CN116245449A - Low-carbon logistics distribution method, device and equipment - Google Patents

Abstract

The application provides a low-carbon logistics distribution method, device and equipment, and relates to the technical field of logistics distribution, wherein the method comprises the following steps: responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points; acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes; according to the distribution information, carrying out iterative operation on the initial distribution matrix to obtain a matrix to be selected; determining a target row vector in M row vectors included in a matrix to be selected, wherein the target row vector is used for indicating a target distribution scheme; and sending the target distribution scheme to the client. According to the scheme, the logistics distribution efficiency is improved.

Description

Low-carbon logistics distribution method, device and equipment

Technical Field

The application relates to the technical field of logistics distribution, in particular to a low-carbon logistics distribution method, a low-carbon logistics distribution device and low-carbon logistics distribution equipment.

Background

Modern logistics in the current society is deeply permeated into the daily life of people, and great convenience is provided for the daily life of people.

To promote the development and progress of society, the demands of enterprises on logistics distribution are becoming larger, and the demands of consumers on the quality of service of logistics distribution are also becoming higher. Although there has been a rapid development in logistics transportation in recent years, it is still difficult to meet the logistics demand of the rapid development compared with the demand of economic development. At present, in the case of logistics distribution, when goods need to be transported from a distribution center to a plurality of distribution destinations, the order of distribution among the plurality of distribution destinations is usually determined manually by a delivery person, and the efficiency of determining the logistics distribution mode manually is low, so that problems such as long bypass, large resource consumption, long time consumption and the like are easy to occur. Accordingly, there is a need to provide a low carbon logistics distribution scheme to improve logistics distribution efficiency.

Disclosure of Invention

The application provides a low-carbon logistics distribution method, device and equipment, which are used for solving the problem of low logistics distribution efficiency at present.

In a first aspect, the present application provides a low-carbon logistics distribution method, applied to a terminal device, the method including:

responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points;

Acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, wherein the distribution schemes are sequences starting from a distribution center and traversing each distribution destination, and M is a positive integer;

according to the distribution information, carrying out iterative operation on the initial distribution matrix to obtain a matrix to be selected, wherein M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected;

determining a target row vector in M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme;

and sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

In a possible implementation manner, the performing an iterative operation on the initial distribution matrix according to the distribution information to obtain a to-be-selected distribution matrix includes:

performing a first operation, the first operation comprising: determining a row vector to be selected in a distribution matrix after the i-1 th iteration according to the distribution information; updating the distribution matrix after the i-1 th iteration according to the row vector to be selected to obtain the distribution matrix after the i-1 th iteration; the i is 1 initially, and the distribution matrix after the 0 th iteration is the initial distribution matrix;

When i is smaller than K, updating i to be i+1, and repeatedly executing the first operation, wherein K is a preset iteration number, and K is a positive integer larger than 1;

and when the i is equal to the K, determining the distribution matrix after the K iteration as the to-be-selected distribution matrix.

In a possible implementation manner, the determining the candidate row vector in the delivery matrix after the i-1 th iteration according to the delivery information includes:

determining delivery data of the plurality of delivery points according to the positions of the delivery points and the path information between any two delivery points, wherein the delivery data comprises the transportation distance and the transportation time between any two delivery points;

according to the distribution data, determining the respective fitness of M row vectors in the distribution matrix after the i-1 th iteration;

and determining the row vector to be selected in the delivery matrix after the i-1 th iteration according to the respective fitness of M row vectors in the delivery matrix after the i-1 th iteration.

In a possible implementation manner, the determining the respective fitness of M row vectors in the delivery matrix after the i-1 th iteration according to the delivery data includes:

Determining a traversing path corresponding to an arbitrary row vector in M row vectors in the i-1 th iterated distribution matrix, wherein the traversing path is a path which starts from the distribution center and traverses each distribution destination in a distribution scheme indicated by the row vector;

according to the distribution data, determining the energy consumption and the time consumption corresponding to the traversal path;

and determining the fitness of the row vector according to the energy consumption and the time consumption.

In one possible implementation, the traversal path includes a plurality of sub-paths; the determining the energy consumption and the time consumption corresponding to the traversal path according to the distribution data comprises the following steps:

determining the length, the driving energy consumption rate, the idle energy consumption rate and the waiting time of each sub-path according to the distribution data;

determining the energy consumption corresponding to the traversal path according to the length of each sub-path, the driving energy consumption rate and the idle speed energy consumption rate;

and determining the time consumption corresponding to the traversing path according to the length of each sub-path and the waiting time.

In one possible implementation manner, any row vector in the distribution matrix includes N elements, where the order of the N elements is used to indicate the order of traversing the corresponding distribution destinations, N is the number of the plurality of distribution destinations, and N is a positive integer; updating the delivery matrix after the i-1 th iteration according to the row vector to be selected to obtain the delivery matrix after the i-1 th iteration, wherein the method comprises the following steps of:

determining a first arrangement sequence of N elements included in the row vector to be selected;

determining a second arrangement sequence of N elements included in the row vector for any row vector in the distribution matrix after the i-1 th iteration;

updating N elements included in the row vector according to the first arrangement sequence and the second arrangement sequence to obtain an updated row vector;

and obtaining the distribution matrix after the ith iteration according to the updated row vector.

In a possible implementation manner, the updating the N elements included in the row vector according to the first arrangement order and the second arrangement order to obtain an updated row vector includes:

determining a first number of elements in the first arrangement order and the second arrangement order, wherein the elements are different in arrangement order;

Determining a second number of update elements according to the first number, the second number being smaller than the first number;

and updating N elements included in the row vector according to the second quantity to obtain the updated row vector.

In a possible implementation manner, the determining a target row vector in M row vectors included in the candidate allocation matrix includes:

according to the distribution data, determining the respective fitness of M row vectors in the to-be-selected distribution matrix;

and determining the target row vector in the to-be-selected distribution matrix according to the respective fitness of M row vectors in the to-be-selected distribution matrix.

