205: determining chromosomes needing to be reserved, chromosomes formed after crossing and chromosomes formed after mutation;
s207: updating the population until an iteration condition is met;
s209: and arranging the node serial numbers in a descending order according to the node weights to obtain a transfer sequence.
In a specific embodiment, the S202 includes:
the total number of nodes is m, the total number of chromosomes of each generation is GEN, and then the chromosome i is expressed as chromoi＝{a1 i,a2 i,...,am i}, in which the gene value is 0<ak i<1 represents the weight of the node k, and if the gene value is large, the node is accessed preferentially;
the node sequence numbers are arranged according to the gene values on the chromosome in a descending order to obtain an initial feasible solution.
In one embodiment, the fitness function is
Wherein D isi 2For determining the degree of excellence of chromosome i.
In a specific embodiment, the S205 includes:
the composition ratio of the chromosomes to be retained, the chromosomes formed after crossing and the chromosomes formed after mutation is t1,t2,t3The three parameters satisfy the following conditions:
t1+t2+t3＝1
if the fitness of chromosome i is f (x)i) Then the probability that the individual is selected is:
the cumulative probability of this chromosome is:
in [0,1 ]]Randomly generating random number r in interval1Retention of the formula Q (x)k-1)≤r1<Q(xk) The kth chromosome of (1);
two chromosomes can form two new chromosomes through crossing operation, and the crossing points r are randomly set2∈N*,1≤r2M is less than or equal to m. Randomly generating [0,1 ]]Randomly generating random number r in interval3Comparison of r3And cross probability PmIf r is3>PmIf so, the two chromosomes are crossed with each other, otherwise, no operation is performed, and the crossing operation is that the genes after the crossing of the two chromosomes are mutually exchanged to form two new chromosomes.
In a specific embodiment, the S205 further includes:
randomly setting the intersection r4∈N*,1≤r4M or less, randomly generating [0,1 ]]Random number r in interval5Comparison of r5And cross probability PcIf r is5>PcThen mutation operation is performed, i.e., the gene value of the gene locus is changed to [0,1 ]]Random number r in interval6On the contraryNo mutation is performed.
The invention has the following beneficial effects:
the method solves the problem of path planning of oil delivery at multiple delivery points under the condition of a complex network, has a dynamic obstacle avoidance search function by introducing the weight coefficients, can dynamically adjust the search direction as required by reconstructing the weight coefficients with different costs, expands the search direction on the basis of improving the search efficiency, and avoids the algorithm from falling into a local optimal solution. And then calculating the shortest paths between the distribution points and all the supply points and between the two points between the supply points, and then calculating the shortest oil delivery allocation sequence problem based on the total delivery path by using a genetic algorithm based on a priority value, thereby saving more time.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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 based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a military oil delivery path planning method according to an embodiment of the present invention.
FIG. 2 is a flow chart of a military oil delivery route planning method that can implement one embodiment of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, in order to implement the military oil delivery route planning method and system architecture according to an embodiment of the present invention, the system architecture may include a map data set 101 and a server 103. The map data set 101 includes a plurality of road distribution maps including a plurality of nodes, and stores the road distribution map of the planned delivery route and the road distribution map of the unplanned route, respectively, and the server 103 is a server providing various services, such as a background server providing support for calculating a tracking target measurement value.
It should be noted that the map data set 101 may be stored on other devices, on a network, or directly in the server 103, which is not limited in this application.
As shown in fig. 2, a military oil delivery path planning method includes:
s10: calculating the shortest delivery path between the starting point and the end point according to the actual map condition;
s101: constructing a mathematical model for calculating the shortest path between two points;
the road distribution map containing n nodes is marked as G1＝(V1,A1) Node set is denoted as V1＝{v1 1＝(x1 1,y1 1),...,v1 n＝(x1 n,y1 n) In which (x)1 i,y1 i) The coordinate of the vertex i is shown, and the two demand points are respectively marked as v1 startAnd v1 end，A1Representing the set of edges. The distance between vertex j and vertex k is noted as
The shortest path is marked as point set P1＝{p1 1,p1 2,...,p1 l}(0<l is less than or equal to n), and the shortest oil delivery path optimization objective function is as follows:
the problems of road blockage, bridge damage, enemy interference and the like exist in the actual military oil delivery process. In such a case, the relevant link is prohibited from access, a1All edges within define a binary variable OijIf O isijIs 1, the path can pass if OijAt 0, the path may not be passed.
