CN118154069B - Intelligent planning method and system for logistics transportation line - Google Patents
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Abstract
The application relates to the technical field of logistics transportation, in particular to an intelligent planning method and system for a logistics transportation line, wherein the method comprises the following steps: collecting horizontal height data, the number of the components and the number of accumulated delivery failure; acquiring an elevation deviation coefficient between any two distribution nodes, and acquiring a trend change coefficient between any two distribution nodes; further obtaining a unidirectional fluctuation disorder coefficient between any two distribution nodes; the method comprises the steps of obtaining a directional preference coefficient between any two distribution nodes and obtaining a turn-back power ratio between any two distribution nodes; further obtaining the optimal directional weight from each distribution node to other distribution nodes; and constructing a weighted directed graph according to the optimal directed weight, and planning a logistics transportation route. Therefore, the actual scene can be more closely reflected, and intelligent dynamic planning of the logistics transportation route is realized.
Description
Technical Field
The application relates to the technical field of logistics transportation, in particular to an intelligent planning method and system for a logistics transportation line.
Background
Logistics transportation is an integral part of modern economic systems, mainly the whole process of safely and efficiently transporting goods from production or storage sites to destinations. With the rapid development of electronic commerce, the logistics transportation scale and complexity are continuously increased, and high requirements on logistics efficiency and response speed are put forward. The intelligent planning of the logistics transportation line is a key technology in the logistics transportation field, and the transportation line in the logistics network is optimized by utilizing an advanced algorithm and a data analysis technology so as to realize the transportation target with minimum cost and maximized efficiency.
In the traditional logistics transportation route intelligent planning, optimization algorithms such as particle swarm, ants, genetic optimization algorithms and the like are usually adopted, but the results of the optimization algorithms depend on modeling analysis of a real scene, namely the construction of a scene graph structure. In conventional optimization algorithms, a weighted undirected graph is usually created, i.e., each destination is regarded as a delivery node, and the delivery cost between two delivery nodes is regarded as a weight, so that the weighted undirected graph is constructed. In the weighted undirected graph, the weights between two connected delivery nodes are the same on a round trip line, but in the dynamic adaptability and multi-objective optimization considering the transportation route in the actual transportation process, the transportation cost of the two connected delivery nodes on the round trip is often different, so that the construction of the graph structure cannot reflect the actual characteristics of the scene, and a certain gap still exists between the optimal planning route obtained by the optimization algorithm and the actual optimal route. Therefore, aiming at the problems, the application provides an intelligent planning method and system for a logistics transportation route, which aim to construct an optimal undirected graph through scene analysis in a logistics transportation network and obtain an optimal route plan by combining an optimization algorithm.
Disclosure of Invention
In order to solve the technical problems, the application aims to provide an intelligent planning method and system for a logistics transportation line, and the adopted technical scheme is as follows:
In a first aspect, an embodiment of the present application provides an intelligent planning method for a logistics transportation line, including the following steps:
S10, collecting the horizontal height data of each sampling point between any two delivery nodes, and forming an elevation sequence between any two delivery nodes according to the order of the sampling points by using the horizontal height data of the sampling points between any two delivery nodes; collecting the number of the distribution nodes and the accumulated number of the distribution failures;
s20, acquiring a unidirectional fluctuation disorder coefficient between any two delivery nodes based on the horizontal height data in a high program sequence between any two delivery nodes, wherein the acquisition process of the unidirectional fluctuation disorder coefficient is as follows:
s21, acquiring elevation deviation coefficients between any two delivery nodes according to a high program sequence, and acquiring trend change coefficients between any two delivery nodes according to logistics delivery conditions of each delivery node;
S22, obtaining a unidirectional fluctuation disorder coefficient between any two distribution nodes according to the relation among the elevation deviation coefficient, the trend change coefficient and each level height data in the elevation sequence;
S30, obtaining optimal directional weights from each distribution node to other distribution nodes based on the unidirectional fluctuation disorder coefficients and distribution conditions of the distribution nodes, wherein the obtaining process of the optimal directional weights is as follows:
s31, obtaining a directional preference coefficient between any two delivery nodes according to the number of accessories of the delivery nodes, and obtaining a turn-back power ratio between any two delivery nodes according to the number of accessories and the accumulated number of delivery failures;
s32, obtaining the optimal directional weight from each distribution node to other distribution nodes according to the unidirectional fluctuation disorder coefficient, the directional preference coefficient and the turn-back success ratio;
and S40, constructing a weighted directed graph according to the optimal directed weight, and planning the logistics transportation route according to the weighted directed graph.
