Dispatching method and system of unmanned logistics distribution vehicle, and medium

Abstract

The invention discloses an unmanned logistics distribution vehicle scheduling method and system, and a medium, belongs to the technical field of unmanned vehicle scheduling, and is used for solving the problem that a current unmanned logistics distribution method neglects reasonable scheduling of a plurality of unmanned logistics vehicles in a whole distribution area. And the logistics distribution efficiency in the distribution area is reduced. The method comprises the following steps: associating delivery addresses with a regional electronic map to obtain position coordinates of each delivery address; carrying out clustering analysis on the delivery addresses based on the position coordinates, and determining a plurality of delivery clustering areas; distributing a corresponding unmanned logistics distribution vehicle according to the number of the to-be-distributed expresses in each distribution clustering area; determining the optimal distribution time of each unmanned logistics distribution vehicle according to the distribution information of the to-be-distributed express in each distribution clustering area; and determining the shortest traversal route of each unmanned logistics distribution vehicle based on a path optimization algorithm, and scheduling the unmanned logistics distribution vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vehicle dispatching, and in particular to a dispatching method, system and medium for an unmanned logistics delivery vehicle. Background Art

[0002] As the terminal delivery stage with the highest cost, lowest efficiency and most serious environmental pollution in the entire freight logistics transmission chain, it has always been the short board of the delivery stage quality. The cost and time consumption of terminal delivery account for more than 30% of the entire delivery operation. The pain points of terminal delivery are caused by objective reasons and human factors, which are reflected in the large number of goods to be delivered at the end of logistics, the complex environment during the delivery process, the complex delivery channels, the various delivery regulations of consumers, and the different service quality of delivery staff.

[0003] The pain points of terminal delivery continue to exist, but the demand for delivery is increasing day by day. With the rapid development of the Internet, logistics and transportation are also developing rapidly. The growing volume of freight and the number of orders have brought great work pressure to the delivery terminal. Relying solely on manpower to carry out terminal delivery has long been unable to fully handle the difficulties of current logistics and transportation terminal delivery. The rapid development of unmanned delivery is urgent.

[0004] Unmanned delivery is a practical application of an autonomous driving system, so it requires the use of relevant technologies in autonomous driving. Most of these technologies are basically the same as general unmanned driving, and require the close integration of hardware and software sensors to complete vehicle positioning, environmental perception, path decision-making, vehicle control and other operations. However, most of the current research on unmanned logistics and delivery focuses on improving the accuracy of self-positioning, environmental perception and other technologies of a single unmanned logistics vehicle during its driving process, ignoring the reasonable scheduling of several unmanned logistics vehicles in the entire delivery area. If the scheduling is unreasonable, the logistics and delivery efficiency in the delivery area will be reduced, and the efficiency cannot be maximized, and people’s expectations for the efficiency of unmanned delivery cannot be met. Summary of the invention

[0005] The embodiments of the present invention provide a scheduling method, system and medium for an unmanned logistics delivery vehicle, which are used to solve the following technical problems: the current unmanned logistics delivery method ignores the reasonable scheduling problem of several unmanned logistics vehicles in the entire delivery area, which easily leads to a decrease in the logistics delivery efficiency in the delivery area and fails to achieve maximum efficiency.

[0006] The embodiment of the present invention adopts the following technical solutions:

[0007] On the one hand, an embodiment of the present invention provides a method for dispatching an unmanned logistics delivery vehicle, the method comprising: obtaining an electronic map of a region to be delivered and a delivery address of a courier to be delivered;

[0008] Associating the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map;

[0009] Based on the location coordinates, cluster analysis is performed on the delivery address to determine a number of delivery cluster areas;

[0010] According to the number of express deliveries to be delivered in each delivery cluster area, the corresponding unmanned logistics delivery vehicles are allocated;

[0011] Determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express deliveries to be delivered in each delivery cluster area;

[0012] Based on the path optimization algorithm, determine the shortest traversal route for each unmanned logistics delivery vehicle;

[0013] The unmanned logistics delivery vehicle is dispatched based on the optimal delivery time and the shortest traversal route.

