Intelligent logistics vehicle scheduling method and system

Abstract

The invention relates to the technical field of logistics distribution, in particular to an intelligent logistics vehicle scheduling method and system, and the method comprises the steps: obtaining the first carriage information of a logistics task; constructing a task area by taking a starting point of a logistics task in the first carriage information as a center, and acquiring second carriage information of a driver in the task area; determining a target vehicle in the task area, and sending the logistics task to a task terminal of the target vehicle; in response to a confirmation request of the target vehicle task terminal, inputting historical carriage information of each driver corresponding to the target vehicle sending the confirmation request into a pre-trained evaluation model, and sending an optimal result output by the evaluation model to the logistics task initiating end; the scheduling system is used for implementing the method. According to the method, the problem that people, vehicles and goods are not coordinated in logistics vehicle scheduling is solved by constructing a proper evaluation model.

Description

Technical Field

[0001] The present invention relates to the field of logistics distribution technology, and in particular to a smart logistics vehicle scheduling method and system. Background Art

[0002] Smart logistics is a logistics service system based on the Internet of Things technology that comprehensively uses big data, cloud computing, blockchain and related information technologies to achieve real-time response and intelligent optimization decision-making through comprehensive perception, identification and tracking of logistics operation status.

[0003] In the logistics process, how to achieve coordination between people (drivers), vehicles and goods is an important task of vehicle dispatching. Although some vehicle dispatching methods and systems have emerged in the prior art, they still cannot meet actual needs.

[0004] Therefore, the present invention provides a smart logistics vehicle scheduling method and system to solve the above-mentioned problems. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a smart logistics vehicle scheduling method and system to solve the problem of lack of coordination between people (drivers), vehicles and goods in the existing vehicle scheduling.

[0006] In order to achieve the above-mentioned purpose, in the first aspect, the technical solution adopted by the present invention is: a smart logistics vehicle scheduling method, the scheduling method comprising: obtaining first carrier information of the logistics task, the first carrier information including the starting point, end point, expected departure time and expected carrying demand of the logistics task; constructing a task area with the starting point of the logistics task in the first carrier information as the center, and obtaining second carrier information of the drivers in the task area, the second carrier information including the driver's historical carrier information, vehicle information and current carrier task status, the vehicle information at least including the actual carrying capacity of the vehicle, and the current carrier task status at least including the expected completion time of the current carrier task; determining the target vehicle in the task area according to the comparison result between the actual carrying capacity of the current vehicle and the expected carrying demand in the task area, sending the logistics task to the task terminal of the target vehicle, and generating a pending logistics task to be confirmed / not confirmed within a limited time period at the task terminal of the target vehicle; in response to the confirmation request of the task terminal of the target vehicle, inputting the historical carrier information of each driver corresponding to the target vehicle that sends the confirmation request into a pre-trained evaluation model, and sending the optimal result output by the evaluation model to the logistics task initiator.

[0007] A further improvement of the present application is that the historical transport information of each driver corresponding to the target vehicle sending the confirmation request includes each driver's driving route preference, historical task punctuality rate and customer satisfaction, and the expression of the evaluation model is:

[0008] S=W1×X1+W2×X2+W3×X3 (1),

[0009] In expression (1), S represents the comprehensive score, X1 represents the standard value of the driver's route preference, X2 represents the standard value of the historical task punctuality rate, X3 represents the standard value of customer satisfaction, W1 represents the weight of the driver's route preference, W2 represents the weight of the historical task punctuality rate, and W3 represents the weight of customer satisfaction.

[0010] A further improvement of the present application is that the process of obtaining the standard value of the driver's driving route preference includes: obtaining the preselected distance of the preferred route of the initiator of all historical logistics tasks completed by all target vehicles; obtaining the actual distance of the actual driving route of the vehicle corresponding to the historical logistics tasks completed by all target vehicles; determining the corresponding route similarities based on multiple preselected distances and corresponding multiple actual distances, and determining a route similarity set, and determining the standard value of the driver's driving route preference based on the route similarity set.