Further, the obtaining the elevation deviation coefficient between any two delivery nodes according to the high program sequence includes:
For an elevation sequence between any two delivery nodes, calculating the average value of all elements in the elevation sequence, calculating the absolute value of the difference value between each element in the elevation sequence and the average value, calculating the average value of all the absolute values of the difference values in the elevation sequence as a first average value, and taking the product of the first average value and the standard deviation of all the elements in the elevation sequence as an elevation deviation coefficient between any two delivery nodes.
Further, the obtaining the trend change coefficient between any two distribution nodes according to the logistics distribution condition of each distribution node includes:
And for the elevation sequence between any two delivery nodes, processing the high program sequence by using a fitting algorithm, obtaining a height curve between any two delivery nodes, calculating a first-order differential function of the height curve, and taking the sum of absolute values of function values corresponding to all horizontal height data of the first-order differential function in the elevation sequence as a trend change coefficient between any two delivery nodes.
Further, the obtaining the unidirectional fluctuation disorder coefficient between any two distribution nodes according to the relationship among the elevation deviation coefficient, the trend change coefficient and the data of each level in the elevation sequence comprises the following steps:
calculating the length of a height curve between any two distribution nodes by using a curve length calculation formula to serve as the actual distribution distance between any two distribution nodes;
For any two distribution nodes, calculating the sum of the elevation deviation coefficient, the trend change coefficient and the number 1 between the two distribution nodes as a first sum; calculating the approximate entropy of all the level height data in the elevation sequence between two delivery nodes, and calculating the calculation result of an exponential function taking the natural constant as the bottom and taking the negative value of the absolute value of the approximate entropy as the exponent as a first calculation result; calculating a ratio of a second calculation result to the first calculation result, wherein the second calculation result is a logarithmic function with a natural constant as a base and the first sum value is a true number, and taking the product of the actual distribution distances of two distribution nodes and the ratio as a unidirectional fluctuation disorder coefficient between any two distribution nodes.
Further, the obtaining the directional preference coefficient between any two delivery nodes according to the number of the accessories of the delivery nodes includes:
And for any distribution node, calculating the sum value of the number of accessories of all other distribution nodes connected with any distribution node as a second sum value, and calculating the ratio of the second sum value to the number of accessories of all other distribution nodes as a directional preference coefficient between any distribution node and all other distribution nodes.
Further, the turn-back power ratio has the formula:
In the method, in the process of the invention, A turn-back power ratio from the jth delivery node to the ith delivery node; Representing a preset foldback distribution coefficient to the ith distribution node; Representing the number of accumulated delivery failures from the total station to the ith delivery node, Indicating the total number of distribution nodes connected to the j-th distribution node,The number of the components of the (r) th delivery node connected to the (j) th delivery node in the present delivery is represented.
Further, the optimal directional weight is expressed as the following formula:
In the method, in the process of the invention, Indicating the best directional weight from the ith delivery node to the jth delivery node in the campus delivery,A one-way heave turbulence coefficient representing the ith and jth delivery nodes in the campus delivery,Representing a high program list between the ith and jth distribution nodesIs used for the correlation of the coefficients of the (c),Indicating the best directional weight from the jth delivery node to the ith delivery node in the campus delivery,An exponential function based on a natural constant is represented.