[0014] In a feasible implementation manner, obtaining an electronic map of the area to be delivered and a delivery address of the express to be delivered specifically includes:

[0015] Obtain and download an electronic map of the area to be delivered in the navigation system;

[0016] In the logistics information entry system, obtain the express that needs to be delivered on the day and the express to be delivered;

[0017] From the express information of the express to be delivered, extract the delivery address.

[0018] In a feasible implementation manner, associating the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map specifically includes:

[0019] Inputting the delivery addresses in batches into the regional electronic map, and displaying corresponding location icons in the regional electronic map;

[0020] A two-dimensional coordinate system is constructed based on the regional electronic map, and the position coordinates of each position icon in the regional electronic map are obtained.

[0021] In a feasible implementation, based on the location coordinates, cluster analysis is performed on the delivery address to determine a number of delivery cluster areas, specifically including:

[0022] Determine the number of cluster centers K based on the number of unmanned logistics delivery vehicles at the current delivery point;

[0023] Randomly select K position coordinates from the position coordinates as initial cluster centers, and calculate the distance between each data point and the initial cluster center;

[0024] According to the distance, the left side of each position is assigned to the nearest initial cluster center to obtain K initial cluster clusters;

[0025] Calculate the mean of all position coordinates in each initial cluster, update it to the new cluster center, and iterate the distribution until the position of the cluster center converges to obtain the final cluster division result;

[0026] The area where each cluster in the cluster division result is located is determined as a distribution cluster area.

[0027] In a feasible implementation, the corresponding unmanned logistics delivery vehicle is allocated according to the number of express deliveries to be delivered in each delivery cluster area, specifically including:

[0028] According to the location coordinates in each delivery cluster area, count the number of express deliveries to be delivered in each delivery cluster area;

[0029] The number of express deliveries to be delivered is matched with the capacity of each unmanned logistics delivery vehicle at the current delivery point, and a corresponding unmanned logistics delivery vehicle is allocated to each delivery cluster area according to the matching result.

[0030] In a feasible implementation, the optimal delivery time of each unmanned logistics delivery vehicle is determined according to the delivery information of the express to be delivered in each delivery cluster area, specifically including:

[0031] In the express delivery system, the delivery information of the express to be delivered corresponding to the current delivery cluster area is extracted; wherein the delivery information at least includes: the recipient, the recipient's historical delivery time and the recipient's historical delivery success rate;

[0032] Count the distribution of historical delivery time for each recipient in the current delivery cluster area to obtain the delivery quantity in each time period;

[0033] Calculate the average of the historical delivery success rates of all recipients in each time period to obtain the delivery success rate in each time period;

[0034] Calculate the delivery efficiency of each time period based on the delivery quantity and delivery success rate in each time period;

[0035] The time period with the highest delivery efficiency is determined as the optimal delivery time for the current delivery cluster area.

[0036] In a feasible implementation, based on a path optimization algorithm, the shortest traversal route of each unmanned logistics delivery vehicle is determined, specifically including:

[0037] Number the delivery addresses within the current delivery cluster area and construct a delivery address set;

[0038] Constructing a shortest distance model based on the distance between each delivery address in the delivery address set;

[0039] Constructing a path optimization algorithm; the path optimization algorithm is a bat optimization algorithm;

[0040] Initializing bat population information in the bat optimization algorithm and randomly generating an initial population; wherein the dimension of each individual bat in the bat optimization algorithm corresponds to the number of delivery addresses in the current delivery clustering area;

[0041] According to the driving speed range of the unmanned logistics delivery vehicle in the current distribution cluster area, the speed range constraint of the individual bat is constructed;

[0042] According to the number of delivery addresses in the current delivery cluster area, the location interval constraints of individual bats are constructed;

[0043] Based on the speed interval constraint and the position interval constraint, the path optimization algorithm is executed, and the traversal route searched each time is substituted into the shortest distance model until an optimal solution is obtained;

[0044] The traversal route corresponding to the optimal solution is determined as the shortest traversal route for the current unmanned logistics delivery vehicle.