The following constraint conditions are required to be met when the planned path does not pass through a special road section:
s103: constructing a cost function;
constructing an initial cost function as f (n) ═ g (n) + h (n), f (n) representing the actual cost from the initial state through state n, g (n) the actual cost from the initial state to state n in the state space, and h (n) the estimated cost from state n to the target state. In order to solve the problem that the cost from the current state to the fault road section is increased in the initial cost function by forbidding the access to the road section in the actual delivery process, weight coefficients are added into the three types of cost, the solution space priority search direction is changed by adjusting the size of the weight coefficients, and finally the cost function containing the cost from the current state to the fault road section is obtained,
f(n)＝α×g(n)+β×h(n)+γ×y(n)
wherein g (n) represents an initial state v1 startTo the current state v1 nH (n) represents the current state v1 nTo an end state v1 endY (n) represents the current state v1 nTo all faultsSection of road v1 i(0<i is less than or equal to l); α, β, and γ are weighting coefficients of g (n), h (n), and y (n), respectively, and α + β + γ is 1.
By introducing y (n) cost factors, the method has a function of dynamically avoiding obstacles in the process of searching for the optimal path. The cost function is composed of 3 factors, if the cost value of a certain factor is too small/too large, the cost function is unbalanced, the search is continued towards the direction, and the local optimal solution is trapped. The invention provides a method for adjusting the search direction of the algorithm, expanding the search range and avoiding the generation of local optimal solution by dynamically planning the values of alpha, beta and gamma.
S105: and calculating the shortest delivery path by using the mathematical model and the cost function.
S1051: setting initial values alpha, beta and gamma of cost function influence factors;
s1053 generating P1', wherein P1' includes the elements of being in communication with a current road node and OijAll nodes of 1;
s1055: judging whether the cost is unbalanced according to the judgment basis, if so, resetting the values of the cost function influence factors alpha, beta and gamma, otherwise, performing S1057, wherein the judgment basis is as follows: judging whether the cost function satisfies the following formula, if yes, the cost is balanced, otherwise, the cost is unbalanced
S1057: computing a set P1' cost value of each element in P1' minimum cost value selection node v1 2Updating the Path Access sequence P1＝{v1 start,v1 2}；
S1059: repeating S1053-1057 until v is accessed1 endUntil now.
S20: and planning the oil blending sequence based on the shortest delivery path.
S201: constructing an objective function for a call sequence plan
Two pointsAfter the shortest delivery route planning is completed, the demand point allocation sequence planning needs to be carried out by taking the shortest total path as a target. Denote the network profile comprising m points as G2＝(V2,A2) Wherein the m points include m-1 demand points and 1 distribution station, V2Is a demand point set, denoted as V2＝{v2 1＝(x2 1,y2 1),...,v2 m＝(x2 m,y2 m) P, the scheduling order is denoted as P2＝{p2 1,p2 2,...,p2 mAnd the target function of the calling and dialing sequence planning is
Wherein D isij 1For the shortest distance of each two points calculated in S105,
will demand point v2 iThe number of accesses is defined as
Constraint condition of 0<i≤m,Ni1, such that each demand point in the scheduling scheme is accessed and only accessed once,
s202, representing a scheduling sequence by using genes and chromosomes and generating an initial feasible solution;
the process of generating a feasible solution to the problem of transpose order scheduling and representing it in terms of genes and chromosomes is called chromosome coding. The total number of nodes is m, the total number of chromosomes of each generation is GEN, and then the chromosome i is expressed as chromoi＝{a1 i,a2 i,...,am i}, in which the gene value is 0<ak i<1 represents the weight of the node k, and if the gene value is large, the node is accessed preferentially
Arranging the node serial numbers in descending order according to the gene values on the chromosome, namely the weight of a certain node, and obtaining a transfer sequence;
s203: establishing a fitness function, and calculating the adaptability of all feasible solutions;