Further, the constructing a weighted directed graph according to the optimal directed weight includes:
and taking each distribution node as a node, and constructing a weighted directed graph according to the optimal directed weight from each distribution node to other distribution nodes.
Further, the planning the logistics transportation route according to the weighted directed graph comprises the following steps:
and taking the graph structure of the weighted directed graph as the input of an optimization algorithm, taking the minimum transportation cost as an objective function, and outputting the optimal distribution route.
In a second aspect, an embodiment of the present application further provides an intelligent logistics transportation line planning system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The application has at least the following beneficial effects:
According to the method, the elevation sequence among the distribution nodes is obtained by sampling the path information among the distribution nodes in the park, the elevation curve is obtained by curve fitting the elevation sequence, the unidirectional fluctuation disorder coefficient among the two distribution nodes is obtained by combining the two information, and the complexity degree of the path of the two distribution nodes and the horizontal elevation fluctuation condition are reflected; in addition, the directional preference coefficient is obtained by combining the number of the components from the current delivery node to the target delivery node and the total number of the components from the initial delivery node, the expected number of the components from the last delivery node is returned to obtain the return power ratio, the influence of the number of failed components on the delivery process in the component process is reflected, and finally, the optimal directional weight in two directions between the connected delivery nodes is obtained by combining the autocorrelation coefficient of the elevation sequence, so that a dynamic weighted directional graph is constructed, and the intelligent dynamic planning of the transport route of the logistics is realized according to an optimization algorithm. The method solves the defect that in the traditional path optimization algorithm, only an undirected graph constructed by combining environmental factors cannot accurately reflect the transportation direction and the actual constraint condition in the logistics network, so that deviation exists between the optimal route planning of the algorithm and the actual optimal route.
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In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent planning method for a logistics transportation route according to an embodiment of the present application;
FIG. 2 is a flowchart of the step of obtaining the one-way heave turbulence coefficient;
FIG. 3 is a flowchart of the best directed weight acquisition step.
Detailed Description
In order to further describe the technical means and effects adopted by the present application to achieve the preset purpose, the following detailed description refers to specific implementation, structure, characteristics and effects of a method and a system for intelligent planning of a logistics transportation line according to the present application, which are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
The application provides a logistics transportation line intelligent planning method and a system specific scheme by combining the drawings.
Referring to fig. 1, a flow chart of steps of a method for intelligently planning a logistics transportation route according to an embodiment of the present application is shown, the method includes the following steps:
s10, collecting the horizontal height data of each sampling point between any two delivery nodes, and forming an elevation sequence between any two delivery nodes according to the order of the sampling points by using the horizontal height data of the sampling points between any two delivery nodes; the number of the components of each distribution node and the number of the accumulated distribution failures are collected.
In this embodiment, taking the mass flow distribution of a large-scale park as an example, there is a total station and a plurality of distribution nodes in the large-scale park, so as to transport the goods of the total station to the plurality of distribution nodes, the total number of the total station and the distribution nodes is assumed to be。
In order to acquire the path information of each delivery node in the delivery network, the embodiment adopts an intelligent plotting vehicle with a GPS receiver to acquire the path information among the delivery nodes, samples the level information of the road surface at equal intervals on the path among the connected delivery nodes, sets the sampling interval to be 5m, acquires the high-precision level data among the delivery nodes in the park, takes the sequence consisting of the level data of all the sampling points among any two delivery nodes as the elevation sequence among any two delivery nodes, and usesRepresenting an elevation sequence between the ith and jth delivery nodes.
Further, the number of the components and the number of the accumulated delivery failure of each delivery node are obtained in the delivery system. The number of failed delivery is the number of failed delivery pieces which are not taken by a receiver after reaching a delivery node, and the accumulated number of failed delivery of each delivery node is the sum of the numbers of failed delivery of each delivery node and all delivery nodes before the delivery node.