In a second aspect, the present application provides a low carbon logistics distribution apparatus comprising:

the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for responding to a delivery instruction sent by a client and acquiring delivery information of a plurality of delivery points in a preset area, the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points;

the system comprises an acquisition module, a distribution center and a distribution destination, wherein the acquisition module is used for acquiring an initial distribution matrix, M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, the distribution schemes are sequences starting from the distribution center and traversing the distribution destinations, and M is a positive integer;

The processing module is used for carrying out iterative operation on the initial distribution matrix according to the distribution information to obtain a matrix to be selected, and M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected;

the determining module is used for determining a target row vector in M row vectors included in the to-be-selected distribution matrix, and the target row vector is used for indicating a target distribution scheme;

and the sending module is used for sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

In a possible implementation manner, the processing module is specifically configured to:

performing a first operation, the first operation comprising: determining a row vector to be selected in a distribution matrix after the i-1 th iteration according to the distribution information; updating the distribution matrix after the i-1 th iteration according to the row vector to be selected to obtain the distribution matrix after the i-1 th iteration; the i is 1 initially, and the distribution matrix after the 0 th iteration is the initial distribution matrix;

when i is smaller than K, updating i to be i+1, and repeatedly executing the first operation, wherein K is a preset iteration number, and K is a positive integer larger than 1;

And when the i is equal to the K, determining the distribution matrix after the K iteration as the to-be-selected distribution matrix.

In a possible implementation manner, the processing module is specifically configured to:

determining delivery data of the plurality of delivery points according to the positions of the delivery points and the path information between any two delivery points, wherein the delivery data comprises the transportation distance and the transportation time between any two delivery points;

according to the distribution data, determining the respective fitness of M row vectors in the distribution matrix after the i-1 th iteration;

and determining the row vector to be selected in the delivery matrix after the i-1 th iteration according to the respective fitness of M row vectors in the delivery matrix after the i-1 th iteration.

In a possible implementation manner, the processing module is specifically configured to:

determining a traversing path corresponding to an arbitrary row vector in M row vectors in the i-1 th iterated distribution matrix, wherein the traversing path is a path which starts from the distribution center and traverses each distribution destination in a distribution scheme indicated by the row vector;

according to the distribution data, determining the energy consumption and the time consumption corresponding to the traversal path;

And determining the fitness of the row vector according to the energy consumption and the time consumption.

In one possible implementation, the traversal path includes a plurality of sub-paths; the processing module is specifically configured to:

determining the length, the driving energy consumption rate, the idle energy consumption rate and the waiting time of each sub-path according to the distribution data;

determining the energy consumption corresponding to the traversal path according to the length of each sub-path, the driving energy consumption rate and the idle speed energy consumption rate;

and determining the time consumption corresponding to the traversing path according to the length of each sub-path and the waiting time.

In one possible implementation manner, any row vector in the distribution matrix includes N elements, where the order of the N elements is used to indicate the order of traversing the corresponding distribution destinations, N is the number of the plurality of distribution destinations, and N is a positive integer; the processing module is specifically configured to:

determining a first arrangement sequence of N elements included in the row vector to be selected;

determining a second arrangement sequence of N elements included in the row vector for any row vector in the distribution matrix after the i-1 th iteration;

Updating N elements included in the row vector according to the first arrangement sequence and the second arrangement sequence to obtain an updated row vector;

and obtaining the distribution matrix after the ith iteration according to the updated row vector.

In a possible implementation manner, the processing module is specifically configured to:

determining a first number of elements in the first arrangement order and the second arrangement order, wherein the elements are different in arrangement order;

determining a second number of update elements according to the first number, the second number being smaller than the first number;

and updating N elements included in the row vector according to the second quantity to obtain the updated row vector.

In one possible implementation manner, the determining module is specifically configured to:

according to the distribution data, determining the respective fitness of M row vectors in the to-be-selected distribution matrix;

and determining the target row vector in the to-be-selected distribution matrix according to the respective fitness of M row vectors in the to-be-selected distribution matrix.

In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the low carbon logistics distribution method of any of the first aspects when executing the program.

In a fourth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor implements the low carbon logistics distribution method of any of the first aspects.

The low-carbon logistics distribution method, the low-carbon logistics distribution device and the low-carbon logistics distribution equipment are characterized in that distribution information of a plurality of distribution points in a preset area is collected in response to a distribution instruction sent by a client, the plurality of distribution points comprise a distribution center and a plurality of distribution destinations, and the distribution information comprises the position of each distribution point and the path information between any two distribution points; then acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, the distribution schemes are sequences which start from a distribution center and traverse each distribution destination, and M is a positive integer; after the initial distribution matrix is obtained, performing iterative operation on the initial distribution matrix according to distribution information to obtain a to-be-selected distribution matrix, wherein M row vectors included in the to-be-selected distribution matrix are used for indicating M different to-be-selected distribution schemes, and finally, determining a target row vector in the M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme and sending the target distribution scheme to a client, and the target distribution scheme comprises a target sequence which starts from a distribution center and traverses each distribution destination. Since the matrix to be selected is obtained by performing iterative operation on the initial distribution matrix according to the distribution information, the distribution information includes the positions of the distribution points and the path information between any two distribution points, after the initial distribution matrix is subjected to iterative operation by the distribution information, the M row vectors in the obtained matrix to be selected indicate that the scheme to be selected is relatively short in time spent and relatively high in efficiency. Thus, a target row vector determined among M row vectors included in the candidate dispensing matrix may be used to indicate a target dispensing scheme. And traversing each delivery destination from the delivery center based on the target delivery scheme indicated by the target row vector. The initial distribution matrix is subjected to iterative operation based on the distribution information, the distribution matrix can be continuously optimized, the target distribution scheme can be finally determined by combining the distribution information well and is sent to the client, a user is guided to carry out logistics distribution, the logistics distribution scheme under the conditions of less carbon emission and short transportation time is provided, and the logistics distribution efficiency is improved.

