CN111859591A - Automatic wire and cable layout method and device - Google Patents
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Abstract
The application discloses a method and a device for automatically arranging wires and cables. The method comprises the following steps: inputting a structural part model and an electrical connection relation; constructing a path network in the structural part model based on a random path diagram algorithm; aiming at the path network, designing a branch structure based on a genetic algorithm to obtain the positions of a branch point and a terminal; designing a central curve formed by discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals, and performing curve fitting; and outputting the layout result of the wires and the cables. The apparatus, comprising: the system comprises an input module, a point network construction module, a branch structure design module, a center curve module and an output module. The method solves the problems that the limiting conditions of the wire cable in engineering application are not considered and the layout design of the branch wire cable cannot be completely processed in the related art.
Description
Technical Field
The application relates to the technical field of electric wire and cable layout design, in particular to an automatic electric wire and cable layout method and device.
Background
The electric wire and cable are used as a medium for transmitting electric energy and electronic signals in electromechanical products, and are widely used in complex electromechanical products. Therefore, the quality of the wire and cable has a great influence on the operation of the electromechanical product. The quality of the wire and cable is related to a number of factors, where the layout of the wire and cable has a direct effect on the operating conditions of the wire and cable. Nowadays, the layout design of the electric wire and the electric cable is basically dependent on manual experience, which consumes a lot of manpower and material resources and takes a lot of time.
At present, in order to reduce the manual workload of the layout design of the electric wires and cables, a large number of methods for automatically designing the layout of the electric wires and cables are researched at home and abroad. On one hand, the efficiency of layout design is improved by optimizing the wiring process and quickly defining the logical relationship of the wires and the cables; on the other hand, considering the layout design of the electric wires and cables as a motion path search problem, it is desirable to automatically calculate the layout result of the electric wires and cables with a computer. In the foreign world, in 1996, Conru has conducted theoretical level research on the structure and channel design method of branch cables, which is designed to take into account the engineering constraints of the layout design and the interference between the cables and mechanical parts. In 2007, Kabul et al proposed an algorithm for wire and cable path search based on a random path diagram algorithm, and used an adaptive forward dynamics approach to obtain wire and cable layout results that satisfy physical constraints. But this method can only calculate the results of the lay-out of an unbranched wire cable. In 2016, Hermansson et al proposed a wire and cable layout design method that satisfied geometric design constraints, but this method was also only applicable to non-branch wires and cables. In the aspect of China, the Liu Chong Hua team of Beijing university of science and technology has proposed a plurality of layout design methods of electric wires and cables based on random sampling motion path search, however, the processing capacity of the method for branch electric wires and cables is limited, and when the number of branches of the electric wires and cables is too large, the method cannot obtain good layout results.
In summary, although the current research results and methods at home and abroad carry out a certain degree of research on the layout design of the electric wires and cables, the research results and methods still have the aspect of insufficient consideration. On the one hand, the constraints of the wire and cable in engineering applications are not considered; on the other hand, the layout design of the branch electric wire cable cannot be completely handled. Therefore, these methods are difficult to practice in engineering.
In the related art, no effective solution is proposed at present for the problem that the limitation condition of the wire cable in the engineering application is not considered and the layout design of the branch wire cable cannot be completely handled.
Disclosure of Invention
The present application provides a method and an apparatus for automatically laying out wires and cables, so as to solve the problems in the related art that the limitations of the wires and cables in engineering applications are not considered and the layout design of branch wires and cables cannot be completely handled.
In order to achieve the above object, in a first aspect, the present application provides an automatic wire and cable layout method.
The specific flow is as follows
Step S1: inputting a structural part model and an electrical connection relation;
step S2: constructing a path network in the structural part model based on a random path diagram algorithm;
step S3: aiming at the path network, designing a branch structure based on a genetic algorithm to obtain the positions of a branch point and a terminal;
step S4: designing a central curve formed by discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals, and performing curve fitting;
step S5: and outputting the layout result of the wires and the cables.
The layout result comprises: the branch structure of the wire and cable, the center line of each branch, and the radius of each branch.
The network of points satisfies both non-interference constraints and adherence constraints;
the non-interference constraint inequality dpt≥dobIs represented by the formula (I) in which dptIndicating the distance between a point on the wire and cable and the surface of the structure, dobRepresenting the minimum distance between a point on the wire and cable and the surface of the structure.
Inequality d for adherence constraintpt≤dsurIs represented by the formula (I) in which dsurRepresenting the maximum distance between a point on the wire and cable and the surface of the structure.
As indicated by the above two constraints, the points on the wire and cable are within a certain distance from the surface of the structural member.