In order to reduce the influence of noise on the structural analysis of a subsequent graph, the embodiment adopts a filtering algorithm to carry out data filtering on the obtained high program column.
It should be noted that, in this embodiment, the kalman filtering algorithm is adopted for processing, where the filtering algorithm includes median filtering, gaussian filtering, wavelet filtering, butterworth filtering, and the like, and an implementer may select other filtering methods according to actual situations, where the kalman filtering algorithm is a known technology, and this embodiment is not repeated.
S20, obtaining a unidirectional fluctuation disorder coefficient between any two distribution nodes based on the horizontal height data in the high program column between any two distribution nodes.
The selection of the optimal route in the logistics transportation route planning depends on the construction of the graph structure. Most of these are often built as undirected graphs, which have the disadvantage of not being able to accurately reflect the transport direction and the actual constraints in the logistics network. In the actual transportation process, the transportation cost is unequal between the connected delivery nodes in the round trip process in consideration of factors such as transportation efficiency, road condition limitation and the like. Therefore, the direction of each side of the directed graph is specified, and the unidirectional transportation path and various constraints in the logistics network can be truly reflected, so that how to construct an effective directed graph structure is the key point of the embodiment.
Because the larger terrain of the park is relatively complex, the connected two delivery nodes contain complex road conditions, so that the distance between the two delivery nodes cannot be simply taken as the weight of the edge, and the complexity of the route needs to be considered. According to the elevation sequence acquired in step S10, the embodiment uses a high program sequence between two connected distribution nodesFor example, where each value in the sequence represents the horizontal height of the sample point.
In order to analyze the fluctuation complexity of the transportation route between two distribution nodes, a high program list is arrangedAs input to a polynomial fitting algorithm, the algorithm outputs a height curve between two delivery nodes usingAnd (3) representing. To obtain the smoothness of the route between two delivery nodes, a high program list between two delivery nodes is calculatedThe approximate entropy of all elements in the system is used forAnd (3) representing. The length of the height curve between any two delivery nodes by using the curve length calculation formula is recorded as the length of the height curve between the ith delivery node and the jth delivery nodeAnd willAs an actual delivery distance between the ith delivery node and the jth delivery node; a first order derivative function of the height curve between any two delivery nodes is calculated.
The polynomial fitting algorithm, the method for calculating the approximate entropy and the curve length calculation formula are known techniques, and are not described in detail in this embodiment. The unidirectional fluctuation disorder coefficient is obtained by combining the elevation sequence and the elevation curve, and the formula is as follows:
In the method, in the process of the invention, Representing an elevation deviation factor between an ith and jth delivery node in the campus delivery; representing the sequence of elevations between the ith and jth delivery nodes, AndRepresenting the calculation of standard deviation and mean values of the ith and jth dispensing node elevation sequences,Representing the number of sampling points between the ith and jth distribution nodes,Representing the kth level value in the sequence of elevations between the ith and jth delivery nodes.
A trend coefficient representing the ith and jth delivery nodes in the campus delivery; a function value of a first order derivative function representing the height curve between the ith and jth dispense nodes at the kth sample position.
A one-way heave turbulence coefficient representing the ith and jth delivery nodes in the campus delivery; representing the ApEn values that calculate the elevation sequence between the ith and jth delivery nodes, Represents a logarithmic function based on a natural constant e,An exponential function based on a natural constant e is represented. The flow chart of the unidirectional fluctuation disorder coefficient acquisition step is shown in fig. 2.
