CN107883956A - A kind of routing resource and system based on dijkstra's algorithm - Google Patents
A kind of routing resource and system based on dijkstra's algorithm Download PDFInfo
- Publication number
- CN107883956A CN107883956A CN201710979873.5A CN201710979873A CN107883956A CN 107883956 A CN107883956 A CN 107883956A CN 201710979873 A CN201710979873 A CN 201710979873A CN 107883956 A CN107883956 A CN 107883956A
- Authority
- CN
- China
- Prior art keywords
- weight
- path
- selection
- degree
- indoor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
Abstract
The invention discloses a kind of routing resource and system based on dijkstra's algorithm, first obtain indoor path model, the indoor path model includes the weights of each selection factor between each node, selection factor in the indoor path model includes the crowding in path distance and path, then the synthesis weights between each node of indoor path model are calculated, again to integrate the weights for the path distance replaced corresponding to weights in dijkstra's algorithm to carry out dijkstra's algorithm, a minimum path of total synthesis weights is selected.The present invention is considered on the selection factor of multinomial influence path planning, can obtain more conforming to the comprehensive optimization route of actual indoor environment and users ' individualized requirement.
Description
Technical Field
The invention relates to the field of navigation, in particular to the aspect of path selection in the navigation process, and more particularly relates to a path selection method and system based on Dijkstra algorithm.
Background
With the acceleration of the urbanization process, large indoor buildings are more and more, the internal structures of the large indoor buildings are more and more complex, the indoor activity time of people is longer and longer, and the indoor navigation path planning has practical significance. In the path planning algorithm, dijkstra (Dijkstra) algorithm is an effective algorithm for solving the shortest path from a certain source point to other points in the directed graph, and the algorithm is suitable for network topology change, has stable performance and is a classical algorithm for path planning.
The idea of the Dijkstra algorithm is as follows: and G = (V, E) is a weighted directed graph, a set V of all vertexes in the graph is divided into two groups, the first group is a set of vertexes with the shortest paths already obtained (indicated by S, only one source point in S is initially obtained, each shortest path is obtained later, the set is added into the set S until all vertexes are added into S, the algorithm is ended), the second group is a set of vertexes (indicated by U) with the remaining undetermined shortest paths, and the vertexes of the second group are added into S in sequence according to the ascending order of the lengths of the shortest paths. In the joining process, the shortest path length from the source point v to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U. In addition, each vertex corresponds to a distance, the distance of the vertex in S is the shortest path length from v to the vertex, and the distance of the vertex in U is the current shortest path length from v to the vertex, only including the vertex in S as the middle vertex.
Dijkstra algorithm steps:
a. initially, S only contains the source point, i.e. S = { v }, the distance of v is 0.U includes vertices other than v, i.e., U = { remaining vertices }, where if v has an edge with vertex U in U, then < U, v > has a weight not ∞, and if U is not an edge-out adjacency point of v, then the < U, v > weight is ∞.
b. And selecting a vertex k with the minimum distance v from U, and adding k into S (the selected distance is the shortest path length from v to k).
c. Modifying the distance of each vertex in the U by taking k as a newly considered middle point; if the distance from the source point v to the vertex u (passing through the vertex k) is shorter than the original distance (not passing through the vertex k), the distance value of the vertex u is modified, and the weight of the distance of the vertex k of the modified distance value is added to the upper side.
d. Repeating steps b and c until all vertices are contained in S.
The Dijkstra algorithm can obtain the shortest path from the starting point to the end point, but in the problem of planning the actual indoor navigation path, due to the complexity and diversity of the indoor environment, the actual indoor path selection is personalized and diversified, so that people can select the indoor path not only by taking the path distance as the unique standard, but also by considering other factors influencing the path selection, and the requirement of people on indoor path selection cannot be met only by considering the path distance.
Disclosure of Invention
The invention aims to solve the technical problems that the indoor path selection is personalized and diversified aiming at the complexity and diversity of the existing indoor environment, the existing path planning method only takes the path distance as the only standard to carry out the planning navigation of the indoor path, and only the path distance is considered, so that the technical defect that the indoor path selection requirement of people cannot be met is overcome, and the path selection method and the system based on the Dijkstra algorithm are provided.
According to one aspect of the present invention, to solve the technical problem, the present invention provides a path selection method based on Dijkstra algorithm, comprising the following steps:
s1, obtaining an indoor path model, wherein the indoor path model comprises weights of all selection factors among all nodes, and the selection factors in the indoor path model comprise path distance and path crowding degree;
s2, calculating a comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and any node q is calculated through the following formula:
f＝ω 1 ×f 1 +ω 2 ×f 2 +…+ω n ×f n ，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f 1 、f 2 8230a and f n Respectively, the weight, omega, of each factor between the node p and the node q 1 、ω 2 8230a and omega n Are respectively f 1 、f 2 "\ 8230", and f n The corresponding weight.