The random path map algorithm comprises the following steps:
step S101: initializing a path graph R-N, E, wherein N is a point in the path graph, E indicates an edge connecting two points in the path graph, i is the iteration number, and K is the termination cycle number;
step S102: a random point qnew is obtained by random sampling.
Step S103: carrying out interference check on the random point qnew;
step S104: if the interference is caused, adding 1 to the iteration number i, entering the next loop, turning to the step S102, and if the interference is not caused, continuing to the step S105.
Step S105: adding the random point qnew into N, finding a point set of which the distance between the random point qnew and the point set N is smaller than a set first step length rho, and connecting each point qnear and qnew in the point set;
step S106: judging whether the connection is successful;
step S107: if the connection is successful, adding the edge qnew-qnear into the E, and turning to the step S108; if the connection is unsuccessful, the step S102 is carried out, and random sampling is carried out again;
step S108: judging the maximum iteration times;
step S109: if the maximum iteration times are reached, outputting a path network; if the maximum iteration number is not reached, the process returns to step S102.
In step S102, the random sampling adopts an obstacle-based uniform sampling strategy, and the flow is as follows:
step S201: initializing a set P of sampling points, the maximum cycle number n, the length l of a line segment used in the sampling process and a second step length delta t.
Step S202: the random sampling obtains a sampling point pt.
Step S203: generating a random direction
And along the direction
And expanding a line segment s with the length of l by taking the sampling point pt as a starting point.
Step S204: and creating a point on the line segment s every length of l/delta t, and storing the points without interference into a set P of sampling points.
Step S205: judging whether the maximum cycle number is reached;
step S206: if the maximum cycle number is reached, outputting a set P of sampling points, adding the set P of sampling points into the path diagram R, and randomly sampling from the set P to obtain a random point qnew; if the maximum cycle count is not reached, the cycle count is increased by 1, the process returns to step S202, and random sampling is performed again.
The design of the branch structure based on the genetic algorithm comprises the following processes:
step S301: encoding the branch cable;
step S302: initializing a population;
step S303: calculating the probability of variation pmAnd cross probability pc；
Step S304: carrying out mutation operation;
step S305: performing cross operation;
step S306: carrying out selection operation;
step S307: judging whether a termination condition is reached;
step S308: if the termination condition is reached, outputting the branch structure; if the termination condition is not reached, go to S302.
The rules for encoding the branch cable are expressed by the following formula:
list{Ti(xi,yi,zi)|Tj,Ek},i＜j (1)
wherein, TiIs the ith branch point, (x)i,yi,zi) As coordinates of the branch point, TjAnd TiBranch points connected to each other with a higher sequence number than i, EkAnd a branch point TiConnected end points, Tj、EkThe number of (3) may be other than 1.
The lower label in the above formula (1) is related to the position of the branch point. When writing the sequence number, the three coordinates should be sorted from small to large, and when encoding, each branch point and end point is allowed to be used only once.
The population is initialized, and the process is as follows:
step S401: n branch points are randomly generated within the boundary of the end point composition. The end points refer to points associated with electrical connectors of the electrical wire cable.
Step S402: finding an end point for each branch point according to distance: every branch point, except the first and last, looks for the nearest end point; and the first branch point and the last branch point need to find the two end points with the nearest distance.
Step S403: according to the formula
The aggregation of the branch points is performed, where N refers to the number of branch points.
Step S404: all the branch points are divided into K point sets in the order of coordinate size, and each point set contains [ N/K ] branch points.
Step S405: and randomly finding a point in each point set to connect with an adjacent point set, wherein the adjacent point set is the sequence number of the branch point.
Step S406: steps S402-S405 are repeated until a predefined maximum number of populations is generated.
The mutation operation comprises the following steps:
according to the variation probability pmJudging whether to perform mutation operation, generating a first random number between 0 and 1, comparing the first random number with the mutation probability, and if the first random number is less than the mutation probability pmIf not, the mutation operation is not performed.
The mutation probability pmThe calculation formula is as follows:
pm(t)＝|exp(f(t)/f(t-1)-1)-1| (2)
wherein t refers to the first generation population; (t) is the fitness value of the tth population; f (t-1) refers to the fitness value of the previous generation population.
The mutation operation comprises three aspects:
first, the number of branch points is changed. Dividing the whole population into three parts; the first part reduces the number of branch points by 1; the second part increases the number of branch points by one; the third portion remains unchanged. Wherein the number of branch points should be 1 to NmaxIs changed from N to NmaxThe size of (2) is the number of wire and cable terminals minus 2. Both the increase and decrease of the branching points are randomly selected.