the goodness of the individual can be calculated by a fitness function, the individual with higher fitness has higher retention probability, the gene can be continuously inherited in the population, and the goodness of the chromosome i is calculated by the advancing distance D of the allocation sequencei 2Determining a fitness function f (x) as:
s205: determining chromosomes needing to be reserved, chromosomes formed after crossing and chromosomes formed after mutation;
the calculation method based on genetics is a global search calculation method proposed by imitating the biological evolution theory in nature. Each feasible solution of the optimization problem corresponds to a chromosome, individuals with high fitness are reserved through operations such as crossing, variation and selection, a new population is generated, and the optimal solution of the optimization problem is approached through continuous iteration. The new population chromosomes respectively have the proportion of t formed by crossing, variation and selection1,t2,t3The three parameters satisfy the following conditions:
t1+t2+t3＝1
whether the individual is retained or not is determined by the method of rotation betting, if the fitness of the chromosome i is f (x)i) Then the probability that the individual is selected is:
the cumulative probability of this chromosome is:
in [0,1 ]]Randomly generating random number r in interval1Retention of the formula Q (x)k-1)≤r1<Q(xk) The kth chromosome of (1);
by adopting a single-point crossover operator, two chromosomes can form two new chromosomes through crossover operation. Randomly setting the intersection r2∈N*,1≤r2M is less than or equal to m. Randomly generating [0,1 ]]Randomly generating random number r in interval3Comparison of r3And cross probability PmIf r is3>PmIf so, the two chromosomes are crossed with each other, otherwise, no operation is performed. Performing cross operation, namely performing mutual exchange of genes behind the cross point of the two chromosomes to form two new chromosomes;
randomly setting the intersection r4∈N*,1≤r4M or less, randomly generating [0,1 ]]Random number r in interval5Comparison of r5And cross probability PcIf r is5>PcThen mutation operation is performed, i.e., the gene value of the gene locus is changed to [0,1 ]]Random number r in interval6Otherwise, no mutation is performed.
S207: updating the population into a selected reserved chromosome, a crossed chromosome and a mutated chromosome until an iteration condition is met;
s209: and arranging the node serial numbers in a descending order according to the node weights to obtain a transfer sequence.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (10)
1. A military oil delivery path planning method is characterized by comprising the following steps:
s10: calculating the shortest delivery path between the starting point and the end point according to the actual map condition;
s20: and planning the oil blending sequence based on the shortest delivery path.
2. The method according to claim 1, wherein the S10 includes:
s101: constructing a mathematical model for calculating the shortest path between two points;
s103: constructing a cost function;
s105: and calculating the shortest delivery path by using the mathematical model and the cost function.
3. The method according to claim 1, wherein the S101 comprises:
the map comprises n nodes, and the road distribution diagram is marked as G1＝(V1,A1) Wherein V is1Is a node set, denoted as V1＝{v1 1＝(x1 1,y1 1),...,v1 n＝(x1 n,y1 n) Wherein (x)1 i,y1 i) The coordinate of the vertex i is shown, and the two demand points are respectively marked as v1 startAnd v1 end，A1Representing the set of edges, the distance between vertex j and vertex k is noted
The shortest path is marked as point set P1＝{p1 1,p1 2,...,p1 lAnd (l is more than 0 and less than or equal to n), the shortest oil delivery path optimization objective function is as follows:
is A1All edges within define a binary variable Oij
Constructing constraint conditions to ensure that the planned path does not pass through a special road section
4. The method of claim 3, wherein the S103 comprises:
constructing a cost function containing the cost of the current state to the failed road segment,
f(n)＝α×g(n)+β×h(n)+γ×y(n)
wherein g (n) represents an initial state v1 startTo the current state v1 nH (n) represents the current state v1 nTo an end state v1 endY (n) represents the current state v1 nTo all faulty section v1 i(i is more than 0 and less than or equal to l); α, β, and γ are weighting coefficients of g (n), h (n), and y (n), respectively, and α + β + γ is 1.