[0011] A further improvement of the present application is that the expression for the route similarity of a single driver's single logistics task is:

[0012]

[0013] In expression (2), A represents the route similarity of a single driver’s single logistics task, L yx Indicates the pre-selected distance of the preferred route, L sj Indicates the actual distance;

[0014] The expression of the standard value X1 of the driver's driving route preference is:

[0015]

[0016] In expression (3), A i represents the average route similarity of the target vehicle corresponding to the i-th driver, A min represents the minimum value in the route similarity set determined by expression (2), A max represents the maximum value in the route similarity set determined by expression (2).

[0017] A further improvement of the present application is that the process of obtaining the standard value of the historical task punctuality rate includes: obtaining the corresponding punctuality rates of the historical logistics tasks completed by all target vehicles; constructing a historical logistics task punctuality rate set for all target vehicles, and determining the standard value of the historical task punctuality rate of the corresponding target vehicle according to the historical logistics task punctuality rate set; wherein the expression of the standard value X2 of the historical task punctuality rate is:

[0018]

[0019] In expression (4), B j represents the average of the historical on-time rate of logistics tasks of the jth target vehicle, B min represents the minimum on-time rate of the historical logistics task on-time rate set, B max It represents the maximum on-time rate of the historical logistics task on-time rate set.

[0020] A further improvement of the present application is that the process of obtaining the standard value of customer satisfaction includes: obtaining the average score of customer satisfaction corresponding to the historical logistics tasks completed by each target vehicle; constructing a score set of customer satisfaction of all target vehicles, and determining the standard value of customer satisfaction of the corresponding target vehicle according to the score set of customer satisfaction; wherein the expression of the standard value X3 of customer satisfaction is:

[0021]

[0022] In expression (5), C k represents the average customer satisfaction score corresponding to the historical logistics tasks completed by the k-th driver, C min represents the minimum value of the score set constructed by the customer satisfaction of all target vehicles, C max Represents the maximum value of the score set constructed from the customer satisfaction of all target vehicles.

[0023] A further improvement of the present application is that the estimated completion time of the current transport task in the second transport information is earlier than the estimated departure time in the first transport information.

[0024] A further improvement of the present application is that the first carrier information also includes transportation requirements, and the transportation requirements at least include whether the vehicle is a closed compartment, and the vehicle information in the second carrier information also includes the closed status of the vehicle.

[0025] A further improvement of the present application is that, based on the comparison result between the actual load capacity of the current vehicle and the expected load demand in the mission area, the target vehicle determination condition in the mission area is determined as follows: if the actual load capacity of the current vehicle is greater than or equal to the expected load demand, the vehicle is determined to be the target vehicle.

[0026] In a second aspect, the present invention provides a smart logistics vehicle dispatching system, the dispatching system is used to execute any of the dispatching methods described above, and the dispatching system includes:

[0027] A task acquisition module is used to obtain the first carrier information of the logistics task;

[0028] A task area construction module is used to construct a task area with the starting point of the logistics task in the first carrier information as the center, and obtain the second carrier information of the driver in the task area;

[0029] A target vehicle determination module is used to determine the target vehicle in the mission area based on the comparison result between the actual carrying capacity of the current vehicles in the mission area and the estimated carrying demand;

[0030] A task assignment module is used to send the logistics task to the task terminal of the target vehicle, and send instructions for confirming / not confirming the logistics task to be executed within a limited time period to the task terminal of the target vehicle;

[0031] A confirmation request response module, which has a pre-trained recommendation model stored therein, is used to receive a confirmation request from a target vehicle task terminal, and input the historical carrier information of each driver corresponding to the target vehicle sending the confirmation request into the pre-trained recommendation model;

[0032] The result output module is used to send the optimal result of the recommendation model to the logistics task initiator.

[0033] The beneficial effects of the present invention are as follows: by comprehensively considering the driver's route preference, historical task punctuality and customer satisfaction, the present invention can more accurately match suitable drivers and tasks, thereby improving the punctuality of task completion and customer satisfaction, achieving the coordination of people, vehicles and goods, improving the efficiency and quality of logistics scheduling, and reducing transportation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic flow chart of a smart logistics vehicle scheduling method of the present invention;

[0035] Figure 2 This is a schematic structural diagram of an intelligent logistics vehicle dispatching system of the present invention. DETAILED DESCRIPTION

[0036] The following will describe various embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0037] Smart logistics is a logistics service system based on the Internet of Things technology that comprehensively uses big data, cloud computing, blockchain and related information technologies to achieve real-time response and intelligent optimization decision-making through comprehensive perception, identification and tracking of logistics operation status.