If the distribution route between the ith and the jth distribution nodes in the park is relatively flat and does not fluctuate much, the road condition gradient between the distribution nodes is smaller, the road condition is better, the value in the elevation sequence fluctuates in a small range of the mean value, and the standard deviation of the whole high-program sequence is smaller, so that the elevation deviation coefficient is causedThe value of (2) is small. Meanwhile, the whole height curve is gentle, so that the first-order differentiation sum approaches zero on each sampling point to ensure that the trend change coefficientIn addition, the whole sequence of the high program is stable, the irregularity of the whole sequence is low, and the method is obtainedThe value of (2) is smaller, and finally the unidirectional fluctuation disorder coefficient is obtainedThe value of (2) is small. Conversely, if the distribution route between the ith and jth distribution nodes is complex, such as uneven, more ascending or more descending, the deviation between each element of the high program sequence and the average value is larger, and the standard deviation of the whole sequence is larger, so thatThe value of (2) is increased, and the trend term of the whole sequence is larger due to the larger fluctuation of the whole sequence, more uphill slopes or more downhill slopes, so thatIs increased to finally lead to the unidirectional fluctuation disorder coefficientThe value of (2) is larger.
S30, obtaining the optimal directional weight from each distribution node to other distribution nodes based on the unidirectional fluctuation disorder coefficient and the distribution condition of each distribution node.
By unidirectional heave turbulence coefficientThe road condition between two connected distribution nodes can be reflected, the smaller the fluctuation of the road surface is, the better the road condition is, and the lower the cost is in transportation; when the fluctuation of the road surface is large, the road condition is poor, and the transportation cost is high. In addition, in the logistics distribution process of the park, the goods at each distribution node cannot be completely successfully distributed, and the possibility that the current distribution node is not available for the pickup person and the pickup cannot be performed may exist. Therefore, when the next delivery node is delivered, the delivery cost is considered, and the picking sequence may return to the previous delivery node for delivery, and dynamic adjustment of route re-planning is performed. The turn-back power ratio is obtained, and the optimal directional weight is obtained based on the turn-back power ratio:
In the method, in the process of the invention, A directional preference coefficient representing a direction preference coefficient from an ith delivery node to a jth delivery node in the campus delivery; representing the number of components of the jth delivery node in the current delivery process, Indicating the total number of distribution nodes connected to the i-th distribution node,The number of the components of the (r) th delivery node connected to the (i) th delivery node in the present delivery is represented.
A turn-back power ratio from the jth delivery node to the ith delivery node; A foldback distribution coefficient representing the distribution node to the i-th distribution node is set in the present embodiment ；Representing the number of accumulated delivery failures from the total station to the ith delivery node,Indicating the total number of distribution nodes connected to the j-th distribution node,The number of the components of the (r) th delivery node connected to the (j) th delivery node in the present delivery is represented.
Indicating the best directional weight from the ith delivery node to the jth delivery node in the campus delivery,A one-way heave turbulence coefficient representing the ith and jth delivery nodes in the campus delivery,Representing a high program list between the ith and jth distribution nodesIs used for the correlation of the coefficients of the (c),Indicating the best directional weight from the jth delivery node to the ith delivery node in the campus delivery,An exponential function based on a natural constant e is represented. The method for calculating the autocorrelation coefficients between sequences is known in the art, and will not be described in detail herein. The flowchart of the best directed weight obtaining step is shown in fig. 3.
If the value of the autocorrelation coefficient of the high program sequence between the ith delivery node and the jth delivery node is larger than zero, indicating that the whole path from the ith delivery node to the jth delivery node shows an ascending trend, and the transportation cost is larger due to the ascending trend of the horizontal height, if the number of delivered goods at the jth delivery node is smaller than the total number of delivered goods at other delivery nodes connected with the ith delivery node, the directional preference coefficient is obtainedThe value of (2) is larger; in addition, due to the influence of the rising trend between two distribution nodes, the unidirectional fluctuation disorder coefficientThe value of (2) is larger, and the optimal directional weight from i to j is finally obtainedThe value of (2) is larger. Conversely, if the value of the autocorrelation coefficient of the high program sequence between the ith delivery node and the jth delivery node is smaller than zero, it indicates that the descending trend is shown from the jth delivery node to the ith delivery node at the moment, and the transportation cost from j to i is lower because the descending trend is shown by the horizontal height; meanwhile, if the number of the re-distribution success numbers of the return i-th distribution node is larger than the total number of the rest distribution nodes of the j-th distribution node, the return success ratio is obtainedThe value of (2) is large, and the one-way fluctuation disorder coefficient is highThe value of (2) is larger, and the optimal directional weight from j to i is finally obtainedThe value of (2) is small.