Drawings

For a clearer description of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;

fig. 2 is a schematic flow chart of a low-carbon logistics distribution method according to an embodiment of the present application;

fig. 3 is a schematic flow chart of a low-carbon logistics distribution method according to an embodiment of the present application;

FIG. 4 is a schematic diagram showing a comparison of the flow distribution scheme under different algorithms according to the embodiments of the present application;

FIG. 5 is a second schematic diagram of a comparison of the flow distribution scheme under different algorithms provided in the embodiments of the present application;

FIG. 6 is a third schematic diagram of a comparison of the flow distribution scheme under different algorithms provided in the embodiments of the present application;

FIG. 7 is a schematic structural diagram of a low-carbon logistics distribution apparatus according to an embodiment of the present application;

fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

Modern logistics in the current society is deeply permeated into the daily life of people, and great convenience is provided for the daily life of people.

To promote the development and progress of society, the demands of enterprises on logistics distribution are becoming larger, and the demands of consumers on the quality of service of logistics distribution are also becoming higher. Although there has been a rapid development in logistics transportation in recent years, it is still difficult to meet the logistics demand of the rapid development compared with the demand of economic development. Therefore, it is necessary to develop a cargo distribution scheme with a small carbon emission and a short transportation time to improve logistics distribution efficiency. In particular, in the formulation of a large-scale cargo transportation scheme for electric coal, the reduction degree of fuel oil cost, carbon emission and transportation time is more remarkable.

Currently, logistics distribution schemes are usually solved by heuristic algorithms. Heuristic algorithms are algorithms based on visual or empirical construction that give a feasible solution to each instance of the combinatorial optimization problem to be solved at acceptable time and space costs, the degree of deviation of which from the optimal solution is generally not predictable. At present, heuristic algorithms mainly comprise an ant colony algorithm, a simulated annealing algorithm, a moth fire suppression algorithm and the like. The moth fire suppression algorithm is a heuristic algorithm which is inspired by the behaviors of a moth special navigation mode, and the moth fire suppression algorithm based on dynamic self-adaptive inertia weight is provided for enhancing the local search performance of the moth fire suppression algorithm. By introducing the objective function value into the inertia weight, the inertia weight is dynamically changed along with the change of the objective function value, so that blindness of the change of the inertia weight is reduced, and the global exploration capacity and the local development capacity of the algorithm are effectively balanced.

Based on this, the embodiment of the application provides a low-carbon logistics distribution scheme to achieve the purposes of reducing carbon emission and reducing transportation time. The following will describe aspects of the present application with reference to the accompanying drawings.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application, as shown in fig. 1, including a distribution center 10, and a plurality of distribution destinations (3 are illustrated in fig. 1, namely, a distribution destination 11, a distribution destination 12, and a distribution destination 13).

There are a plurality of goods at the distribution center 10, which need to be distributed to these 3 destinations, respectively. From the distribution center 10, it is necessary to reach the distribution destination 11, the distribution destination 12, and the distribution destination 13, respectively, and then return to the distribution center 10. The distance between the distribution center 10 and each distribution destination and the road condition are different, so how to determine the logistics distribution scheme can reduce the carbon emission to the greatest extent, save the distribution time and solve the problem to be solved urgently.

Based on this, the embodiment of the application provides a low-carbon logistics distribution method, which is used for solving the technical problems. The scheme of the embodiment of the application can be applied to the fields of electric coal transportation and the like, or other fields possibly related to logistics distribution.

Fig. 2 is a schematic flow chart of a low-carbon logistics distribution method according to an embodiment of the present application, as shown in fig. 2, the method may include:

S21, responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points.

The preset area comprises a plurality of delivery points, the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, the delivery center comprises a certain amount of goods, and the goods need to be delivered to each delivery destination.

In the delivery process, the delivery center needs to be started, each delivery destination is routed, and then the delivery center is returned. Since there are a plurality of distribution destinations, the route to be taken differs depending on the order of the distribution destinations from the distribution center.

In order to improve the distribution efficiency, a user operates a client and sends a distribution instruction to a terminal device. The terminal equipment responds to the delivery instruction sent by the client, and can acquire delivery information, wherein the delivery information comprises the position of each delivery point and the path information between any two delivery points. The positions of the delivery points comprise the positions of the delivery centers and the positions of the delivery destinations. The route information between two delivery points may include a route between two delivery points, a road condition, and the like, where the route is a route from one delivery point to another delivery point, and the road condition may include traffic lights, traffic flows, and the like included in the corresponding route. In this embodiment of the present application, the route information between any two distribution points includes route information between the distribution center and each distribution destination, and further includes route information between any two distribution destinations.

S22, acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, wherein the distribution schemes are sequences which start from a distribution center and traverse each distribution destination, and M is a positive integer.

The initial distribution matrix is a matrix with a dimension of m×n, that is, M rows and N columns, where M and N are positive integers greater than 1. For each row in the initial distribution matrix, one row vector may be formed, so that the initial distribution matrix includes M row vectors.

For any one row vector in the initial distribution matrix, the row vector includes N elements, where N is also the number of multiple distribution destinations. Taking the example that 5 distribution destinations are included in the preset area, n=5. Each of the N elements corresponds to a delivery destination, and the order of the N elements in the row vector indicates the order in which the delivery destinations are traversed from the delivery center. Taking 1 for the 1 st delivery destination, 2 for the 2 nd delivery destination, 3 for the 3 rd delivery destination, 4 for the 4 th delivery destination, and 5 for the 5 th delivery destination as examples, if a certain row vector is [5,3,2,4,1], the initial delivery scheme indicated by the row vector is: from the delivery center, the 5 th delivery destination is reached first, then the 5 th delivery destination is moved to the 3 rd delivery destination, then the 3 rd delivery destination is moved to the 2 nd delivery destination, then the 2 nd delivery destination is moved to the 4 th delivery destination, then the 4 th delivery destination is moved to the 1 st delivery destination, and finally the delivery center is returned. Thus, M row vectors in the initial distribution matrix may be used to indicate M different initial distribution schemes.