[0045] In a feasible implementation, based on the distance between each delivery address in the delivery address set, a shortest distance model is constructed, specifically including:

[0046] Assume that there are N delivery addresses in the current delivery cluster area, numbered as P = {1, 2, 3, ..., N};

[0047] according to Construct the shortest path model; wherein, C i is the i-th delivery address, C i+1 is the i+1th delivery address, C N is the Nth delivery address, C1 is the first delivery address; d(C i ,C i+1 ) means from the delivery address C i To delivery address C i+1 The distance, d(C N ,C1) means from the delivery address C N Distance to delivery address C1.

[0048] On the other hand, an embodiment of the present invention further provides a dispatching system for an unmanned logistics delivery vehicle, the system comprising:

[0049] The unmanned logistics delivery vehicle allocation module is used to obtain the regional electronic map of the area to be delivered and the delivery address of the express to be delivered; associate the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map; based on the location coordinates, perform cluster analysis on the delivery address to determine a number of delivery cluster areas; and allocate the corresponding unmanned logistics delivery vehicle according to the number of express to be delivered in each delivery cluster area;

[0050] The unmanned logistics delivery vehicle scheduling module is used to determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express to be delivered in each delivery cluster area; determine the shortest traversal route for each unmanned logistics delivery vehicle based on the path optimization algorithm; and schedule the unmanned logistics delivery vehicles based on the optimal delivery time and the shortest traversal route.

[0051] Finally, an embodiment of the present invention also provides a storage medium, which is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by a terminal, the terminal executes the scheduling method for an unmanned logistics delivery vehicle.

[0052] Compared with the prior art, the scheduling method, system and medium of an unmanned logistics delivery vehicle provided by the embodiments of the present invention have the following beneficial effects:

[0053] The present invention performs cluster analysis based on the addresses of the express delivery to be delivered in the delivery area, thereby allocating different unmanned logistics delivery vehicles to deliver the express delivery in different cluster areas, making full use of the unmanned vehicle resources of the distribution station. The present invention also provides a scheduling method for each unmanned logistics delivery vehicle, reasonably determines the optimal delivery time of each unmanned logistics delivery vehicle based on the delivery information, and determines the shortest traversal route of each unmanned logistics delivery vehicle based on the path optimization algorithm, so that the unmanned logistics delivery vehicle can perform express delivery with the shortest traversal route during the optimal delivery period. It realizes the scheduling of multiple unmanned logistics delivery vehicles from a macro perspective, and improves the overall delivery efficiency and delivery success rate of the distribution station. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0055] Figure 1 A flow chart of a method for dispatching an unmanned logistics delivery vehicle provided by an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of the structure of a dispatching system for an unmanned logistics delivery vehicle provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0058] The embodiment of the present invention provides a method for dispatching an unmanned logistics delivery vehicle, such as Figure 1 As shown, the dispatching method of the unmanned logistics delivery vehicle specifically includes steps S101-S106:

[0059] S101, obtaining a regional electronic map of the area to be delivered and a delivery address of the express to be delivered, and associating the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map.

[0060] Specifically, an electronic map of the area to be delivered is obtained and downloaded in the navigation system, and then the express to be delivered on the day is obtained in the logistics information entry system to obtain the express to be delivered.

[0061] Furthermore, the delivery address is extracted from the express information of the express to be delivered. The delivery addresses are batch-entered into the regional electronic map, and the corresponding location icons are displayed in the regional electronic map. The regional electronic map displaying the location icons is exported in the form of a picture, a two-dimensional coordinate system is constructed based on the exported picture, and the location coordinates of each location icon in the picture are obtained.