Second, the position of the branch point is changed. The remaining branch points should be moved to the positions of the adjacent path points in the path diagram. When changing the position of the branch point, it should be ensured that the position of the branch point should not exceed the boundary of the end point composition.
Third, the adjacency list structure is changed. When a branch point is removed, its connected terminal should be connected to a branch point adjacent to the removal branch point. When a branch point is added, the added branch point should be connected to the nearest end point and the branch point. All population individuals should randomly select K branch points, disconnect the branch points from the original branch point, and reconnect the branch points to different branch points.
The cross operation comprises the following processes:
according to the cross probability pcJudging whether to perform cross operation between 0 and 1Second random number, and comparing the second random number with the cross probability, if less than the cross probability pcIf not, the cross operation is not carried out.
The formula of the cross probability is as follows:
pc(t)＝1-pm(t) (3)
the crossing operation comprises four aspects:
firstly, pairing the population individuals with the same quantity of branching points;
secondly, the fitness value of each item in the adjacency list is compared to find an optimal item and a worst item;
thirdly, exchanging corresponding structures in the adjacency list and storing the structures of the two individuals with the minimum fitness values;
fourthly, according to the encoding principle, the structure of the adjacent table is readjusted to generate a new population individual.
The selection operation comprises the following processes:
an elite selection is used for the selection operation. In each iteration process, the population individuals are sorted according to their fitness values, and individuals of the same size as the population are selected to enter the next generation population.
The termination condition is expressed by the following formula:
wherein, nwbiThe ith branch, lbiThe ith branch length; gi＝5+nwbi，giIs the ith fitness;
the termination condition being that the number of iterations reaches a maximum value or
I.e. when the minimum value of the fitness function is close to the mean value.
The method for designing the central curve formed by the discrete points of each branch of the electric wire and cable and performing curve fitting comprises the following steps:
(1) searching out a central line consisting of a group of discrete points in the point network according to the branch structure; adopting a modified Dijkstra's algorithm, wherein the improvement is as follows: will { KSiThe dot cost value in { FS } is set to be half of the two-dot distanceiSetting the cost value of the point to be 10 times of the distance between two points, wherein the cost value of the point not in the two sets is the distance between two points, and the distance between two points is the straight-line distance between the current point and one adjacent point;
and performing curve fitting on the central line. And (5) performing curve fitting on the discrete points by using a B-spline curve, wherein the formula of the B-spline curve is (5). And (3) carrying the discrete points obtained in the step (1) into a step (5), solving the polygonal control points of the B-spline curve by using a numerical solution, and obtaining a curve passing through the group of discrete points according to a formula (5), namely a final central line.
Wherein, Bx,yAnd (u) is a basic function, u is a node on the curve, and x is the serial number y of the node of the x-th node, wherein the highest power of the basic function is the power of y.
In a second aspect, the present application further provides an automatic layout apparatus for electrical cables, which is implemented by an automatic layout method for electrical cables, including: the system comprises an input module, a point network construction module, a branch structure design module, a central curve module and an output module;
the input module, the point network construction module, the branch structure design module, the center curve module and the output module are sequentially connected;
the input module is used for inputting a structural part model and an electrical connection relation;
the point network construction module is used for constructing a point network in the structural part model based on a random path diagram algorithm;
the branch structure design module is used for designing a branch structure based on a genetic algorithm aiming at the point network to obtain the positions of a branch point and a terminal;
the central curve module is used for designing a central curve formed by discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals and carrying out curve fitting;
and the output module outputs the layout result of the wires and the cables.
The beneficial technical effects are as follows:
(1) compared with the prior art, the method and the device can process the wire and cable layout design with a complex structure, namely the wire and cable layout design with a large number of terminals and a complex attached structural part.
(2) The layout design engineering constraint with universality can be met. Simultaneously, compare in prior art, increased such engineering constraint in laying the region, can guarantee that electric wire and cable avoids risk layout area to also enable electric wire and cable overall arrangement to have better result.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
fig. 1 is a flow chart of an automatic layout method for electric wires and cables according to an embodiment of the present application;
FIG. 2 is a flow chart of a random path graph algorithm provided according to an embodiment of the present application;
FIG. 3 is a flow chart of random sampling provided according to an embodiment of the present application;
FIG. 4 is a flow chart of a design of a branching structure based on genetic algorithm provided according to an embodiment of the present application;
FIG. 5 is a flowchart of population initialization provided according to an embodiment of the present application;
FIG. 6 is a diagram illustrating an example of a branch structure provided in accordance with an embodiment of the present application;
FIG. 7 is a schematic diagram of an obstacle-based random sampling process provided in accordance with an embodiment of the present application;
fig. 8 is a schematic diagram of a coding scheme of a wire and cable branching structure provided according to an embodiment of the present application, wherein (a) a lower table ordering showing points in a lead table is shown; (b) is the coding of the cable in figure (a).