S23, carrying out iterative operation on the initial distribution matrix according to the distribution information to obtain a matrix to be selected, wherein M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected.

The distribution information includes the position of each distribution point and the distance information between any two distribution points. After the distribution information is obtained, iterative operation can be performed on the initial distribution matrix according to the distribution information, and the iterative operation process is to update the sequence of elements in M row vectors in the initial distribution matrix, so as to obtain a matrix to be selected.

The dimension of the candidate distribution matrix is identical to the initial distribution matrix, and is also m×n. The to-be-selected distribution matrix comprises M row vectors, each row vector comprises N elements, and the sequence of the N elements is used for indicating the sequence of starting from a distribution center and traversing each distribution destination. Thus, the M row vectors included in the candidate dispensing matrix are used to indicate M different candidate dispensing schemes.

S24, determining a target row vector in M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme.

After the candidate dispensing matrix is obtained, a target row vector can be determined from M row vectors included in the candidate dispensing matrix, where the target row vector includes N elements, and an order of the N elements is used to indicate a traversal order among the N dispensing destinations.

And S25, sending a target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from a delivery center and traverses each delivery destination.

After the terminal device determines the target row vector, a target delivery scheme can be determined according to the target row vector, and the target delivery scheme is sent to the client, wherein the target delivery scheme comprises a target sequence which starts from a delivery center and traverses each delivery destination. After receiving the target delivery scheme, the client guides the user to carry out logistics delivery according to the target sequence of each delivery destination from the delivery center in the target delivery scheme.

According to the low-carbon logistics distribution method, distribution information of a plurality of distribution points in a preset area is collected in response to a distribution instruction sent by a client, the distribution points comprise distribution centers and a plurality of distribution destinations, and the distribution information comprises the position of each distribution point and the path information between any two distribution points; then acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, the distribution schemes are sequences which start from a distribution center and traverse each distribution destination, and M is a positive integer; after the initial distribution matrix is obtained, performing iterative operation on the initial distribution matrix according to distribution information to obtain a to-be-selected distribution matrix, wherein M row vectors included in the to-be-selected distribution matrix are used for indicating M different to-be-selected distribution schemes, and finally, determining a target row vector in the M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme and sending the target distribution scheme to a client, and the target distribution scheme comprises a target sequence which starts from a distribution center and traverses each distribution destination. Since the matrix to be selected is obtained by performing iterative operation on the initial distribution matrix according to the distribution information, the distribution information includes the positions of the distribution points and the path information between any two distribution points, after the initial distribution matrix is subjected to iterative operation by the distribution information, the M row vectors in the obtained matrix to be selected indicate that the scheme to be selected is relatively short in time spent and relatively high in efficiency. Thus, a target row vector determined among M row vectors included in the candidate dispensing matrix may be used to indicate a target dispensing scheme. And traversing each delivery destination from the delivery center based on the target delivery scheme indicated by the target row vector. The initial distribution matrix is subjected to iterative operation based on the distribution information, the distribution matrix can be continuously optimized, the target distribution scheme can be finally determined by combining the distribution information well and is sent to the client, a user is guided to carry out logistics distribution, the logistics distribution scheme under the conditions of less carbon emission and short transportation time is provided, and the logistics distribution efficiency is improved.

On the basis of any of the above embodiments, the following describes the scheme of the present application in detail with reference to the accompanying drawings.

After the initial distribution matrix is obtained, iterative operation can be performed on the initial distribution matrix according to the distribution information so as to obtain a to-be-selected distribution matrix.

Fig. 3 is a schematic flow chart of a low-carbon logistics distribution method according to an embodiment of the present application, as shown in fig. 3, including:

s31, initializing setting parameters.

And numbering N distribution destinations, and then setting a preset iteration number K and a parameter M. Taking the moth fire suppression algorithm as an example, M is the number of moth populations. The distribution points can be respectively numbered, and the numbers of the different distribution points are different. In the following examples, the distribution center number is 0, and the N distribution destination numbers are 1, 2, N, k=200, and m=45 in this order.

S32, randomly initializing a distribution matrix.

The distribution matrix is a matrix with a dimension of M x N, the distribution matrix comprises M row vectors, each row vector comprises N elements, and the N elements correspond to N distribution destinations. The N elements are different from each other, and the order of the N elements indicates the order in which the corresponding N delivery destinations are traversed in the delivery scheme. And initializing the distribution matrix to obtain an initial distribution matrix.

S33, executing the ith iteration, and updating the distribution matrix.

Specifically, a first operation is performed, the first operation including: according to the distribution information, determining a row vector to be selected in a distribution matrix after the i-1 th iteration; updating the distribution matrix after the i-1 th iteration according to the row vector to be selected to obtain the distribution matrix after the i-1 th iteration; wherein, i is 1 initially, and the delivery matrix after the 0 th iteration is the initial delivery matrix;

when i is smaller than K, repeatedly executing the first operation, wherein K is a preset iteration number, and K is a positive integer larger than 1; and when i is equal to K, determining the distribution matrix after the Kth iteration as a candidate distribution matrix.

For the delivery matrix after the i-1 th iteration, M row vectors are also included in the matrix, each row vector comprises N elements, the N elements correspond to N delivery destinations, and the sequence of the N elements is used for indicating the delivery scheme corresponding to the row vector. After the distribution information is obtained, the distribution data of the plurality of distribution points can be determined according to the position of each distribution point and the distance information between any two distribution points, and the distribution data comprises the transportation distance and the transportation time between any two distribution points, because the distribution information comprises the position of each distribution point and the distance information between any two distribution points.