[0062] S102: Based on the location coordinates, cluster analysis is performed on the delivery addresses to determine a number of delivery cluster areas.

[0063] Specifically, according to the number of unmanned logistics delivery vehicles at the current delivery point, the number of cluster centers K is determined. K location coordinates are randomly selected from the location coordinates as the initial cluster centers, and the distance between each data point and the initial cluster center is calculated.

[0064] Furthermore, according to the calculated distance, the left side of each position is assigned to the nearest initial cluster center to obtain K initial clusters.

[0065] Furthermore, the mean of all position coordinates in each initial cluster is calculated, updated as a new cluster center, and iterated allocation is performed until the position of the cluster center converges to obtain the final cluster division result. The area where each cluster in the cluster division result is located is determined as a distribution cluster area.

[0066] Through cluster analysis, the delivery addresses are divided into multiple compact areas, which makes it easier for multiple unmanned logistics delivery vehicles to carry out centralized delivery and save time on the road.

[0067] S103. Allocate corresponding unmanned logistics delivery vehicles according to the number of express deliveries to be delivered in each delivery cluster area.

[0068] Specifically, according to the location coordinates in each delivery cluster area, the number of express deliveries to be delivered in each delivery cluster area is counted.

[0069] Furthermore, the number of express deliveries to be delivered is matched with the capacity of each unmanned logistics delivery vehicle at the current delivery point, and a corresponding unmanned logistics delivery vehicle is allocated to each delivery cluster area based on the matching results.

[0070] As a feasible implementation method, the number of express deliveries to be delivered in each delivery cluster area is different. For delivery cluster areas with a large number of express deliveries, delivery vehicles with larger loading capacity are allocated. For delivery cluster areas with a small number of express deliveries, delivery vehicles with smaller loading capacity are allocated. This can rationally utilize delivery vehicle resources and improve space utilization in unmanned logistics delivery vehicles.

[0071] S104. Determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express deliveries to be delivered in each delivery cluster area.

[0072] Specifically, in the express delivery system, the delivery information of the express to be delivered corresponding to the current delivery cluster area is extracted, wherein the delivery information at least includes: the recipient, the recipient's historical delivery time, and the recipient's historical delivery success rate.

[0073] Furthermore, the distribution of the historical delivery time of each recipient in the current delivery cluster area is counted to obtain the delivery quantity in each time period. Then, the average of the historical delivery success rates of all recipients corresponding to each time period is calculated to obtain the delivery success rate in each time period.

[0074] Furthermore, the delivery efficiency of each time period is calculated based on the delivery quantity and delivery success rate in each time period, and the time period with the highest delivery efficiency is determined as the optimal delivery time for the current delivery cluster area.

[0075] In one embodiment, first obtain the collection time of each recipient in the current distribution cluster area in the past month, and calculate how many express deliveries were delivered in each time period every day in the past month based on these collection times. A time period can be one hour or more hours, which can be limited according to actual conditions. Then calculate the average number of express deliveries delivered in each time period every day to obtain the number of deliveries in each time period. Then count how many of all express deliveries delivered in each time period every day in the past month were successfully signed for, and how many were not successfully signed for due to various reasons, so as to calculate the historical collection success rate of each time period every day, and then calculate the average value of the historical collection success rate of each time period every day to obtain the delivery success rate in each time period.

[0076] Finally, multiply the delivery quantity and delivery success rate in each time period to obtain the delivery efficiency in each time period, so as to select the time period with the highest delivery efficiency. For example, the delivery efficiency between 12 noon and 2 o'clock is the highest, so this time period is the best delivery time for the current delivery clustering area.

[0077] S105. Determine the shortest traversal route for each unmanned logistics delivery vehicle based on a path optimization algorithm.

[0078] Specifically, the delivery addresses in the current delivery cluster area are numbered to construct a delivery address set, and then the shortest distance model is constructed based on the distance between each delivery address in the delivery address set.