Fig. 9 is a schematic diagram of a generation process of an initial population according to an embodiment of the present application, (a) a population initialization state, (b) a population generation intermediate process, (c) a final generation result of the initial population;
fig. 10 is a schematic diagram illustrating an operation process of a crossover operator according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
In addition, the term "plurality" shall mean two as well as more than two.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In a first aspect, the present application provides a method for automatic wire and cable layout.
As shown in fig. 1, the specific process is as follows:
step S1: inputting a structural part model and an electrical connection relation;
step S2: constructing a path network in the structural part model based on a random path diagram algorithm;
step S3: aiming at the path network, designing a branch structure based on a genetic algorithm to obtain the positions of a branch point and a terminal;
step S4: designing a central curve formed by discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals, and performing curve fitting;
step S5: and outputting the layout result of the wires and the cables.
Layout design problem description of electric wire and cable
The main contents of the layout design of the wire and cable include the design of the branch structure and the design of the appearance of the wire and cable. The branch structure is a connection relationship between the terminals of the electric wire and the cable and the branch points. As shown in fig. 6, taking a wire cable of 5 terminals as an example, there may be 7 different branch structures. The profile of the wire cable includes the centerline curve, radius and branching structure of the wire cable.
The branch structure design has the main design content of designing the number and the positions of the branch points of the electric wire and the electric cable and the connection relationship between the branch points and each terminal. The shape design of the wire and the cable is to design the center line curve of each branch on the basis of the known branch structure.
Engineering constraints on the layout design of the wire and cable:
the engineering constraints of the wire and cable layout design to which the application relates include the following 4,
(1) no interference constraint exists, and the wire and the cable do not interfere with the structural part;
(2) the adherence is restrained, and the electric wires and the cables are laid on the surface of the structural part as much as possible;
(3) the minimum bending radius, the bending radius of the center line of the electric wire cable, is not less than a value which is generally set to three times the radius.
(4) Area constraint, wherein structures designed for the layout of the wires and the cables exist in the structural part, and the areas belong to the areas which need to be laid out; there are structures in the structural member that do not allow the layout of the electric wires and cables, and these regions belong to the regions where the layout is prohibited. The zone constraint can be manually set to provide constraint for the trend of the wire and cable layout.
2.3 mathematical description of wire and Cable layout design
In order to better describe the layout design problem of the electric wire and the electric cable, mathematical symbols are introduced for description.
(1) Mathematical description of wire and cable layout design content
The branch structure is expressed by an adjacency list with the mathematical expression of list { Ti|Tj,EkIn which EiShown are the terminals, T, of the branch wire cableiThe branch point of the branch wire cable is shown.
Mathematical expression q: → { list { T } for the wire and cable layout resultsi|Tj,Ek},Ti,CEi,riWhere q is expressed as a wire and cable layout result, CEiIndicated as electric wireCenter line of cable branch, riIndicated as the cross-sectional radius of the wire cable branch.
(2) Mathematical description of layout design engineering constraints for electrical wires and cables
Inequality d for non-interference constraintpt≥dobIs represented by the formula (I) in which dptIndicating the distance between a point on the wire and cable and the surface of the structure, dobRepresenting the minimum distance between a point on the wire and cable and the surface of the structure.
Inequality d for wall-attached constraintpt≤dsurIs represented by the formula (I) in which dsurRepresenting the maximum distance between a point on the wire and cable and the surface of the structure. As indicated by the above two constraints, the points on the wire and cable are within a certain distance from the surface of the structural member.
Minimum bend radius rbendAnd (4) showing. The bend radius anywhere along the wire and cable centerline should be greater than this value.
In the area constraint, the areas allowed to be laid down are represented by the surfaces of the structural member, and these surfaces are represented by { KSiA set representation; the areas of the structure where the application is forbidden are also indicated by the surfaces of the structure, these surfaces being denoted by { FSiThe set representation.
The layout result comprises: the branch structure of the wire and cable, the center line of each branch, and the radius of each branch.
The network of points satisfies both non-interference constraints and adherence constraints;
the non-interference constraint inequality dpt≥dobIs represented by the formula (I) in which dptIndicating the distance between a point on the wire and cable and the surface of the structure, dobRepresenting the minimum distance between a point on the wire and cable and the surface of the structure.
Inequality d for adherence constraintpt≤dsurIs represented by the formula (I) in which dsurRepresenting the maximum distance between a point on the wire and cable and the surface of the structure.