For example, after the position of each delivery point is obtained, the transportation distance between any two delivery points can be determined according to the position of each delivery point. Specifically, for any two delivery points, after the positions of the two delivery points are determined, a path from one delivery point to the other delivery point is also determined, so that the length of the path from one delivery point to the other delivery point is determined as the transportation distance between the two delivery points. In some cases, there are multiple paths from one delivery point to another, one may be selected as a path between two delivery points in the multiple paths, or one may be selected as a path between two delivery points in the multiple paths according to certain constraints, including, for example, high-speed priority, avoiding peak sections, and so on.

For any two delivery points, after determining the transportation distance between any two delivery points, the transportation time required for going from one delivery point to the other delivery point can be obtained by combining the information of the distance between the two delivery points, such as the number of included intersections, traffic light time and people flow.

After the delivery data is obtained, the respective adaptability of M row vectors in the delivery matrix after the i-1 th iteration can be determined according to the delivery data.

Specifically, for any jth row vector in M row vectors in the delivery matrix after the i-1 th iteration, determining a traversing path corresponding to the jth row vector, where the traversing path is a path from a delivery center and traversing each delivery destination in a delivery scheme indicated by the jth row vector.

Then, according to the distribution data, the energy consumption and the time consumption corresponding to the traversal path are determined. The delivery data comprises the transportation distance and the transportation time between any two delivery points, and the length, the driving energy consumption rate, the idle energy consumption rate and the waiting time of each sub-path in the traversal path can be determined according to the delivery data.

Then, according to the length of each sub-path, the driving energy consumption rate and the idle energy consumption rate, determining the energy consumption amount corresponding to the traversing path, wherein the process can be seen in the following formula (1):

（1）

wherein, the liquid crystal display device comprises a liquid crystal display device,

for traversing the energy consumption corresponding to path e, +.>

Representing the length of a traversing path e corresponding to the j-th row vector, wherein the size of the traversing path e is determined by the sum of the lengths of all sub-paths included in the traversing path; / >

Representing the latency of traversing path e may include, for example, traversing path eRed light waiting time; d is the running energy consumption rate of the distributed vehicle, and can be, for example, the fuel rate when the vehicle runs; q is the idle energy consumption rate, which may be, for example, the fuel rate of the vehicle when idling; p is the energy unit price cost, and may be, for example, the price of oil.

And determining the time consumption corresponding to the traversing path according to the length and the waiting time of each sub-path. This procedure can be seen in the following formula (2):

（2）

wherein, the liquid crystal display device comprises a liquid crystal display device,

the time consumption corresponding to the traversal path e; />

Representing the length of a traversing path e corresponding to the j-th row vector, wherein the size of the traversing path e is determined by the sum of the lengths of all sub-paths included in the traversing path; />

Representing the latency of traversing path e may include, for example, a red light latency on traversing path e; />

Indicating the speed of travel of the delivered vehicle on the traversal path e.

After determining the energy consumption and the time consumption corresponding to the traversal path, the fitness of the row vector may be determined according to the energy consumption and the time consumption.

For any jth row vector in the distribution matrix after the i-1 th iteration, the solution mode of the fitness of the jth row vector can be seen as the following formula (3)

（3）/>

Wherein, the liquid crystal display device comprises a liquid crystal display device,

is the fitness of the j-th row vector; />

Representing the j-th row vector in the distribution matrix after the i-1 th iteration; />

Representing the distribution cost corresponding to the j-th row vector (namely the j-th moth in the moth fire suppression algorithm), and (I)>

，/>

Representing the delivery cost through traversal path e, the calculation formula can be seen in the following formula (4):

（4）

wherein, the liquid crystal display device comprises a liquid crystal display device,

for traversing the energy consumption corresponding to path e, +.>

The amount of time consumed for traversing path e.

And->

Are constants, and respectively represent energy weight and time weight.

After the fitness of each M row vectors in the delivery matrix after the i-1 th iteration is obtained, determining the row vector to be selected in the delivery matrix after the i-1 th iteration according to the fitness of each M row vectors in the delivery matrix after the i-1 th iteration. Since the fitness reflects the energy consumption amount and the time consumption amount of the distribution scheme indicated by the j-th row vector to some extent, a row vector corresponding to a distribution scheme with a smaller energy consumption amount and time consumption amount can be selected as the row vector to be selected based on the fitness. For example, the row vector with the smallest fitness can be determined as the row vector to be selected in the distribution matrix after the i-1 th iteration. In the moth fire suppression algorithm, the vector to be selected is the known optimal position of the moth, namely the position of the flame.

In the above embodiment, it is described how to determine the row vector to be selected in the distribution matrix after the i-1 th iteration, and how to update the distribution matrix after the i-1 th iteration according to the row vector to be selected will be described below.

First, a first arrangement order of N elements included in a row vector to be selected is determined. And determining a second arrangement sequence of N elements included in the row vector aiming at any row vector in the distribution matrix after the i-1 th iteration, and updating the N elements included in the row vector according to the first arrangement sequence and the second arrangement sequence to obtain an updated row vector.

Specifically, a first number of elements having different arrangement sequences in the first arrangement sequence and the second arrangement sequence is determined, and then a second number of updated elements is determined according to the first number, wherein the second number is smaller than the first number. And after the second number is obtained, updating N elements included in the row vector according to the second number to obtain an updated row vector.

For example, according to the first arrangement sequence and the second arrangement sequence, it is determined that the first number of elements with different arrangement sequences in the row vector and the row vector to be selected is T, where T is a positive integer, then a positive integer T may be randomly selected in the range [1, G ], where T is a second number of updated elements, then N elements included in the row vector are updated according to T, so as to obtain an updated row vector, and G is an integer less than or equal to T. After the updated row vector is obtained, the distribution matrix after the ith iteration can be obtained according to the updated row vector.