[0079] As a feasible implementation method, the specific method of constructing the shortest distance model is as follows:

[0080] Assume that there are N delivery addresses in the current delivery cluster area, numbered as P = {1, 2, 3, ..., N};

[0081] according to Construct the shortest path model; among them, C i is the i-th delivery address, C i+1 is the i+1th delivery address, C Nis the Nth delivery address, C1 is the first delivery address; d(C i ,C i+1 ) means from the delivery address C i To delivery address C i+1 The distance, d(C N ,C1) means from the delivery address C N Distance to delivery address C1.

[0082] Furthermore, a path optimization algorithm is constructed; the path optimization algorithm is a bat optimization algorithm. Then, the bat population information in the bat optimization algorithm is initialized, and an initial population is randomly generated; wherein, the dimension of each individual bat in the bat optimization algorithm corresponds to the number of delivery addresses in the current delivery clustering area.

[0083] Furthermore, according to the driving speed range of the unmanned logistics delivery vehicle in the current delivery cluster area, the speed interval constraint of the individual bat is constructed. According to the number of delivery addresses in the current delivery cluster area, the location interval constraint of the individual bat is constructed.

[0084] Furthermore, based on the speed interval constraints and the position interval constraints, the path optimization algorithm is executed, and the traversal route searched each time is substituted into the shortest distance model until the optimal solution is obtained. The traversal route corresponding to the optimal solution is determined as the shortest traversal route of the current unmanned logistics delivery vehicle.

[0085] In one embodiment, since each path needs to traverse all the delivery addresses in the current delivery cluster area, the dimension of each bat should correspond to the number of delivery addresses. Assuming that there are N addresses that need to be delivered, the encoding method of the bat is X = {x1, x2, x3, ..., x N}. Assume that there are 12 delivery addresses in the current delivery cluster area, then the corresponding speed value interval is [-11,11], and the corresponding position value interval is [1,12]. i The speed is updated to v i =[3,-5,7,4,-8,9,13,11,1,-5,-17,10], it can be found that there is an out-of-bounds problem in the speed. At this time, the speed is corrected, and the amount of speed components greater than 11 is set to 11, and the amount of speed components less than -11 is set to -11. After processing according to this rule, the bat individual x i The speed should be v i =[3,-5,7,4,-8,9,11,11,1,-5,-11,10]. i The position is updated to x i=[3,5,5,4,8,9,13,11,1,2,17,10], it can be found that there are out-of-bounds and duplicate problems in the location. At the same time, since the delivery path passes through each delivery address only once, the missing points are traversed first, and then the out-of-bounds and duplicate points are screened out, and the missing points are randomly assigned to the out-of-bounds and duplicate points. After this processing, the bat individual x i One possible solution for the position is xi = [3,5,6,4,8,9,7,11,1,2,12,10].

[0086] S106. Dispatching the unmanned logistics delivery vehicles based on the optimal delivery time and the shortest traversal route.

[0087] According to the best delivery time and the shortest traversal route in each distribution cluster area, the corresponding unmanned logistics delivery vehicle is controlled to start express delivery within the best delivery time, and traverse each delivery address in turn according to the shortest traversal route to ensure that each delivery address is passed at least once. After the delivery vehicle arrives at the delivery address, it automatically calls the phone number in the express information. If the call is not answered within the preset time or no one comes to pick up the express, it will leave the current delivery address and mark the express as a failed delivery and the number of failed delivery. Expresses that have failed to deliver multiple times will be transferred to the manual delivery process.

[0088] In addition, the embodiment of the present invention also provides a dispatching system for unmanned logistics delivery vehicles, such as Figure 2 As shown, the dispatching system 200 of the unmanned logistics delivery vehicle specifically includes:

[0089] The unmanned logistics delivery vehicle allocation module 210 is used to obtain the regional electronic map of the area to be delivered and the delivery address of the express to be delivered; associate the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map; based on the location coordinates, perform cluster analysis on the delivery address to determine a number of delivery cluster areas; and allocate the corresponding unmanned logistics delivery vehicle according to the number of express to be delivered in each delivery cluster area;

[0090] The unmanned logistics delivery vehicle scheduling module 220 is used to determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express to be delivered in each delivery cluster area; determine the shortest traversal route for each unmanned logistics delivery vehicle based on the path optimization algorithm; and schedule the unmanned logistics delivery vehicles based on the optimal delivery time and the shortest traversal route.