[0038] The existing logistics vehicle dispatching cannot take into account the coordination of people (drivers), vehicles and goods. To address this problem, this application proposes a smart logistics vehicle dispatching system, such as Figure 2As shown, the scheduling system includes:

[0039] The task acquisition module 100 is used to acquire first carrier information of the logistics task. The first carrier information includes the starting point, end point, estimated departure time and estimated carrying demand of the logistics task. Specifically, the first carrier information is sent to the task acquisition module 100 via the client 106. The client 106 may be a mobile phone, a tablet or a computer.

[0040] The task area construction module 101 is used to construct a task area with the starting point of the logistics task in the first carrier information as the center, and obtain the second carrier information of the driver in the task area, wherein the second carrier information includes the driver's historical carrier information, vehicle information and current carrier task status, wherein the vehicle information at least includes the actual carrying capacity of the vehicle, and the current carrier task status at least includes the estimated completion time of the current carrier task;

[0041] Specifically, the starting point of the logistics task in the first carrier information can be used as the center, and the information of the drivers corresponding to the vehicles whose positioning information is within a radius of 10km can be obtained through the vehicle terminal 107. For example, if there are six vehicles A, B, C, D, E, and F within a radius of 10km, the corresponding information of the six vehicles is obtained;

[0042] The target vehicle determination module 102 is used to determine the target vehicle in the task area according to the comparison result between the actual carrying capacity of the current vehicles in the task area and the expected carrying capacity. As mentioned above, assuming that the expected carrying capacity of this logistics task is 10 tons, the actual carrying capacity of vehicle A is 5 tons, and the actual carrying capacity of vehicles B, C, D, E, and F are all greater than 10 tons, then vehicles B, C, D, E, and F are determined as target vehicles, and the corresponding drivers are determined as target drivers;

[0043] The task allocation module 103 is used to send the logistics task to the task terminal of the target vehicle, that is, the vehicle terminal 107, and send the instruction of the confirmed / unconfirmed logistics task to be executed within a limited time period to the task terminal of the target vehicle. In actual use, the logistics task with an estimated carrying demand of 10 tons is sent to the task terminals of five vehicles B, C, D, E, and F, and the drivers corresponding to the five vehicles are prompted to confirm whether to accept the logistics task within the limited time. Preferably, the limited time is usually set to 30 minutes;

[0044] The confirmation request response module 104 has a pre-trained recommendation model stored therein, and is used to receive the confirmation request from the target vehicle task terminal (i.e., the vehicle-mounted terminal 107), and input the historical carrier information of each driver corresponding to the target vehicle that sent the confirmation request into the pre-trained recommendation model. Assuming that the five vehicles B, C, D, E, and F all confirm that they can accept this logistics task within 30 minutes, the historical carrier information of the corresponding five vehicles is input into the pre-trained recommendation model;

[0045] The result output module 105 is used to send the optimal result of the recommendation model to the logistics task initiator, that is, the client 106.

[0046] This application proposes a smart logistics vehicle scheduling method, referring to Figure 1 , the scheduling method comprises steps S11-S14:

[0047] S11, obtaining first carrier information of the logistics task, where the first carrier information includes a starting point, an end point, an estimated departure time, and an estimated carrying demand of the logistics task;

[0048] S12, constructing a task area with the starting point of the logistics task in the first carrier information as the center, and obtaining second carrier information of the driver in the task area, wherein the second carrier information includes the driver's historical carrier information, vehicle information and current carrier task status, wherein the vehicle information at least includes the actual carrying capacity of the vehicle, and the current carrier task status at least includes the estimated completion time of the current carrier task;

[0049] S13, according to the comparison result between the actual carrying capacity of the current vehicles in the task area and the estimated carrying demand, determine the target vehicle in the task area, send the logistics task to the task terminal of the target vehicle, and generate a pending logistics task to be confirmed / unconfirmed within a limited time period at the task terminal of the target vehicle;

[0050] S14. In response to the confirmation request of the target vehicle task terminal, the historical transportation information of each driver corresponding to the target vehicle sending the confirmation request is input into a pre-trained evaluation model, and the optimal result output by the evaluation model is sent to the logistics task initiator.