As indicated by the above two constraints, the points on the wire and cable are within a certain distance from the surface of the structural member.
The random path map algorithm, as shown in fig. 2, includes the following steps:
step S101: initializing a path graph R-N, E, wherein N is a point in the path graph, E indicates an edge connecting two points in the path graph, i is the iteration number, and K is the termination cycle number;
step S102: a random point qnew is obtained by random sampling.
Step S103: carrying out interference check on the random point qnew;
step S104: if the interference is caused, adding 1 to the iteration number i, entering the next loop, turning to the step S102, and if the interference is not caused, continuing to the step S105.
Step S105: adding the random point qnew into N, finding a point set in N, wherein the distance between the point set and the random point qnew is smaller than a set step length rho, and connecting each point qnear and qnew in the set;
step S106: judging whether the connection is successful;
step S107: if the connection is successful, adding the edge qnew-qnear into the E, and turning to the step S108; if the connection is unsuccessful, the step S102 is carried out, and random sampling is carried out again;
step S108: judging the maximum iteration times;
step S109: if the maximum iteration times are reached, outputting a path network; if the maximum iteration number is not reached, the process returns to step S102.
In step S102, the random sampling adopts an obstacle-based uniform sampling strategy, as shown in fig. 3, and an example of the random sampling is shown in fig. 7, and the flow is as follows:
step S201: initializing a set P of sampling points, the maximum cycle number n, the length l of a line segment used in the sampling process and a second step length delta t.
Step S202: the random sampling obtains a sampling point pt.
Step S203: generating a random direction
And along the direction
And expanding a line segment s with the length of l by taking the sampling point pt as a starting point.
Step S204: and creating a point on the line segment s every length of l/delta t, and storing the points without interference into a set P of sampling points.
Step S205: judging whether the maximum cycle number is reached;
step S206: if the maximum cycle number is reached, outputting a set P of sampling points, adding the set P of sampling points into the path diagram R, and randomly sampling from the set P to obtain a random point qnew; if the maximum cycle count is not reached, the cycle count is increased by 1, the process returns to step S202, and random sampling is performed again.
The design of the branch structure based on the genetic algorithm is shown in FIG. 4, and the flow is as follows
Step S301: encoding the branch cable;
step S302: population initialization, the schematic diagram of the initial population generation process is shown in fig. 9;
step S303: calculating the probability of variation pmAnd cross probability pc；
Step S304: carrying out mutation operation;
step S305: performing cross operation;
step S306: carrying out selection operation;
step S307: judging whether a termination condition is reached;
step S308: if the termination condition is reached, outputting the branch structure; if the termination condition is not reached, go to S302.
The coding of the wire and cable branching structure design needs to take into account the branching structure of the wire and cable and the location of the branching point. In the traditional genetic algorithm, the encoding modes such as character string encoding, gray encoding and floating point encoding are not suitable for encoding the design of the branch structure. In the application, a wire and cable branching structure design is coded using a specific coding scheme.
The rules for encoding the branch cable, the encoding scheme is shown in fig. 8, (a) shows the following table ordering of the points in the tie-down table; (b) is the coding of the cable in figure (a). Expressed by the following equation:
list{Ti(xi,yi,zi)|Tj,Ek},i＜j (1)
wherein, TiIs the ith branch point, (x)i,yi,zi) As coordinates of the branch point, TjAnd TiBranch points connected to each other with a higher sequence number than i, EkAnd a branch point TiConnected end points, Tj、EkThe number of (3) may be other than 1.
The lower label in the above formula (1) is related to the position of the branch point. When writing the sequence number, the three coordinates should be sorted from small to large, and when encoding, each branch point and end point is allowed to be used only once.
The branch structure design and branch point location determination problem is similar to the Traveling Salesman Problem (TSP). They are both graph search problems, so the population initialization methods of both problems can be handled in a similar way. The k-means clustering strategy can improve the quality of initial population at TSP, so the population initialization clustering strategy can be added.
The population is initialized, and the flow shown in fig. 5 is as follows:
step S401: n branch points are randomly generated within the boundary of the end point composition. The end points refer to points associated with electrical connectors of the electrical wire cable.
Step S402: finding an end point for each branch point according to distance: every branch point, except the first and last, looks for the nearest end point; and the first branch point and the last branch point need to find the two end points with the nearest distance.
Step S403: according to the formula
The aggregation of the branch points is performed, where N refers to the number of branch points.
Step S404: all the branch points are divided into K point sets in the order of coordinate size, and each point set contains [ N/K ] branch points.