The following description will take the line vector to be selected as flame, and each line vector in the distribution matrix after the i-1 th iteration as moth, and take the moth fire suppression algorithm as an example. Each moth is searched around the flame, and the moth position is updated. According to the number T of distribution destinations with different arrangement sequences of the moths and the flames, a positive integer T in [1, T/2] can be randomly selected, and the distribution destinations with different arrangement sequences of the moths and the flames are exchanged for T times, so that a new position of the moths is obtained.

In this example, if the flame (i.e., the candidate row vector) is u _t =[1, 2, 3, 4, 5, 6, …,29, 30]One of the moths j (i.e., the j-th row vector in the delivery matrix after the i-1 th iteration) is u _j =[2, 3, 4, 1, 5, 6,…,29, 30]There are 4 different distribution destinations, i.e., t=4. Suppose random [1, T/2]Positive integer t=2, and then the first exchange is performed to make the moth u _j The 1 st order (i.e. delivery order) of the flame is changed to the same index as the flame, i.e. u _j _new1=[1, 3, 4, 1, 5, 6,…,29, 30]This causes duplication of number 1, and illegal solution, which modifies the unmodified duplicate number to the unmodified index 2, i.e., u _j _new2=[1, 3, 4, 2, 5, 6,…,29, 30]. Then exchange for the 2 nd time, and make the moth u _j The current 1 st number of_new 2, which is different from the flame distribution sequence, is changed to the same number as the flame, i.e. u _j _new3=[1, 2, 4, 2, 5, 6,…,29, 30]This causes duplication of number 2, and illegal resolution, which modifies the unmodified duplicate number to the unmodified index 3, i.e., u _j _new4=[1, 2, 4, 3, 5, 6,…,29, 30]。u _j And_new 4 is the position of the new moth j, namely the j-th row vector after updating. The distribution matrix after the ith iteration can be obtained by updating any row vector in the mode.

S34, judging whether the iteration times are equal to K, if so, executing S36, and if not, executing S35.

S35, updating i to be i+1, and executing S33.

And when the preset iteration number K is not reached, executing the next iteration process according to the scheme.

S36, determining the distribution matrix after the Kth iteration as a to-be-selected distribution matrix.

Stopping iteration when the preset iteration times K are reached, and determining the distribution matrix after the Kth iteration as a to-be-selected distribution matrix.

After the matrix to be selected is obtained, according to the distribution data, the fitness of each of the M row vectors in the matrix to be selected can be determined, and the implementation manner of determining the fitness can be referred to the description of the solution manner about the fitness in the above embodiment, which is not repeated here.

After the respective fitness of the M row vectors in the matrix to be selected is obtained, the target row vector can be determined in the matrix to be selected according to the respective fitness of the M row vectors in the matrix to be selected. For example, a row vector having the lowest fitness may be determined as a target row vector, a row vector having a moderate fitness may be determined as a target row vector, and so on.

In the embodiment of the application, according to the transportation distance, the energy consumption and the time consumption of each road section between the current distribution center and each distribution destination, parameters are selected, and the energy cost and the transportation time of each distribution scheme in the to-be-selected distribution matrix are calculated by adopting a summation normalization method, so that the optimal distribution scheme is selected.

Fig. 4 is a first comparative schematic diagram of a flow distribution scheme under different algorithms provided in an embodiment of the present application, fig. 5 is a second comparative schematic diagram of a flow distribution scheme under different algorithms provided in an embodiment of the present application, and fig. 6 is a third comparative schematic diagram of a flow distribution scheme under different algorithms provided in an embodiment of the present application:

as shown in fig. 4, the horizontal axis represents the number of iterations and the vertical axis represents the overall cost. Compared with the traditional genetic algorithm, the low-carbon logistics distribution method provided by the embodiment of the application has about 13% to 18% improvement on the comprehensive cost, namely the comprehensive cost in the logistics distribution process is reduced.

As shown in fig. 5, the horizontal axis represents the number of iterations, and the vertical axis represents the energy consumption. Compared with the traditional genetic algorithm, the low-carbon logistics distribution method provided by the embodiment of the application has about 8% to 12% improvement in energy consumption, namely the energy consumption in the logistics distribution process is reduced.

As shown in fig. 6, the horizontal axis represents the number of iterations, and the vertical axis represents the time consumption. Compared with the traditional genetic algorithm, the low-carbon logistics distribution method provided by the embodiment of the application has the advantages that the time consumption is obviously improved, namely, the time consumption in the logistics distribution process is reduced.

In summary, according to the scheme of the embodiment of the present application, since the to-be-selected distribution matrix is obtained by performing iterative operation on the initial distribution matrix according to the distribution information, where the distribution information includes the positions of each distribution point and the path information between any two distribution points, after performing iterative operation on the initial distribution matrix by using the distribution information, the indicated M row vectors in the to-be-selected distribution matrix are relatively shorter in time spent by the to-be-selected distribution scheme, and are relatively higher in efficiency. Thus, a target row vector determined among M row vectors included in the candidate dispensing matrix may be used to indicate a target dispensing scheme. And traversing each delivery destination from the delivery center based on the target delivery scheme indicated by the target row vector. The logistics distribution scheme is provided under the conditions of low carbon emission and short transportation time, and the logistics distribution efficiency is improved. The algorithm complexity is effectively reduced when the number of the logistics distribution destinations is large, the calculation time is reduced, the real-time performance of the system is improved, and the stability and the robustness of the logistics transportation system are further improved.

The low-carbon logistics distribution device provided by the application is described below, and the low-carbon logistics distribution device and the low-carbon logistics distribution method described below can be referred to correspondingly.