And S3, carrying out Dijkstra algorithm by using the weight corresponding to the comprehensive weight and replacing the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
Further, in the Dijkstra algorithm-based path selection method of the present invention, the normalized weight of the kth selection factor is obtained by the following method:
wherein k =1, 2, \ 8230, n, S k The matrix standard degree is judged by 1-9 levels, the matrix standard degree values of the kth selection factor relative to all the selection factors are respectively calculated, and then the matrix standard degree values of all the selection factors are added to obtain the sum.
Further, in the path selection method based on Dijkstra algorithm according to the present invention, the plurality of selection factors may be summarized as three selection factors, which are a distance to a path, a congestion degree of a path, and a preference degree of a path.
Further, in the Dijkstra algorithm-based path selection method of the present invention, the weight of the path distance is ω 1 =0.633, and the weight of the degree of congestion of the route is ω 2 =0.261, and the preference degree of the path is weighted by ω 3 ＝0.106。
Further, in the path selection method based on Dijkstra algorithm of the present invention, in the process of obtaining the weight of each selection factor, the method further includes the steps of: whether the normalized weight obtained by calculation meets the actual importance among weight types is checked by carrying out consistency judgment on the normalized weight, if so, the weight calculated this time is taken as the final value of the weight, otherwise, the value of the matrix standard degree is obtained again to calculate the weight;
the consistency judgment method comprises the following steps:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein, the first and the second end of the pipe are connected with each other,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambda max The method is used for judging the maximum characteristic root of the matrix, and the element of the jth column of the ith row of the matrix is the value of the matrix standard degree of the ith selection factor relative to the jth selection factor, i =1, 2, \ 8230;, n, j =1, 2, \ 8230;, and n.
According to another aspect of the present invention, to solve the technical problem, the present invention further provides a path selection system based on Dijkstra algorithm, including the following steps:
a path model obtaining module, configured to obtain an indoor path model, where the indoor path model includes weights of selection factors between nodes, and the selection factors in the indoor path model include a path distance and a congestion degree of a path;
and the comprehensive weight calculation module is used for calculating the comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and q is calculated by the following formula:
f＝ω 1 ×f 1 +ω 2 ×f 2 +…+ω n ×f n ，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f 1 、f 2 "\ 8230", and f n Respectively, the weight, omega, of each factor between the node p and the node q 1 、ω 2 8230a and omega n Are respectively f 1 、f 2 "\ 8230", and f n The corresponding weight.
And the Dijkstra algorithm module is used for performing Dijkstra algorithm by using the weight corresponding to the comprehensive weight to replace the path distance in the Dijkstra algorithm and selecting a path with the minimum total comprehensive weight.
Further, in the Dijkstra algorithm-based path selection system of the present invention, the normalized weight of the kth selection factor is obtained by the following method:
wherein k =1, 2, \8230, n, S k The matrix standard degree is judged by 1-9 levels, the matrix standard degree values of the kth selection factor relative to all the selection factors are respectively calculated, and then the matrix standard degree values of all the selection factors are added to obtain the sum.
Further, in the Dijkstra algorithm-based path selection system of the present invention, the selection factors may be classified into three selection factors, which are a path distance, a congestion degree of a path, and a preference degree of a path.
Further, in the Dijkstra algorithm-based path selection system of the present invention, the weight of the path distance is ω 1 =0.633, and the weight of the degree of congestion of the route is ω 2 =0.261, and the preference degree of the path is weighted by ω 3 ＝0.106。
Further, in the Dijkstra algorithm-based path selection system of the present invention, in the process of obtaining the weight of each selection factor, the method further comprises the steps of: checking whether the normalized weight obtained by calculation accords with the actual importance among weight types or not by carrying out consistency on the normalized weight, if so, taking the weight calculated this time as the final value of the weight, and otherwise, obtaining the value of the matrix standard degree to be reselected to calculate the weight;
the method for consistency judgment is as follows:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein the content of the first and second substances,R.I. is equal to the value of the n-th order matrix in the average random consistency table, λ max The method is used for judging the maximum characteristic root of the matrix, and the element of the jth column of the ith row of the matrix is the value of the matrix standard degree of the ith selection factor relative to the jth selection factor, i =1, 2, \ 8230;, n, j =1, 2, \ 8230;, and n.