[0051] In the above steps, it should be noted that the estimated completion time of the current transport task in the second transport information is earlier than the estimated departure time in the first transport information.

[0052] Specifically, in step S13, based on the comparison result between the actual load capacity of the current vehicle and the expected load demand in the mission area, the target vehicle determination condition in the mission area is determined as follows: if the actual load capacity of the current vehicle is greater than or equal to the expected load demand, the vehicle is determined to be the target vehicle, as exemplified in the above system.

[0053] In one embodiment of the present invention, the historical transport information of each driver corresponding to the target vehicle sending the confirmation request includes each driver's route preference, historical task punctuality rate and customer satisfaction, and the expression of the evaluation model is:

[0054] S=W1×X1+W2×X2+W3×X3 (1),

[0055] In expression (1), S represents the comprehensive score, X1 represents the standard value of the driver's route preference, X2 represents the standard value of the historical task punctuality rate, X3 represents the standard value of customer satisfaction, W1 represents the weight of the driver's route preference, W2 represents the weight of the historical task punctuality rate, and W3 represents the weight of customer satisfaction.

[0056] It should be noted that the values ​​of W1, W2 and W3 can be assigned differently according to the specific conditions of different regions, as long as the sum of W1, W2 and W3 is equal to 1, for example, W1=0.4, W2=0.3, W3=0.3.

[0057] Correspondingly, the process of obtaining the standard value of the driver's driving route preference includes steps S21-S23:

[0058] S21, obtaining the pre-selected distance of the preferred route of the initiator of all historical logistics tasks completed by all target vehicles;

[0059] S22, obtaining the actual distance of the actual driving route of all target vehicles corresponding to the historical logistics tasks completed;

[0060] S23. Determine corresponding route similarities based on the multiple pre-selected distances and the corresponding multiple actual distances, determine a route similarity set, and determine a standard value of the driver's driving route preference based on the route similarity set.

[0061] Correspondingly, the expression of the route similarity of a single driver's single logistics task is:

[0062]

[0063] In expression (2), A represents the route similarity of a single driver’s single logistics task, L yx Indicates the pre-selected distance of the preferred route, L sj Indicates the actual distance;

[0064] The expression of the standard value X1 of the driver's driving route preference is:

[0065]

[0066] In expression (3), A i represents the average route similarity of the target vehicle corresponding to the i-th driver, A min represents the minimum value in the route similarity set determined by expression (2), A max represents the maximum value in the route similarity set determined by expression (2).

[0067] Specifically, it is necessary to calculate the route similarity of each vehicle for each logistics task according to expression (2), and form a route similarity set with the calculated route similarities. Then, the average value of the route similarity of each vehicle is calculated, and the standard value of the route preference of the corresponding driver is calculated according to expression (3).

[0068] The average value of each vehicle's route similarity is equal to the ratio of the sum of each vehicle's route similarities for each mission to the number of missions.

[0069] Correspondingly, the process of obtaining the standard value of the historical task punctuality rate includes steps S31-S32:

[0070] S31, obtaining the corresponding on-time rates of historical logistics tasks completed by all target vehicles;

[0071] S32, construct a historical logistics task on-time rate set for all target vehicles, and determine the standard value of the historical task on-time rate of the corresponding target vehicle according to the historical logistics task on-time rate set; wherein the expression of the standard value X2 of the historical task on-time rate is:

[0072]

[0073] In expression (4), B j represents the average of the historical on-time rate of logistics tasks of the jth target vehicle, B min represents the minimum on-time rate of the historical logistics task on-time rate set, B max It represents the maximum on-time rate of the historical logistics task on-time rate set.

[0074] Specifically, the historical on-time rate of logistics tasks of a single vehicle is equal to the ratio of the number of on-time tasks among all the logistics tasks completed by the vehicle to the total number of tasks, and the average historical on-time rate of logistics tasks of a single vehicle is equal to the ratio of the sum of the on-time rates of each task of each vehicle to the number of tasks.