Fig. 7 is a schematic structural diagram of a low-carbon logistics distribution apparatus according to an embodiment of the present application, as shown in fig. 7, the apparatus includes:

the collecting module 71 is configured to collect, in response to a delivery instruction sent by the client, delivery information of a plurality of delivery points in a preset area, where the plurality of delivery points include a delivery center and a plurality of delivery destinations, and the delivery information includes a position of each delivery point and path information between any two delivery points;

an obtaining module 72, configured to obtain an initial distribution matrix, where M row vectors in the initial distribution matrix are used to indicate M different initial distribution schemes, where a distribution scheme is a sequence that starts from the distribution center and traverses each distribution destination, and M is a positive integer;

a processing module 73, configured to perform an iterative operation on the initial distribution matrix according to the distribution information, to obtain a to-be-selected distribution matrix, where M row vectors included in the to-be-selected distribution matrix are used to indicate M different to-be-selected distribution schemes;

A determining module 74, configured to determine a target row vector from M row vectors included in the candidate dispensing matrix, where the target row vector is used to indicate a target dispensing scheme;

and a sending module 75, configured to send the target delivery scheme to the client, where the target delivery scheme includes a target sequence that starts from the delivery center and traverses each of the delivery destinations.

In one possible implementation, the processing module 73 is specifically configured to:

performing a first operation, the first operation comprising: determining a row vector to be selected in a distribution matrix after the i-1 th iteration according to the distribution information; updating the distribution matrix after the i-1 th iteration according to the row vector to be selected to obtain the distribution matrix after the i-1 th iteration; the i is 1 initially, and the distribution matrix after the 0 th iteration is the initial distribution matrix;

when i is smaller than K, updating i to be i+1, and repeatedly executing the first operation, wherein K is a preset iteration number, and K is a positive integer larger than 1;

and when the i is equal to the K, determining the distribution matrix after the K iteration as the to-be-selected distribution matrix.

In one possible implementation, the processing module 73 is specifically configured to:

Determining delivery data of the plurality of delivery points according to the positions of the delivery points and the path information between any two delivery points, wherein the delivery data comprises the transportation distance and the transportation time between any two delivery points;

according to the distribution data, determining the respective fitness of M row vectors in the distribution matrix after the i-1 th iteration;

and determining the row vector to be selected in the delivery matrix after the i-1 th iteration according to the respective fitness of M row vectors in the delivery matrix after the i-1 th iteration.

In one possible implementation, the processing module 73 is specifically configured to:

determining a traversing path corresponding to an arbitrary row vector in M row vectors in the i-1 th iterated distribution matrix, wherein the traversing path is a path which starts from the distribution center and traverses each distribution destination in a distribution scheme indicated by the row vector;

according to the distribution data, determining the energy consumption and the time consumption corresponding to the traversal path;

and determining the fitness of the row vector according to the energy consumption and the time consumption.

In one possible implementation, the traversal path includes a plurality of sub-paths; the processing module 73 is specifically configured to:

Determining the length, the driving energy consumption rate, the idle energy consumption rate and the waiting time of each sub-path according to the distribution data;

determining the energy consumption corresponding to the traversal path according to the length of each sub-path, the driving energy consumption rate and the idle speed energy consumption rate;

and determining the time consumption corresponding to the traversing path according to the length of each sub-path and the waiting time.

In one possible implementation manner, any row vector in the distribution matrix includes N elements, where the order of the N elements is used to indicate the order of traversing the corresponding distribution destinations, N is the number of the plurality of distribution destinations, and N is a positive integer; the processing module 73 is specifically configured to:

determining a first arrangement sequence of N elements included in the row vector to be selected;

determining a second arrangement sequence of N elements included in the row vector for any row vector in the distribution matrix after the i-1 th iteration;

updating N elements included in the row vector according to the first arrangement sequence and the second arrangement sequence to obtain an updated row vector;

And obtaining the distribution matrix after the ith iteration according to the updated row vector.

In one possible implementation, the processing module 73 is specifically configured to:

determining a first number of elements in the first arrangement order and the second arrangement order, wherein the elements are different in arrangement order;

determining a second number of update elements according to the first number, the second number being smaller than the first number;

and updating N elements included in the row vector according to the second quantity to obtain the updated row vector.

In one possible implementation, the determining module 74 is specifically configured to:

according to the distribution data, determining the respective fitness of M row vectors in the to-be-selected distribution matrix;

and determining the target row vector in the to-be-selected distribution matrix according to the respective fitness of M row vectors in the to-be-selected distribution matrix.

Fig. 8 illustrates a physical structure diagram of an electronic device, as shown in fig. 8, which may include: processor 810, communication interface (Communications Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform a low carbon logistics distribution method, which is applied to a terminal device, comprising: responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points; acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, wherein the distribution schemes are sequences starting from a distribution center and traversing each distribution destination, and M is a positive integer; according to the distribution information, carrying out iterative operation on the initial distribution matrix to obtain a matrix to be selected, wherein M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected; determining a target row vector in M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme; and sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, the present application further provides a computer program product, where the computer program product includes a computer program, where the computer program may be stored on a non-transitory computer readable storage medium, where the computer program, when executed by a processor, is capable of executing the low-carbon logistics distribution method provided by the above methods, where the method is applied to a terminal device, and includes: responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points; acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, wherein the distribution schemes are sequences starting from a distribution center and traversing each distribution destination, and M is a positive integer; according to the distribution information, carrying out iterative operation on the initial distribution matrix to obtain a matrix to be selected, wherein M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected; determining a target row vector in M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme; and sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