Step S405: and randomly finding a point in each point set to connect with an adjacent point set, wherein the adjacent point set is the sequence number of the branch point.
Step S406: steps S402-S405 are repeated until a predefined maximum number of populations is generated. The mutation operation comprises the following steps:
according to the variation probability pmJudging whether to perform mutation operation, generating a first random number between 0 and 1, comparing the first random number with the mutation probability, and if the first random number is less than the mutation probability pmIf not, the mutation operation is not performed.
The crossover operator does not make any improvement to the genetic algorithm in the TSP and vehicle routing problems. For routing problems, mutation operators and selection operators are key parts of genetic algorithms. Based on the above facts, the genetic algorithm of the present invention is different from the process of the classical genetic algorithm. The mutation operation is performed before the crossover operation, with the mutation probability approaching 1 at the beginning and the crossover probability approaching 0. In addition, and as a function of the iterative process, is determined by the quality of each generation of individuals. The adaptive policy based approach may calculate the change in the sum.
The execution of the genetic algorithm is shown in fig. 4. In the genetic algorithm execution process, a mutation operator is executed before a selection operator, the selection operator is executed after the two operators are executed, in each iteration process, the algorithm needs to generate new population individuals through the mutation operator and a crossover operator, and then the population individuals needing to be reserved are selected through the selection operator.
The mutation probability pmThe calculation formula is as follows:
pm(t)＝|exp(f(t)/f(t-1)-1)-1| (2)
wherein t refers to the first generation population; (t) is the fitness value of the tth population; f (t-1) refers to the fitness value of the previous generation population.
The mutation operation comprises three aspects:
first, the number of branch points is changed. Will be the wholeThe population is divided into three parts; the first part reduces the number of branch points by 1; the second part increases the number of branch points by one; the third portion remains unchanged. Wherein the number of branch points should be 1 to NmaxIs changed from N to NmaxThe size of (2) is the number of wire and cable terminals minus 2. Both the increase and decrease of the branching points are randomly selected.
Second, the position of the branch point is changed. The remaining branch points should be moved to the positions of the adjacent path points in the path diagram. When changing the position of the branch point, it should be ensured that the position of the branch point should not exceed the boundary of the end point composition.
Third, the adjacency list structure is changed. When a branch point is removed, its connected terminal should be connected to a branch point adjacent to the removal branch point. When a branch point is added, the added branch point should be connected to the nearest end point and the branch point. All population individuals should randomly select K branch points, disconnect the branch points from the original branch point, and reconnect the branch points to different branch points.
The cross operation, as shown in fig. 10, is as follows:
according to the cross probability pcJudging whether to carry out cross operation, generating a second random number between 0 and 1, comparing the second random number with the cross probability, and if the second random number is less than the cross probability pcIf not, the cross operation is not carried out.
The formula of the cross probability is as follows:
pc(t)＝1-pm(t) (3)
the crossing operation comprises four aspects:
firstly, pairing the population individuals with the same quantity of branching points;
secondly, the fitness value of each item in the adjacency list is compared to find an optimal item and a worst item;
thirdly, exchanging corresponding structures in the adjacency list and storing the structures of the two individuals with the minimum fitness values;
fourthly, according to the encoding principle, the structure of the adjacent table is readjusted to generate a new population individual.
During each iteration, the mutation operator and the crossover operator generate new individuals. At this point, the number of individuals in the population will exceed the size of the population. In order to keep the population size unchanged, the operation of selecting an operator needs to be performed. Classical selection operators include proportional wheel selection, linear sort selection, exponential sort selection, tournament selection, and the like. In particular, elite selection is a practical selection operator for selecting the best elite population from the current generation population into the next generation population. The present invention uses elite selection for operator selection operations. During each iteration, the population individuals are ranked according to their fitness value, with the best individuals of the same number as the population size being selected for entry into the next generation population.
The selection operation comprises the following processes:
an elite selection is used for the selection operation. During each iteration, the population individuals are ranked according to their fitness value, with the best individuals of the same number as the population size being selected for entry into the next generation population.
The termination condition is expressed by the following formula:
nwb thereini＝number of wires in branch i，lbi＝length of branch i；
giUnit fixed value for branch i 5+ nwb; wherein, nwbiThe ith branch, lbiThe ith branch length; gi＝5+nwbi，giIs the ith fitness;
the termination condition being that the number of iterations reaches a maximum value or
I.e. when the minimum value of the fitness function is close to the mean value.