The invention discloses a Dijkstra algorithm-based path selection method and a Dijkstra algorithm-based path selection system, wherein an indoor path model is obtained firstly, the indoor path model comprises weights of all selection factors among all nodes, the selection factors in the indoor path model comprise path distances and path crowding degrees, then comprehensive weights among all nodes of the indoor path model are calculated, the Dijkstra algorithm is carried out by using the weights corresponding to the comprehensive weights to replace the path distances in the Dijkstra algorithm, and a path with the minimum total comprehensive weight is selected. The invention comprehensively considers a plurality of selection factors influencing path planning, and can obtain a comprehensive optimal path which better meets the actual indoor environment and the individual requirements of users.
Drawings
The invention will be further described with reference to the following drawings and examples, in which:
FIG. 1 is a flow chart of an embodiment of a Dijkstra algorithm based path selection method of the present invention;
fig. 2 is a path network topology diagram of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, it is a flowchart of an embodiment of the Dijkstra algorithm-based path selection method of the present invention. The path selection method of the embodiment mainly comprises the following steps:
s1, obtaining an indoor path model, wherein the indoor path model comprises weights of all selection factors among all nodes, and the selection factors are at least two.
The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user.
The path distance reflects the length of the road section, is the most direct factor in the navigation path planning problem, is simple and intuitive, and can be obtained by relevant measurement and calculation of the road section. The weight of the path distance is the basis of planning all navigation paths, is a factor that must be considered when a user selects a path, and is generally more favorable for selecting the road section when the value is smaller. The traditional Dijkstra algorithm selects the path distance as the unique weight for navigation path planning to obtain the shortest path from the starting point to the destination point. However, due to the complexity and diversity of the indoor environment, the user often does not consider only the weight of the path distance as a factor for path selection, and therefore other influencing factors for indoor navigation path planning need to be analyzed, and a more comprehensive optimized path is planned. According to the length of the path, the path can be divided into a plurality of levels, each level corresponds to a weight, and the longer the path is, the larger the value is.
The congestion degree is a conceptual numerical value comprehensively reflecting the smoothness or congestion of the road network, and the congestion degree weight is combined to optimize the path, so that congested road sections can be effectively avoided, and the travel time is saved. An urban road traffic operation evaluation index system is published in Beijing City in 2011 month 4 and formally implemented with August in the same year, traffic operation indexes are used for comprehensively reflecting traffic jam conditions of a road network, the urban road traffic operation evaluation index system qualitatively divides the traffic jam degree into five levels, the numerical value range is 0 to 10, every two levels are divided into one level respectively corresponding to 'unblocked', 'basically unblocked', 'slightly jammed', 'moderately jammed', 'severely jammed', 'seriously jammed', the smaller the numerical value is, the more unblocked traffic is indicated, and the more serious the numerical value is, the more serious the traffic jam condition is indicated. By referring to and referring to a definition scheme of the congestion degree in a traffic evaluation index system in outdoor navigation, a specific definition rule of the indoor road congestion degree for indoor navigation path planning can be determined. In the field of outdoor navigation, the traffic congestion degree mainly reflects the real-time traffic congestion condition through the traffic flow, and similarly, in the indoor path navigation, the indoor congestion degree is reflected through the traffic flow. In practical application, people flow data of a certain building can be actually measured, a large amount of data is subjected to statistical analysis to obtain an indoor road congestion degree index, and the characteristics of the congestion degree of the indoor building are further reflected. Referring to the definition rule of the traffic congestion degree of the urban road, the indoor congestion degree evaluation index is divided into five levels according to the embodiment, and the evaluation index is as follows: "clear", "substantially clear", "light congestion", "moderate congestion", "severe congestion". The value range of the congestion degree index is [0, 10], two numbers are divided into a grade at intervals, and the value of the congestion degree index can be directly used as the weight of the influence factor. A congestion degree value closer to 0 indicates a more clear link, and a value closer to 10 indicates a more congested link. The correspondence between the degree of congestion and the congestion degree index is as follows:
table 1 indoor road network congestion degree evaluation table
In the actual indoor navigation path planning, due to the diversity and complexity of indoor building structures and different use situations, a user is used as a main body of path decision, the indoor activities of the user often have strong autonomous selectivity and personalized preference, and the user preference degree can also influence the path selection of indoor navigation, so that the personalized preference of the user cannot be ignored, for example: in large indoor malls, some users prefer to select a main road or a road section near certain shops, elevators, etc. The user preference represents the preference degree of the user on the road section, and is a subjective decision index provided for the user. The path which meets the user preference better is searched under the condition of meeting certain time or distance constraint, balance is found between path consumption (time or distance) and the personalized requirements of the user, the planning idea combines the indoor environment characteristics, the experience feeling of the path is enhanced, and the humanized idea is embodied. Because the complexity of the indoor environment and the diversification and individuation of the indoor activities of the user, the selection of the indoor path has strong subjectivity and instantaneity, meanwhile, the invention mainly focuses on the analysis of the influence of the user preference on the indoor path planning and establishes the indoor path navigation model with comprehensive weight, therefore, the invention adopts a rule-based planning method to qualitatively divide the user preference into nine grades, the value assignment range is 1-9, each numerical value corresponds to one-level preference degree, the corresponding relation between the user preference degree and the evaluation index is as the following formula, the smaller the user preference degree value is, the higher the user preference degree is, namely the more the user likes, the larger the user selection preference degree is; the greater the value of the user preference, the less preferred, i.e., less preferred, the less user selection-oriented. In the specific indoor navigation path planning, the corresponding preference values set by different users according to a subjective weighting method can be obtained, and the preference values are also the weight values of the preference degrees of the paths.