[0075] Correspondingly, the process of obtaining the standard value of customer satisfaction includes steps S41-S42:

[0076] S41, obtaining the average score of customer satisfaction corresponding to the historical logistics tasks completed by each target vehicle;

[0077] S42, constructing a score set of customer satisfaction of all target vehicles, and determining a standard value of customer satisfaction of the corresponding target vehicle according to the score set of customer satisfaction; wherein the expression of the standard value X3 of customer satisfaction is:

[0078]

[0079] In expression (5), C k represents the average customer satisfaction score corresponding to the historical logistics tasks completed by the k-th driver, C min represents the minimum value of the score set constructed by the customer satisfaction of all target vehicles, C max Represents the maximum value of the score set constructed from the customer satisfaction of all target vehicles.

[0080] Specifically, in step S41, the average score of customer satisfaction corresponding to a single target vehicle is equal to the ratio of the sum of customer satisfaction for each vehicle and each task to the number of tasks.

[0081] Preferably, in the present application, customer satisfaction is divided into five levels of evaluation: 1 point, 2 points, 3 points, 4 points, and 5 points, and after each logistics task is completed, the client 106 will always feedback a score.

[0082] In one embodiment of the present invention, the first carrier information also includes transportation requirements, which include at least whether the vehicle is a closed compartment. The vehicle information in the second carrier information also includes the closed state of the vehicle. In actual use, restrictions on the closedness of the compartment can be added to further improve the matching of the vehicle and cargo logistics needs.

[0083] Compared with the prior art, the intelligent logistics vehicle scheduling method of the present application has the following beneficial effects:

[0084] 1. Improve logistics scheduling efficiency: By comprehensively considering factors such as the actual vehicle load capacity, expected load demand, driver route preference, historical task punctuality rate and customer satisfaction, it is possible to more accurately match appropriate vehicles and drivers, thereby improving the efficiency and accuracy of logistics scheduling.

[0085] 2. Reduce logistics costs: By optimizing vehicle scheduling plans, reducing idle driving and waiting time, reducing fuel consumption and labor costs, and thus reducing overall logistics costs.

[0086] 3. Improve customer satisfaction: By considering customer satisfaction as one of the evaluation criteria, it is possible to better meet customer needs, improve customer satisfaction with logistics services, and enhance customer loyalty.

[0087] 4. Enhanced scheduling flexibility: The intelligent logistics vehicle scheduling method of the present invention can dynamically adjust the scheduling plan according to real-time data, respond to emergencies, and improve the flexibility and adaptability of the scheduling system.

[0088] 5. Improve driver job satisfaction: By considering the driver's route preferences, we can arrange tasks that better suit their preferences for the driver, thereby improving the driver's job satisfaction and reducing driver turnover.

[0089] In summary, the intelligent logistics vehicle scheduling method of the present invention can not only improve the efficiency and accuracy of logistics scheduling and reduce logistics costs, but also improve customer satisfaction and driver job satisfaction, and has significant practical value and market prospects.

[0090] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0092] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0095] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0096] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0097] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A smart logistics vehicle dispatching method, characterized in that: The scheduling method comprises: Acquire first carrier information of the logistics task, where the first carrier information includes a starting point, an end point, an estimated departure time, and an estimated carrying demand of the logistics task; A task area is constructed with the starting point of the logistics task in the first carrier information as the center, and second carrier information of the driver in the task area is obtained, wherein the second carrier information includes the driver's historical carrier information, vehicle information and current carrier task status, wherein the vehicle information at least includes the actual carrying capacity of the vehicle, and the current carrier task status at least includes the estimated completion time of the current carrier task; According to the comparison result between the actual carrying capacity of the current vehicles in the task area and the expected carrying capacity, the target vehicle in the task area is determined, the logistics task is sent to the task terminal of the target vehicle, and a pending logistics task to be confirmed / unconfirmed within a limited time period is generated at the task terminal of the target vehicle; In response to the confirmation request from the target vehicle task terminal, the historical transportation information of each driver corresponding to the target vehicle sending the confirmation request is input into the pre-trained evaluation model, and the optimal result output by the evaluation model is sent to the logistics task initiator.