[0091] Finally, an embodiment of the present invention further provides a storage medium, which is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by a terminal, the terminal executes:

[0092] Obtain an electronic map of the area to be delivered and the delivery address of the express to be delivered;

[0093] Associating the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map;

[0094] Based on the location coordinates, cluster analysis is performed on the delivery address to determine a number of delivery cluster areas;

[0095] According to the number of express deliveries to be delivered in each delivery cluster area, the corresponding unmanned logistics delivery vehicles are allocated;

[0096] Determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express deliveries to be delivered in each delivery cluster area;

[0097] Based on the path optimization algorithm, determine the shortest traversal route for each unmanned logistics delivery vehicle;

[0098] The unmanned logistics delivery vehicle is dispatched based on the optimal delivery time and the shortest traversal route.

[0099] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0100] The above describes specific embodiments of the present invention. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for dispatching an unmanned logistics delivery vehicle, characterized in that: The method comprises: Obtain an electronic map of the area to be delivered and the delivery address of the express to be delivered; Associating the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map; Based on the location coordinates, cluster analysis is performed on the delivery address to determine a number of delivery cluster areas; According to the number of express deliveries to be delivered in each delivery cluster area, the corresponding unmanned logistics delivery vehicles are allocated; Determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express deliveries to be delivered in each delivery cluster area; Based on the path optimization algorithm, determine the shortest traversal route for each unmanned logistics delivery vehicle; The unmanned logistics delivery vehicle is dispatched based on the optimal delivery time and the shortest traversal route.

2. The method for dispatching an unmanned logistics delivery vehicle according to claim 1, characterized in that: Obtain the electronic map of the area to be delivered and the delivery address of the express to be delivered, including: Obtain and download an electronic map of the area to be delivered in the navigation system; In the logistics information entry system, obtain the express that needs to be delivered on the day and the express to be delivered; From the express information of the express to be delivered, extract the delivery address.

3. The method for dispatching an unmanned logistics delivery vehicle according to claim 1, characterized in that: Associating the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map specifically includes: Inputting the delivery addresses in batches into the regional electronic map, and displaying corresponding location icons in the regional electronic map; A two-dimensional coordinate system is constructed based on the regional electronic map, and the position coordinates of each position icon in the regional electronic map are obtained.

4. The method for dispatching an unmanned logistics delivery vehicle according to claim 1, characterized in that: Based on the location coordinates, cluster analysis is performed on the delivery address to determine several delivery cluster areas, specifically including: Determine the number of cluster centers K based on the number of unmanned logistics delivery vehicles at the current delivery point; Randomly select K position coordinates from the position coordinates as initial cluster centers, and calculate the distance between each data point and the initial cluster center; According to the distance, the left side of each position is assigned to the nearest initial cluster center to obtain K initial cluster clusters; Calculate the mean of all position coordinates in each initial cluster, update it to the new cluster center, and iterate the distribution until the position of the cluster center converges to obtain the final cluster division result; The area where each cluster in the cluster division result is located is determined as a distribution cluster area.

5. The method for dispatching an unmanned logistics delivery vehicle according to claim 1, characterized in that: According to the number of express deliveries to be delivered in each delivery cluster area, the corresponding unmanned logistics delivery vehicles are allocated, including: According to the location coordinates in each delivery cluster area, count the number of express deliveries to be delivered in each delivery cluster area; The number of express deliveries to be delivered is matched with the capacity of each unmanned logistics delivery vehicle at the current delivery point, and a corresponding unmanned logistics delivery vehicle is allocated to each delivery cluster area according to the matching result.