S2, calculating a comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and any node q is calculated through the following formula:
f＝ω 1 ×f 1 +ω 2 ×f 2 +…+ω n ×f n ，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f 1 、f 2 8230a and f n Respectively, the weight, omega, of each factor between the node p and the node q 1 、ω 2 8230a and omega n Are respectively f 1 、f 2 8230a and f n The corresponding weight. If omega 1 、ω 2 、ω 3 Normalized, then ω 1 +ω 2 +ω 3 ＝1。
The invention uses an analytic hierarchy process, firstly, qualitatively determines the importance degree between every two weights, and then converts the qualitative analysis into quantitative data calculation to obtain the influence of each weight type on the comprehensive weight. Solving influence degree of each weight type of multi-target linear weighting on comprehensive weight in an indoor navigation path planning problem by using AHP, and specifically comprising the following steps:
the judgment matrix of each weight type is constructed by introducing a judgment matrix standard degree, and adopting The 1-9 grade judgment matrix standard degree proposed by t.l. saaty et al (see book "The analytical Hierarchy Process", author t.l. saaty), as shown in table 2 below:
TABLE 2 Standard degree of 1-9 judging matrix
According to the judgment matrix standard table, constructing judgment matrixes of various weight types, as shown in the following table 3:
TABLE 3 judgment matrix for each weight type
As can be seen from the table, the relative importance degree between each two of the weights is preset in the present embodiment, for example: the weight of the route distance is higher than the influence of the weight of the congestion degree of the route and the weight of the preference degree of the route on the integrated weight, and the weight of the congestion degree of the route is slightly higher than the influence of the preference degree of the route.
Then, a normalized column average method is adopted for calculation, and the calculation steps for solving the combination weight coefficient are as follows:
first, summing the decision matrices in columns to obtain the following table 4:
TABLE 4 matrix column summation
| F | f1 | f2 | f3 |
| f1 | 1 | 3 | 5 |
| f2 | 1/3 | 1 | 3 |
| f3 | 1/5 | 1/3 | 1 |
| sum | 23/15 | 13/3 | 9 |
The columns are normalized and summed together to obtain the eigenvectors, as shown in table 5 below:
table 5 column normalization
| F | f1 | f2 | f3 | sum |
| f1 | 15/23 | 9/13 | 5/9 | 1.9 |
| f2 | 5/23 | 3/13 | 1/3 | 0.782 |
| f3 | 3/23 | 1/13 | 1/9 | 0.318 |
Then, normalizing the feature vector to obtain a combination weight coefficient, namely, each influence degree value:
ω 1 ＝0.633，ω 2 ＝0.261，ω 3 ＝0.106。
and carrying out consistency judgment on the influence coefficient, and checking whether the calculated relative weight coefficient accords with the actual importance among the weight types or not through the consistency judgment, thereby checking the correctness of the multi-weight fusion. A unified table of average random consensus was introduced (see Beynon M J. An analysis of distributions of priorities from alternative scales with AHP, journal of European Journal of Operational Research, 2002,140 (1): 104-117.) as follows, table 6:
TABLE 6 average random consistency Table
TABLE 6 average random consistency table (continue)
TABLE 6 average random consistency Table (continuation)
The check consistency formula is as follows:
wherein
n is the number of parameters, i.e. the number of each weight type, R.I. is equal to the value of n-order matrix in the average random consistency table, lambda max Is the largest feature root of the decision matrix. If C.R.&And lt, 0.1, judging that the matrixes are consistent, otherwise, judging that the matrixes are inconsistent. And if the matrix standard degree is consistent with the weight, taking the weight calculated this time as the final value of the weight, otherwise, obtaining the value of the reselecting matrix standard degree to calculate the weight. Due to the fact that
Can obtain lambda max =3.0556, will λ max And n into the above formula can yield c.i. =3.0556-3/3-1=0.0535, and bringing c.i. into the above formula can yield c.r. =0.0535/0.52=0.053<, 0.1. Therefore, the judgment matrix obtained by the calculation meets the consistency, accords with the actual importance among all the weights, and verifies the correctness of the judgment matrix. In summary, the calculation formula of the obtained combined weight coefficient integrated weight is used to obtain the integrated weight of the improved indoor navigation path planning model as follows
F＝0.633*f 1 +0.261*f 2 +0.106*f 3
And S3, carrying out Dijkstra algorithm by using the weight corresponding to the comprehensive weight to replace the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight. The step is completely consistent with the Dijkstra algorithm, and only the path distance weight in the Dijkstra algorithm is replaced by the comprehensive weight correspondingly.