The method for designing the central curve formed by the discrete points of each branch of the electric wire and cable and performing curve fitting comprises the following steps:
searching a group of discrete points in the point network according to the branch structure; using a modified Dijkstra's algorithm for searching for discrete points of the centerline of each branch, wherein the improvement is: setting the cost value of the middle point as a half of the distance between the two points, wherein the distance is a set of key layout surfaces, the key layout surfaces are surfaces through which the electric wire and the cable can pass, the cost value of the points is set to be 10 times of the distance between the two points, the cost value of the points which are not in the two sets is the distance between the two points, the distance between the two points is a straight line distance between the current point and one adjacent point, the distance between the two points is a set of layout-prohibited surfaces, and the layout-prohibited surfaces are surfaces through which the electric wire and the cable can not;
and performing curve fitting on the central line. And (5) performing curve fitting on the discrete points by using a B-spline curve, wherein the formula of the B-spline curve is (5). And (3) carrying the discrete points obtained in the step (1) into a step (5), solving the polygonal control points of the B-spline curve by using a numerical solution, and obtaining a curve passing through the group of discrete points according to a formula (5), namely a final central line.
Wherein, Bx,yAnd (u) is a basic function, u is a node on the curve, and x is the serial number y of the node of the x-th node, wherein the highest power of the basic function is the power of y.
In a second aspect, the present application further provides an automatic layout apparatus for electrical cables, which is implemented by an automatic layout method for electrical cables, including: the system comprises an input module, a point network construction module, a branch structure design module, a central curve module and an output module;
the input module, the point network construction module, the branch structure design module, the center curve module and the output module are sequentially connected;
the input module is used for inputting a structural part model and an electrical connection relation;
the point network construction module is used for constructing a point network in the structural part model based on a random path diagram algorithm;
the branch structure design module is used for designing a branch structure based on a genetic algorithm aiming at the point network to obtain the positions of a branch point and a terminal, and an example diagram of the branch structure is shown in FIG. 6;
the central curve module is used for designing a central curve formed by discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals and carrying out curve fitting;
and the output module outputs the layout result of the wires and the cables.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An automatic layout method of wires and cables is characterized by comprising the following steps:
step S1: inputting a structural part model and an electrical connection relation;
step S2: constructing a path network in the structural part model according to interference-free constraint and adherence constraint based on a random path diagram algorithm;
step S3: analyzing the branch structure based on a genetic algorithm aiming at the path network to obtain the positions of branch points and terminals;
step S4: forming a central curve by discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals, and performing curve fitting;
step S5: and outputting the layout result of the wires and the cables.
2. The method for automatic placement of wires and cables according to claim 1, wherein based on said random path map algorithm, a network of points is constructed in a structure model according to non-interference constraints and adherence constraints by the steps of:
step S101: initializing a path graph R-N, E, wherein N is a point in the path graph, E indicates an edge connecting two points in the path graph, i is the iteration number, and K is the termination cycle number;
step S102: obtaining a random point qnew through random sampling;
step S103: carrying out interference check on the random point qnew;
step S104: if the interference is caused, adding 1 to the iteration number i, entering the next loop, turning to the step S102, and if the interference is not caused, continuing to the step S105;
step S105: adding the random point qnew into N, finding a point set of which the distance between the random point qnew and the point set N is smaller than a set first step length rho, and connecting each point qnear in the point set with qnew;
step S106: judging whether the connection is successful;
step S107: if the connection is successful, adding the edge qnew-qnear into E, turning to the step S108, and if the connection is unsuccessful, turning to the step S102;
step S108: judging the maximum iteration times;
step S109: if the maximum iteration times are reached, outputting a path network; if the maximum iteration number is not reached, the process returns to step S102.
3. The method for automatic layout of electric wires and cables according to claim 2, wherein in step S102, the random sampling employs a uniform sampling strategy based on obstacles, and the procedure is as follows:
step S201: initializing a set P of sampling points, the maximum cycle number n, the length l of a line segment used in the sampling process and a second step length delta t;
step S202: randomly sampling to obtain a sampling point pt, and recording the cycle number;
step S203: generating a random direction
And along the direction
Expanding a line segment s with the length of l by taking the sampling point pt as a starting point;
step S204: creating a point on the line segment s every length of l/delta t, and storing the point which does not interfere into a set P of sampling points;
step S205: judging whether the maximum cycle number is reached;
step S206: if the maximum cycle number is reached, outputting a set P of sampling points, adding the set P of sampling points into the path diagram R, and randomly sampling from the set P to obtain a random point qnew; if the maximum cycle count is not reached, the cycle count is increased by 1, the process returns to step S202, and random sampling is performed again.