By the method, the unidirectional fluctuation disorder coefficient of each distribution node can be used as the static weight between every two connected distribution nodes, and the optimal directional weight is used as the real-time dynamic weight in the transportation process. If the weight value from the ith delivery node to the jth delivery node is larger, the transportation cost from the ith delivery node to the jth delivery node is larger; conversely, the smaller the weight from the ith delivery node to the jth delivery node, the smaller the transportation cost from the ith delivery node to the jth delivery node, and the smaller the energy consumed by the transportation of the unmanned intelligent delivery trolley. And the weight of each directed edge can be dynamically adjusted according to the information of the failed delivery piece in the transportation process, so that the construction of the directed graph with the weight of each delivery node for logistics delivery in the large-scale park is realized.
And S40, constructing a weighted directed graph according to the optimal directed weight, and planning the logistics transportation route according to the weighted directed graph.
According to the optimal directional weight among all the connected delivery nodes, the distribution system can be adjusted according to the real-time delivery state. The obtained graph structure of the weighted directed graph is used as input of a particle swarm optimization algorithm, the minimum transportation cost is used as an objective function, the number of initialized particles is set to 20, the maximum iteration number is 100, the inertia weight is set to 0.9, the number of iterations is reduced linearly to 0.4, the individual learning factors and the social learning factors are set to 1.5 and 2.0 respectively, and finally, the optimal distribution route of each unmanned intelligent distribution trolley can be dynamically adjusted and intelligently planned according to the distribution condition of the unmanned intelligent distribution trolley, so that the distribution cost is saved to the greatest extent while the efficiency is improved.
Based on the same inventive concept as the above method, the embodiment of the application also provides an intelligent logistics transportation route planning system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the above intelligent logistics transportation route planning methods when executing the computer program.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present application are intended to be included within the scope of the present application.
Claims (6)
1. An intelligent planning method for a logistics transportation line is characterized by comprising the following steps:
S10, collecting the horizontal height data of each sampling point between any two delivery nodes, and forming an elevation sequence between any two delivery nodes according to the order of the sampling points by using the horizontal height data of the sampling points between any two delivery nodes; collecting the number of the distribution nodes and the accumulated number of the distribution failures;
s20, acquiring a unidirectional fluctuation disorder coefficient between any two delivery nodes based on the horizontal height data in a high program sequence between any two delivery nodes, wherein the acquisition process of the unidirectional fluctuation disorder coefficient is as follows:
s21, acquiring elevation deviation coefficients between any two delivery nodes according to a high program sequence, and acquiring trend change coefficients between any two delivery nodes according to logistics delivery conditions of each delivery node;
S22, obtaining a unidirectional fluctuation disorder coefficient between any two distribution nodes according to the relation among the elevation deviation coefficient, the trend change coefficient and each level height data in the elevation sequence;
S30, obtaining optimal directional weights from each distribution node to other distribution nodes based on the unidirectional fluctuation disorder coefficients and distribution conditions of the distribution nodes, wherein the obtaining process of the optimal directional weights is as follows:
s31, obtaining a directional preference coefficient between any two delivery nodes according to the number of accessories of the delivery nodes, and obtaining a turn-back power ratio between any two delivery nodes according to the number of accessories and the accumulated number of delivery failures;
s32, obtaining the optimal directional weight from each distribution node to other distribution nodes according to the unidirectional fluctuation disorder coefficient, the directional preference coefficient and the turn-back success ratio;
s40, constructing a weighted directed graph according to the optimal directed weight, and planning a logistics transportation line according to the weighted directed graph;
The step of obtaining the elevation deviation coefficient between any two distribution nodes according to the high program sequence comprises the following steps:
For an elevation sequence between any two delivery nodes, calculating the average value of all elements in the elevation sequence, calculating the absolute value of the difference value between each element in the elevation sequence and the average value, calculating the average value of all the absolute values of the difference values in the elevation sequence as a first average value, and taking the product of the first average value and the standard deviation of all the elements in the elevation sequence as an elevation deviation coefficient between any two delivery nodes;
The obtaining the trend change coefficient between any two distribution nodes according to the logistics distribution condition of each distribution node comprises the following steps:
For an elevation sequence between any two delivery nodes, a fitting algorithm is used for processing the high program sequence, a height curve between any two delivery nodes is obtained, a first-order differential function of the height curve is calculated, and the sum of absolute values of function values corresponding to all horizontal height data of the first-order differential function in the elevation sequence is used as a trend change coefficient between any two delivery nodes;
The method for obtaining the unidirectional fluctuation disorder coefficient between any two distribution nodes according to the relation among the elevation deviation coefficient, the trend change coefficient and each level height data in the elevation sequence comprises the following steps:
calculating the length of a height curve between any two distribution nodes by using a curve length calculation formula to serve as the actual distribution distance between any two distribution nodes;
For any two distribution nodes, calculating the sum of the elevation deviation coefficient, the trend change coefficient and the number 1 between the two distribution nodes as a first sum; calculating the approximate entropy of all the level height data in the elevation sequence between two delivery nodes, and calculating the calculation result of an exponential function taking the natural constant as the bottom and taking the negative value of the absolute value of the approximate entropy as the exponent as a first calculation result; calculating a ratio of a second calculation result to the first calculation result, wherein the second calculation result is a logarithmic function with a natural constant as a base and the first sum value is a true number, and taking a product of the actual distribution distances of two distribution nodes and the ratio as a unidirectional fluctuation disorder coefficient between any two distribution nodes;
The obtaining the directional preference coefficient between any two distribution nodes according to the number of the accessories of the distribution nodes comprises the following steps:
And for any distribution node, calculating the sum value of the number of accessories of all other distribution nodes connected with any distribution node as a second sum value, and calculating the ratio of the second sum value to the number of accessories of all other distribution nodes as a directional preference coefficient between any distribution node and all other distribution nodes.
2. The intelligent planning method for a logistics transportation line according to claim 1, wherein the turn-back power ratio is as follows:
In the method, in the process of the invention, A turn-back power ratio from the jth delivery node to the ith delivery node; Representing a preset foldback distribution coefficient to the ith distribution node; Representing the number of accumulated delivery failures from the total station to the ith delivery node, Indicating the total number of distribution nodes connected to the j-th distribution node,The number of the components of the (r) th delivery node connected to the (j) th delivery node in the present delivery is represented.
3. The intelligent logistics transportation route planning method according to claim 1, wherein the optimal directional weight is as follows:
In the method, in the process of the invention, Indicating the best directional weight from the ith delivery node to the jth delivery node in the campus delivery,A one-way heave turbulence coefficient representing the ith and jth delivery nodes in the campus delivery,Representing a high program list between the ith and jth distribution nodesIs used for the correlation of the coefficients of the (c),Indicating the best directional weight from the jth delivery node to the ith delivery node in the campus delivery,An exponential function based on a natural constant is represented.
4. The intelligent logistics transportation route planning method of claim 1, wherein the constructing a weighted directed graph according to the optimal directed weight comprises:
and taking each distribution node as a node, and constructing a weighted directed graph according to the optimal directed weight from each distribution node to other distribution nodes.
5. The intelligent logistics transportation route planning method of claim 1, wherein the logistics transportation route planning according to the weighted directed graph comprises:
and taking the graph structure of the weighted directed graph as the input of an optimization algorithm, taking the minimum transportation cost as an objective function, and outputting the optimal distribution route.
6. A logistics transportation route intelligent planning system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the method of any one of claims 1-5.
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