The invention adopts the following indoor path network topological graph to carry out specific example simulation test, and the path network topological graph is shown as the following figure 2. The node 0 in the road network is set as an initial node, and three values marked on a road section between two nodes respectively represent a path distance, a congestion degree and a user preference degree weight in sequence.
Firstly, preprocessing the road network information data to obtain a multidimensional array of three weights of path distance, congestion degree and user preference degree, wherein the following formula is shown in sequence, and if two nodes are not adjacent, the numerical value is defined as infinite.
Substituting each weight of each road section between two adjacent nodes into a comprehensive weight calculation formula of the built indoor navigation path planning model: f = 0.633F 1 +0.261*f 2 +0.106*f 3 And obtaining the comprehensive weight of each road section between two adjacent nodes, and obtaining a matrix of the comprehensive weight of the path network topological graph. As shown in the following formula:
the numerical value in the formula represents the comprehensive weight of the road section between two nodes of the indoor navigation path planning model, and the comprehensive weight of non-adjacent nodes is infinite. By means of the matrix, a path with the minimum comprehensive weight value is obtained by combining a Dijkstra algorithm, namely, a comprehensive optimal path which is more in line with the individual requirements of users and is from the initial node to the target node is planned. Set 0 as the source node, the simulation test results are shown in table 7 below:
TABLE 7 improved model test results
The composite optimal path from the source node to each node is shown in table 7. Taking the starting node 0 to the destination node 9 as an example, the optimal path integrating the multiple weight types is a path from the node 0 to the node 6 through the node 3 to the node 5 and finally to the node 9.
The traditional Dijkstra algorithm only considers the path length as the only weight for path planning, and expands outward layer by taking the starting point as the center until the path is expanded to the end point, so as to obtain the shortest path from the starting point to the end point. The traditional Dijkstra algorithm path planning model is adopted to solve the planned path from the starting node 0 to each target node in the path network topological graph, and the results are shown in the following table 8.
TABLE 8 results of conventional model testing
Also taking the starting node 0 to the destination node 9 as an example, the planned path obtained by the traditional Dijkstra algorithm path planning model is a path from the node 0 to the node 9 through the node 2, the node 5, the node 6 and finally the node 9.
And comparing and analyzing the test results of the optimized path obtained by the improved indoor navigation path planning model and the optimized path obtained by path planning of the traditional Dijkstra algorithm. Taking the starting node 0 to the destination node 4 as an example, the optimized path obtained under the improved model is from the starting node 0 to the destination node 4 through the node 3; the path obtained under the traditional Dijkstra algorithm model is from the starting node 0 to the destination node 4 through the node 1. It can be seen that, when the path distance is taken as the only consideration factor, 0-1-4 is the shortest path from the starting node 0 to the destination node 4, and this situation only considers the length of the path, but due to the complexity and diversity of the indoor environment, the user is the main body of the indoor activity, the personalized demand is more and more strong, and the road congestion degree and the user preference degree are important influence factors in the indoor path navigation problem and need to be taken into account. The optimized path planned by the improved indoor navigation planning model is 0-3-4, although the path is not shortest, the congestion degree and the user preference degree are small, namely, the road is smooth, and the user prefers, so that the path 0-3-4 in the road network is a comprehensive and optimal path from the starting node 0 to the destination node 4. In an indoor environment, a user selects a shortest path, important factors such as the congestion degree of a road and the personalized preference degree of the user can be considered on the basis of the path distance, the improved indoor navigation planning model avoids the problem that the traditional Dijkstra algorithm only considers the singleness of the factor of the path distance, comprehensively considers the influence of the path distance, the congestion degree and the user preference degree on indoor navigation path planning, has comprehensiveness, and can better meet the user requirements.