5. The method according to claim 1, wherein the S105 comprises:
s1051: setting initial values alpha, beta and gamma of cost function influence factors;
s1053 generating P1', wherein P1' includes the elements of being in communication with a current road node and OijAll nodes of 1;
s1055: judging whether the cost is unbalanced according to the judgment basis, if so, resetting the values of the cost function influence factors alpha, beta and gamma, otherwise, performing S1057, wherein the judgment basis is as follows: judging whether the cost function satisfies the following formula, if yes, the cost is balanced, otherwise, the cost is unbalanced
S1057: computing a set P1' cost value of each element in P1' minimum cost value selection node v1 2Updating the Path Access sequence P1＝{v1 start,v1 2}；
S1059: repeating S1053-1057 until v is accessed1 endUntil now.
6. The method according to claim 1, wherein the S20 includes:
s201: constructing an objective function of a transfer sequence plan, comprising the following steps:
denote the network distribution map of m points as G2＝(V2,A2) Wherein the m points include m-1 demand points and 1 distribution station, V2Is a demand point set, denoted as V2＝{v2 1＝(x2 1,y2 1),...,v2 m＝(x2 m,y2 m) P, the scheduling order is denoted as P2＝{p2 1,p2 2,...,p2 mCalculating the shortest distance D between each two points according to the S105ij 1Then the target function of the calling sequence planning is
Will demand point v2 iThe number of accesses is defined as
The constraint condition is that i is more than 0 and less than or equal to m, Ni1 so that each demand point in the scheduling scheme is visited and only once;
s202, representing a scheduling sequence by using genes and chromosomes and generating an initial feasible solution;
s203: establishing a fitness function, and calculating the adaptability of all feasible solutions;
s205: determining chromosomes needing to be reserved, chromosomes formed after crossing and chromosomes formed after mutation;
s207: updating the population until an iteration condition is met;
s209: and arranging the node serial numbers in a descending order according to the node weights to obtain a transfer sequence.
7. The method according to claim 6, wherein the S202 comprises:
the total number of nodes is m, the total number of chromosomes of each generation is GEN, and then the chromosome i is expressed as chromoi＝{a1 i,a2 i,...,am iIn which the gene value 0 < ak iIf the weight of the node k is less than 1, if the gene value is large, the node is accessed preferentially;
the node sequence numbers are arranged according to the gene values on the chromosome in a descending order to obtain an initial feasible solution.
8. The method of claim 1, wherein the fitness function is
Wherein D isi 2For determining the degree of excellence of chromosome i.
9. The method according to claim 1, wherein the S205 comprises:
the composition ratio of the chromosomes to be retained, the chromosomes formed after crossing and the chromosomes formed after mutation is t1,t2,t3The three parameters satisfy the following conditions:
t1+t2+t3＝1
if the fitness of chromosome i is f (x)i) Then the probability that the individual is selected is:
the cumulative probability of this chromosome is:
in [0,1 ]]Randomly generating random number r in interval1Retention of the formula Q (x)k-1)≤r1＜Q(xk) The kth chromosome of (1);
two chromosomes can form two new chromosomes through crossing operation, and the crossing points r are randomly set2∈N*,1≤r2M is less than or equal to m. Randomly generating [0,1 ]]Randomly generating random number r in interval3Comparison of r3And cross probability PmIf r is3＞PmIf so, the two chromosomes are crossed with each other, otherwise, no operation is performed, and the crossing operation is that the genes after the crossing of the two chromosomes are mutually exchanged to form two new chromosomes.
10. The method according to claim 1, wherein the S205 further comprises:
randomly setting the intersection r4∈N*,1≤r4M or less, randomly generating [0,1 ]]Random number r in interval5Comparison of r5And cross probability PcIf r is5＞PcThen perform mutation operationThen, the gene value of the gene locus is changed to [0,1 ]]Random number r in interval6Otherwise, no mutation is performed.
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