In still another aspect, the present application further provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the low-carbon logistics distribution method provided by the above methods, the method being applied to a terminal device, and comprising: responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points; acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, wherein the distribution schemes are sequences starting from a distribution center and traversing each distribution destination, and M is a positive integer; according to the distribution information, carrying out iterative operation on the initial distribution matrix to obtain a matrix to be selected, wherein M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected; determining a target row vector in M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme; and sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. A low-carbon logistics distribution method, characterized by being applied to a terminal device, comprising:

responding to a delivery instruction sent by a client, and collecting delivery information of a plurality of delivery points in a preset area, wherein the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points;

acquiring an initial distribution matrix, wherein M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, wherein the distribution schemes are sequences starting from a distribution center and traversing each distribution destination, and M is a positive integer;

According to the distribution information, carrying out iterative operation on the initial distribution matrix to obtain a matrix to be selected, wherein M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected;

determining a target row vector in M row vectors included in the to-be-selected distribution matrix, wherein the target row vector is used for indicating a target distribution scheme;

and sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

2. The method of claim 1, wherein iteratively operating the initial distribution matrix according to the distribution information to obtain a candidate distribution matrix comprises:

performing a first operation, the first operation comprising: determining a row vector to be selected in a distribution matrix after the i-1 th iteration according to the distribution information; updating the distribution matrix after the i-1 th iteration according to the row vector to be selected to obtain the distribution matrix after the i-1 th iteration; the i is 1 initially, and the distribution matrix after the 0 th iteration is the initial distribution matrix;

when i is smaller than K, updating i to be i+1, and repeatedly executing the first operation, wherein K is a preset iteration number, and K is a positive integer larger than 1;

And when the i is equal to the K, determining the distribution matrix after the K iteration as the to-be-selected distribution matrix.

3. The method of claim 2, wherein determining the candidate row vector in the delivery matrix after the i-1 th iteration according to the delivery information comprises:

determining delivery data of the plurality of delivery points according to the positions of the delivery points and the path information between any two delivery points, wherein the delivery data comprises the transportation distance and the transportation time between any two delivery points;

according to the distribution data, determining the respective fitness of M row vectors in the distribution matrix after the i-1 th iteration;

and determining the row vector to be selected in the delivery matrix after the i-1 th iteration according to the respective fitness of M row vectors in the delivery matrix after the i-1 th iteration.

4. The method of claim 3, wherein said determining, based on said delivery data, respective fitness of M row vectors in said delivery matrix after said i-1 th iteration comprises:

determining a traversing path corresponding to an arbitrary row vector in M row vectors in the i-1 th iterated distribution matrix, wherein the traversing path is a path which starts from the distribution center and traverses each distribution destination in a distribution scheme indicated by the row vector;

According to the distribution data, determining the energy consumption and the time consumption corresponding to the traversal path;

and determining the fitness of the row vector according to the energy consumption and the time consumption.

5. The method of claim 4, wherein the traversal path comprises a plurality of sub-paths; the determining the energy consumption and the time consumption corresponding to the traversal path according to the distribution data comprises the following steps:

determining the length, the driving energy consumption rate, the idle energy consumption rate and the waiting time of each sub-path according to the distribution data;

determining the energy consumption corresponding to the traversal path according to the length of each sub-path, the driving energy consumption rate and the idle speed energy consumption rate;

and determining the time consumption corresponding to the traversing path according to the length of each sub-path and the waiting time.

6. The method of any of claims 2-5, wherein N elements are included in any row vector in the distribution matrix, the order of the N elements being used to indicate an order of traversing corresponding distribution destinations, N being a number of the plurality of distribution destinations, and N being a positive integer; updating the delivery matrix after the i-1 th iteration according to the row vector to be selected to obtain the delivery matrix after the i-1 th iteration, wherein the method comprises the following steps of:

Determining a first arrangement sequence of N elements included in the row vector to be selected;

determining a second arrangement sequence of N elements included in the row vector for any row vector in the distribution matrix after the i-1 th iteration;

updating N elements included in the row vector according to the first arrangement sequence and the second arrangement sequence to obtain an updated row vector;

and obtaining the distribution matrix after the ith iteration according to the updated row vector.

7. The method of claim 6, wherein updating the N elements included in the row vector according to the first arrangement order and the second arrangement order to obtain an updated row vector comprises:

determining a first number of elements in the first arrangement order and the second arrangement order, wherein the elements are different in arrangement order;

determining a second number of update elements according to the first number, the second number being smaller than the first number;

and updating N elements included in the row vector according to the second quantity to obtain the updated row vector.

8. The method according to any one of claims 3-5, wherein determining a target row vector among M row vectors included in the candidate dispensing matrix comprises:

According to the distribution data, determining the respective fitness of M row vectors in the to-be-selected distribution matrix;

and determining the target row vector in the to-be-selected distribution matrix according to the respective fitness of M row vectors in the to-be-selected distribution matrix.

9. A low carbon logistics distribution apparatus, comprising:

the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for responding to a delivery instruction sent by a client and acquiring delivery information of a plurality of delivery points in a preset area, the plurality of delivery points comprise a delivery center and a plurality of delivery destinations, and the delivery information comprises the position of each delivery point and the path information between any two delivery points;

the system comprises an acquisition module, a distribution center and a distribution destination, wherein the acquisition module is used for acquiring an initial distribution matrix, M row vectors in the initial distribution matrix are used for indicating M different initial distribution schemes, the distribution schemes are sequences starting from the distribution center and traversing the distribution destinations, and M is a positive integer;

the processing module is used for carrying out iterative operation on the initial distribution matrix according to the distribution information to obtain a matrix to be selected, and M row vectors included in the matrix to be selected are used for indicating M different schemes to be selected;

The determining module is used for determining a target row vector in M row vectors included in the to-be-selected distribution matrix, and the target row vector is used for indicating a target distribution scheme;

and the sending module is used for sending the target delivery scheme to the client, wherein the target delivery scheme comprises a target sequence which starts from the delivery center and traverses each delivery destination.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the low carbon logistics distribution method of any one of claims 1 to 8 when the program is executed.

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