The invention also correspondingly provides a path selection system based on Dijkstra algorithm, which comprises:
the route model acquisition module is used for acquiring an indoor route model, wherein the indoor route model comprises weights of all selection factors among all nodes, and the selection factors are at least two;
and the comprehensive weight calculation module is used for calculating the comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and q is calculated by the following formula:
f＝ω 1 ×f 1 +ω 2 ×f 2 +…+ω n ×f n ，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f 1 、f 2 8230a and f n Respectively the weight value, omega, of each factor between the node p and the node q 1 、ω 2 8230a and omega n Are respectively f 1 、f 2 "\ 8230", and f n The corresponding weight.
And the Dijkstra algorithm module is used for performing Dijkstra algorithm by using the weight corresponding to the comprehensive weight and replacing the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A path selection method based on Dijkstra algorithm is characterized by comprising the following steps:
s1, obtaining an indoor path model, wherein the indoor path model comprises weights of all selection factors among all nodes, and the selection factors in the indoor path model comprise path distance and path crowding degree;
s2, calculating comprehensive weights among all nodes of the indoor path model, wherein the comprehensive weight between any node p and any node q is obtained through calculation according to the following formula:
f＝ω 1 ×f 1 +ω 2 ×f 2 +…+ω n ×f n ，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f 1 、f 2 "\ 8230", and f n Respectively, the weight, omega, of each factor between the node p and the node q 1 、ω 2 8230a and omega n Are respectively f 1 、f 2 "\ 8230", and f n A corresponding weight;
and S3, carrying out Dijkstra algorithm by using the weight corresponding to the comprehensive weight to replace the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
2. The path selection method according to claim 1, wherein the normalized weight of the kth selection factor is obtained by:
wherein k =1, 2, \ 8230, n, S k The matrix standard degree is judged by 1-9 levels, the matrix standard degree values of the kth selection factor relative to all the selection factors are respectively calculated, and then the matrix standard degree values of all the selection factors are added to obtain the sum.
3. The method according to claim 2, wherein the plurality of selection factors are three selection factors, which are a route distance, a congestion degree of a route, and a preference degree of a route.
4. A method for selecting a path according to claim 3, characterized in that the path distance is weighted by ω 1 =0.633, and the weight of the degree of congestion of the route is ω 2 =0.261, and the preference degree of the path is weighted by ω 3 ＝0.106。
5. The method for selecting a route according to claim 2, wherein the process of obtaining the weight of each selection factor further comprises the steps of: whether the normalized weight obtained by calculation meets the actual importance among weight types is checked by carrying out consistency judgment on the normalized weight, if so, the weight calculated this time is taken as the final value of the weight, otherwise, the value of the matrix standard degree is obtained again to calculate the weight;
the consistency judgment method comprises the following steps:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein, the first and the second end of the pipe are connected with each other,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambda max The method is used for judging the maximum characteristic root of the matrix, and the element of the jth column of the ith row of the matrix is the value of the matrix standard degree of the ith selection factor relative to the jth selection factor, i =1, 2, \ 8230;, n, j =1, 2, \ 8230;, and n.
6. A path selection system based on Dijkstra algorithm, comprising:
a path model obtaining module, configured to obtain an indoor path model, where the indoor path model includes weights of selection factors between nodes, and the selection factors in the indoor path model include a path distance and a congestion degree of a path;
and the comprehensive weight calculation module is used for calculating the comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and q is calculated by the following formula:
f＝ω 1 ×f 1 +ω 2 ×f 2 +…+ω n ×f n ，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f 1 、f 2 "\ 8230", and f n Respectively, the weight, omega, of each factor between the node p and the node q 1 、ω 2 8230a and omega n Are respectively f 1 、f 2 8230a and f n The corresponding weight.
And the Dijkstra algorithm module is used for performing Dijkstra algorithm by using the weight corresponding to the comprehensive weight and replacing the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
7. The routing system of claim 6, wherein the normalized weight for the kth selection factor is obtained by:
wherein k =1, 2, \8230, n, S k The method comprises judging the matrix standard degree by 1-9 levelsAnd solving the values of the matrix standard degree of the k-th selection factor relative to all the selection factors respectively, and then adding the values of all the matrix standard degrees of the selection factors.
8. The routing system of claim 7, wherein the plurality of selection factors are three selection factors, each of which is a route distance, a congestion degree of the route, and a preference degree of the route.