4. The method for automatic layout of electric wire and cable according to claim 1, wherein the branch structure is designed based on genetic algorithm as follows:
step S301: encoding the branch cable;
step S302: initializing a population;
step S303: calculating the probability of variation pmAnd cross probability pc；
Step S304: carrying out mutation operation;
step S305: performing cross operation;
step S306: carrying out selection operation;
step S307: judging whether a termination condition is reached;
step S308: if the termination condition is reached, outputting the branch structure; if the termination condition is not reached, go to S302.
5. The method of claim 4, wherein the population is initialized by the following steps:
step S401: randomly generating N branch points in a boundary formed by the end points;
step S402: finding an end point for each branch point according to distance: every branch point, except the first and last, looks for the nearest end point; the first branch point and the last branch point need to find two end points with the nearest distance;
step S403: according to the formula
Performing an aggregation of said branch points, wherein N refers to the number of branch points;
step S404: dividing all branch points into K point sets according to the coordinate size sequence, wherein each point set comprises [ N/K ] branch points;
step S405: randomly finding a point in each point set to connect with an adjacent point set, wherein the adjacent point sets are the branch point serial numbers which are adjacent;
step S406: steps S402-S405 are repeated until a predefined maximum number of populations is generated.
6. The method of claim 4, wherein the mutation operation is performed by the following steps:
according to the variation probability pmJudging whether to perform mutation operation: generating a first random number between 0 and 1, comparing the first random number with the variation probability, and if the first random number is less than the variation probability pmIf not, the mutation operation is not carried out;
the mutation probability pmThe calculation formula is as follows:
pm(t)＝|exp(f(t)/f(t-1)-1)-1| (2)
wherein t refers to the first generation population; (t) is the fitness value of the tth population; f (t-1) refers to the fitness value of the previous generation population;
the mutation operation comprises three aspects:
first, the number of branch points is changed: dividing the whole population into three parts; the first part reduces the number of branch points by 1; the second part increases the number of branch points by one; the third portion remains unchanged; wherein the number of branch points should be 1 to NmaxIs changed from N to NmaxThe size of (A) is the number of wire and cable terminals reducedGo 2, increase and decrease of branch points are randomly selected;
second, changing the position of the branch point: the remaining branch points should be moved to the positions of the adjacent path points in the path diagram; when the position of the branch point is changed, the position of the branch point should not exceed the boundary formed by the end points;
third, the adjacency list structure is changed: when a branch point is removed, the terminal to which it is connected should be connected to a branch point adjacent to the removed branch point; when a branch point is added, the newly added branch point should be connected with the nearest end point and the branch point, and all population individuals should randomly select K branch points, disconnect the branch points from the original branch point, and reconnect to different branch points.
7. The method of claim 4, wherein the cross-over operation is performed by:
according to the cross probability pcJudging whether to carry out cross operation, generating a second random number between 0 and 1, comparing the second random number with the cross probability, and if the second random number is less than the cross probability pcIf not, the cross operation is not carried out;
the formula of the cross probability is as follows:
pc(t)＝1-pm(t) (3)
the crossing operation comprises four aspects:
firstly, pairing the population individuals with the same quantity of branching points;
secondly, the fitness value of each item in the adjacency list is compared to find an optimal item and a worst item;
thirdly, exchanging corresponding structures in the adjacency list and storing the structures of the two individuals with the minimum fitness value;
fourthly, according to the encoding principle, the structure of the adjacent table is readjusted to generate a new population individual.
8. The method of claim 4, wherein the cross-over operation is performed by:
selecting operation is performed by adopting elite selection: in each iteration process, the population individuals are sorted according to the fitness value of the population individuals, and the individuals with the same size as the population are selected to enter the next generation of population.
9. The method of claim 4, wherein the termination condition is defined by the formula:
wherein, nwbiThe ith branch, lbiThe ith branch length; gi＝5+nwbi，giIs the ith fitness; the termination condition being that the number of iterations reaches a maximum value or
10. An automatic wire and cable laying-out device, which is realized by the automatic wire and cable laying-out method according to any one of claims 1-9, and comprises: the system comprises an input module, a point network construction module, a branch structure design module, a central curve module and an output module;
the input module, the point network construction module, the branch structure design module, the center curve module and the output module are sequentially connected;
the input module is used for inputting a structural part model and an electrical connection relation;
the network construction module of the point is used for constructing a path network in the structural part model based on a random path diagram algorithm;
the branch structure design module is used for designing a branch structure based on a genetic algorithm aiming at the path network to obtain the positions of a branch point and a terminal;
the central curve module is used for designing a central line consisting of discrete points of each branch of the electric wire and the cable according to the positions of the branch points and the terminals and carrying out curve fitting;
and the output module is used for outputting the layout result of the electric wires and the cables.
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