6. The method for dispatching an unmanned logistics delivery vehicle according to claim 1, characterized in that: According to the delivery information of the express to be delivered in each delivery cluster area, the optimal delivery time of each unmanned logistics delivery vehicle is determined, including: In the express delivery system, the delivery information of the express to be delivered corresponding to the current delivery cluster area is extracted; wherein the delivery information at least includes: the recipient, the recipient's historical delivery time and the recipient's historical delivery success rate; Count the distribution of historical delivery time for each recipient in the current delivery cluster area to obtain the delivery quantity in each time period; Calculate the average of the historical delivery success rates of all recipients in each time period to obtain the delivery success rate in each time period; Calculate the delivery efficiency of each time period based on the delivery quantity and delivery success rate in each time period; The time period with the highest delivery efficiency is determined as the optimal delivery time for the current delivery cluster area.

7. The method for dispatching an unmanned logistics delivery vehicle according to claim 1, characterized in that: Based on the path optimization algorithm, the shortest traversal route for each unmanned logistics delivery vehicle is determined, including: Number the delivery addresses within the current delivery cluster area and construct a delivery address set; Constructing a shortest distance model based on the distance between each delivery address in the delivery address set; Constructing a path optimization algorithm; the path optimization algorithm is a bat optimization algorithm; Initializing bat population information in the bat optimization algorithm and randomly generating an initial population; wherein the dimension of each individual bat in the bat optimization algorithm corresponds to the number of delivery addresses in the current delivery clustering area; According to the driving speed range of the unmanned logistics delivery vehicle in the current distribution cluster area, the speed range constraint of the individual bat is constructed; According to the number of delivery addresses in the current delivery cluster area, the location interval constraints of individual bats are constructed; Based on the speed interval constraint and the position interval constraint, the path optimization algorithm is executed, and the traversal route searched each time is substituted into the shortest distance model until an optimal solution is obtained; The traversal route corresponding to the optimal solution is determined as the shortest traversal route for the current unmanned logistics delivery vehicle.

8. The method for dispatching an unmanned logistics delivery vehicle according to claim 7, characterized in that: Based on the distance between each delivery address in the delivery address set, a shortest distance model is constructed, specifically including: Assume that there are N delivery addresses in the current delivery cluster area, numbered as P = {1, 2, 3, ..., N}; according to Construct the shortest path model; wherein, C i is the i-th delivery address, C i+1 is the i+1th delivery address, C N is the Nth delivery address, C1 is the first delivery address; d(C i ,C i+1 ) means from the delivery address C i To delivery address C i+1 The distance, d(C N ,C1) means from the delivery address C N Distance to delivery address C1.

9. A dispatching system for unmanned logistics delivery vehicles, characterized in that: The system comprises: The unmanned logistics delivery vehicle allocation module is used to obtain the regional electronic map of the area to be delivered and the delivery address of the express to be delivered; associate the delivery address with the regional electronic map to obtain the location coordinates of each delivery address in the regional electronic map; based on the location coordinates, perform cluster analysis on the delivery address to determine a number of delivery cluster areas; and allocate the corresponding unmanned logistics delivery vehicle according to the number of express to be delivered in each delivery cluster area; The unmanned logistics delivery vehicle scheduling module is used to determine the optimal delivery time for each unmanned logistics delivery vehicle based on the delivery information of the express to be delivered in each delivery cluster area; determine the shortest traversal route for each unmanned logistics delivery vehicle based on the path optimization algorithm; and schedule the unmanned logistics delivery vehicles based on the optimal delivery time and the shortest traversal route.

10. A storage medium, characterized in that: The storage medium is a non-volatile computer-readable storage medium, which stores at least one program, each of which includes instructions. When the instructions are executed by a terminal, the terminal executes a scheduling method for an unmanned logistics delivery vehicle according to any one of claims 1-8.

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