9. The routing system of claim 8, wherein the path distance is weighted by ω 1 =0.633, and the weight of the degree of congestion of the route is ω 2 =0.261, and the preference degree of the path is weighted by ω 3 ＝0.106。
10. The routing system of claim 7, wherein the process of obtaining the weight of each selection factor further comprises the steps of: whether the normalized weight obtained by calculation accords with the actual importance among weight types is checked by carrying out consistency judgment on the normalized weight, if so, the weight calculated this time is taken as the final value of the weight, otherwise, the value of the matrix standard degree is obtained again to calculate the weight;
the method for consistency judgment is as follows:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein, the first and the second end of the pipe are connected with each other,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambda max The method is characterized in that the maximum characteristic root of a matrix is judged, elements of a jth column of an ith row of the matrix are judged to be values of matrix standard degrees of the ith selection factor relative to the jth selection factor, i =1, 2, \8230;, n, j =1, 2, \8230;, and n.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710979873.5A CN107883956A (en) | 2017-10-19 | 2017-10-19 | A kind of routing resource and system based on dijkstra's algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710979873.5A CN107883956A (en) | 2017-10-19 | 2017-10-19 | A kind of routing resource and system based on dijkstra's algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107883956A true CN107883956A (en) | 2018-04-06 |
Family
ID=61782040
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710979873.5A Pending CN107883956A (en) | 2017-10-19 | 2017-10-19 | A kind of routing resource and system based on dijkstra's algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107883956A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109506667A (en) * | 2018-11-28 | 2019-03-22 | 上海智能交通有限公司 | A kind of information interacting method of the intelligent travel for heavily loaded oversize vehicle |
| CN109979006A (en) * | 2019-03-14 | 2019-07-05 | 北京建筑大学 | Indoor road net model construction method and device |
| CN110598921A (en) * | 2019-08-30 | 2019-12-20 | 泰华智慧产业集团股份有限公司 | Shortest path algorithm-based building project auxiliary site selection method and system |
| CN110672089A (en) * | 2019-09-23 | 2020-01-10 | 上海功存智能科技有限公司 | Method and device for navigation in indoor environment |
| CN111367275A (en) * | 2020-02-18 | 2020-07-03 | 吉利汽车研究院（宁波）有限公司 | Intelligent driving control method, device and system and storage medium |
| CN111573126A (en) * | 2020-05-11 | 2020-08-25 | 盐城工学院 | Modular intelligent logistics system material distribution path planning method based on omnidirectional wheel |
| CN112862625A (en) * | 2021-01-07 | 2021-05-28 | 国网浙江杭州市余杭区供电有限公司 | Power supply path acquisition method based on Dijkstra algorithm |
| CN114866459A (en) * | 2022-04-18 | 2022-08-05 | 北京计算机技术及应用研究所 | Path planning method under multi-constraint condition |
| CN115646818A (en) * | 2022-12-28 | 2023-01-31 | 江苏智联天地科技有限公司 | AGV intelligence letter sorting system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007334472A (en) * | 2006-06-13 | 2007-12-27 | Mitsubishi Electric Engineering Co Ltd | Taxi fare system device |
| CN103791906A (en) * | 2014-02-21 | 2014-05-14 | 南京北大工道创新有限公司 | Indoor positioning position correction method based on indoor positioning device |
| CA2847758A1 (en) * | 2013-10-24 | 2015-04-24 | Palantir Technologies, Inc. | Systems and methods for distance and congestion-aware resource deployment |
| CN105910605A (en) * | 2016-05-30 | 2016-08-31 | 中国科学技术大学苏州研究院 | Indoor navigation dynamic route generation method |
| CN106052692A (en) * | 2016-05-20 | 2016-10-26 | 中国地质大学（武汉） | Shortest route planning and navigating method and system |
| CN106918345A (en) * | 2017-03-27 | 2017-07-04 | 中国农业大学 | A kind of optimization method and device in scenic region guide path |
-
2017
- 2017-10-19 CN CN201710979873.5A patent/CN107883956A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007334472A (en) * | 2006-06-13 | 2007-12-27 | Mitsubishi Electric Engineering Co Ltd | Taxi fare system device |
| CA2847758A1 (en) * | 2013-10-24 | 2015-04-24 | Palantir Technologies, Inc. | Systems and methods for distance and congestion-aware resource deployment |
| CN103791906A (en) * | 2014-02-21 | 2014-05-14 | 南京北大工道创新有限公司 | Indoor positioning position correction method based on indoor positioning device |
| CN106052692A (en) * | 2016-05-20 | 2016-10-26 | 中国地质大学（武汉） | Shortest route planning and navigating method and system |
| CN105910605A (en) * | 2016-05-30 | 2016-08-31 | 中国科学技术大学苏州研究院 | Indoor navigation dynamic route generation method |
| CN106918345A (en) * | 2017-03-27 | 2017-07-04 | 中国农业大学 | A kind of optimization method and device in scenic region guide path |
Non-Patent Citations (2)
| Title |
|---|
| 王海峰等: "基于驾驶员偏好的智能导航终端开发中的路网权值标定方法研究", 《科学技术与工程》 * |
| 陆百川等: "基于主客观赋权法的最优路径选择分析", 《武汉理工大学学报(信息与管理工程版)》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109506667A (en) * | 2018-11-28 | 2019-03-22 | 上海智能交通有限公司 | A kind of information interacting method of the intelligent travel for heavily loaded oversize vehicle |
| CN109979006A (en) * | 2019-03-14 | 2019-07-05 | 北京建筑大学 | Indoor road net model construction method and device |
| CN109979006B (en) * | 2019-03-14 | 2024-02-23 | 北京建筑大学 | Indoor road network model construction method and device |
| CN110598921A (en) * | 2019-08-30 | 2019-12-20 | 泰华智慧产业集团股份有限公司 | Shortest path algorithm-based building project auxiliary site selection method and system |
| CN110672089A (en) * | 2019-09-23 | 2020-01-10 | 上海功存智能科技有限公司 | Method and device for navigation in indoor environment |
| CN111367275A (en) * | 2020-02-18 | 2020-07-03 | 吉利汽车研究院（宁波）有限公司 | Intelligent driving control method, device and system and storage medium |
| CN111573126A (en) * | 2020-05-11 | 2020-08-25 | 盐城工学院 | Modular intelligent logistics system material distribution path planning method based on omnidirectional wheel |
| CN112862625A (en) * | 2021-01-07 | 2021-05-28 | 国网浙江杭州市余杭区供电有限公司 | Power supply path acquisition method based on Dijkstra algorithm |
| CN114866459A (en) * | 2022-04-18 | 2022-08-05 | 北京计算机技术及应用研究所 | Path planning method under multi-constraint condition |
| CN114866459B (en) * | 2022-04-18 | 2023-04-28 | 北京计算机技术及应用研究所 | Path planning method under multiple constraint conditions |
| CN115646818A (en) * | 2022-12-28 | 2023-01-31 | 江苏智联天地科技有限公司 | AGV intelligence letter sorting system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107883956A (en) | A kind of routing resource and system based on dijkstra's algorithm | |
| Baranyi et al. | Contribution of habitat patches to network connectivity: Redundancy and uniqueness of topological indices | |
| Ye et al. | Measuring urban maturation processes in Dutch and Chinese new towns: Combining street network configuration with building density and degree of land use diversification through GIS | |
| CN110276556B (en) | Multi-strategy-based environmental bearing capacity index weight calculation method and device | |
| McDaniel et al. | Using origin–destination centrality to estimate directional bicycle volumes | |
| Zheng et al. | A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties | |
| CN108799844B (en) | Fuzzy set-based water supply network pressure monitoring point site selection method | |
| CN112819210B (en) | Online single-point task allocation method capable of being rejected by workers in space crowdsourcing | |
| CN112797995B (en) | Vehicle emergency navigation method with space-time characteristic situation information | |
| CN109214863B (en) | Method for predicting urban house demand based on express delivery data | |
| CN111882909B (en) | Emergency rescue scheduling and dynamic path integration method based on double-layer planning | |
| CN103824127B (en) | Service self-adaptive combinatorial optimization method under cloud computing environment | |
| El Rashidy et al. | A composite resilience index for road transport networks | |
| Terekhov et al. | Forecasting a global air passenger demand network using weighted similarity-based algorithms | |
| CN111008736A (en) | Opening decision method and system for new airline | |
| Olsson et al. | Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems | |
| CN111008730B (en) | Crowd concentration prediction model construction method and device based on urban space structure | |
| Parent et al. | Simulating future forest fragmentation in a Connecticut region undergoing suburbanization | |
| CN107609938A (en) | A kind of service recommendation method based on the qualitative and quantitative preference of user | |
| Ivanchev et al. | Spatial and temporal analysis of mismatch between planned road infrastructure and traffic demand in large cities | |
| Ma et al. | Integrating logistic regression with ant colony optimization for smart urban growth modelling | |
| CN116451963A (en) | Multi-factor pension service facility optimal configuration method based on ensemble learning | |
| CN114401211B (en) | Test system and test method for accessing industrial wireless network equipment to IPv6 network | |
| CN116882599A (en) | Planning method and device for travel route | |
| Hutasuhut et al. | Decision Support System for Finding the Best Restaurant Using AHP Method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180406 |
|
| RJ01 | Rejection of invention patent application after publication |