2. According to claim 1, a smart logistics vehicle dispatching method is characterized in that: The historical transport information of each driver corresponding to the target vehicle sending the confirmation request includes each driver's driving route preference, historical task punctuality rate and customer satisfaction. The expression of the evaluation model is: S=W1×X1+W2×X2+W3×X3 (1), In expression (1), S represents the comprehensive score, X1 represents the standard value of the driver's route preference, X2 represents the standard value of the historical task punctuality rate, X3 represents the standard value of customer satisfaction, W1 represents the weight of the driver's route preference, W2 represents the weight of the historical task punctuality rate, and W3 represents the weight of customer satisfaction.

3. A smart logistics vehicle dispatching method according to claim 2, characterized in that: The process of obtaining the standard value of the driver's driving route preference includes: Obtain the pre-selected distance of the preferred route of the initiator of all historical logistics tasks completed by all target vehicles; Obtain the actual distance of the vehicle's actual driving route corresponding to the historical logistics tasks completed by all target vehicles; According to a plurality of preselected distances and a corresponding plurality of actual distances, corresponding route similarities are determined, and a route similarity set is determined, and a standard value of the driver's driving route preference is determined according to the route similarity set.

4. A smart logistics vehicle dispatching method according to claim 3, characterized in that: The expression of the route similarity of a single driver's single logistics task is: In expression (2), A represents the route similarity of a single driver’s single logistics task, L yx Indicates the pre-selected distance of the preferred route, L sj Indicates the actual distance; The expression of the standard value X1 of the driver's driving route preference is: In expression (3), A i represents the average route similarity of the target vehicle corresponding to the i-th driver, A min represents the minimum value in the route similarity set determined by expression (2), A max represents the maximum value in the route similarity set determined by expression (2).

5. According to claim 2, a smart logistics vehicle dispatching method is characterized in that: The process of obtaining the standard value of the historical task punctuality rate includes: Obtain the corresponding on-time rates of all historical logistics tasks completed by all target vehicles; Construct a historical logistics task on-time rate set for all target vehicles, and determine the standard value of the historical task on-time rate of the corresponding target vehicle based on the historical logistics task on-time rate set; wherein the expression of the standard value X2 of the historical task on-time rate is: In expression (4), B j represents the average of the historical on-time rate of logistics tasks of the jth target vehicle, B min represents the minimum on-time rate of the historical logistics task on-time rate set, B max It represents the maximum on-time rate of the historical logistics task on-time rate set.

6. A smart logistics vehicle dispatching method according to claim 2, characterized in that: The process of obtaining the standard value of customer satisfaction includes: Obtain the average customer satisfaction score corresponding to the historical logistics tasks completed by each target vehicle; Construct a score set of customer satisfaction of all target vehicles, and determine the standard value of customer satisfaction of the corresponding target vehicle according to the score set of customer satisfaction; wherein the expression of the standard value X3 of customer satisfaction is: In expression (5), C k represents the average customer satisfaction score corresponding to the historical logistics tasks completed by the k-th driver, C min represents the minimum value of the score set constructed by the customer satisfaction of all target vehicles, C max Represents the maximum value of the score set constructed from the customer satisfaction of all target vehicles.

7. The intelligent logistics vehicle dispatching method according to claim 1 is characterized in that: The estimated completion time of the current transport task in the second transport information is earlier than the estimated departure time in the first transport information.

8. A smart logistics vehicle dispatching method according to claim 7, characterized in that: The first carrier information also includes transportation requirements, and the transportation requirements at least include whether the vehicle is a closed compartment. The vehicle information in the second carrier information also includes the closed status of the vehicle.

9. A smart logistics vehicle dispatching method according to claim 8, characterized in that: According to the comparison result between the actual load capacity of the current vehicle and the expected load demand in the mission area, the target vehicle determination condition in the mission area is determined as follows: if the actual load capacity of the current vehicle is greater than or equal to the expected load demand, the vehicle is determined to be the target vehicle.

10. A smart logistics vehicle dispatching system, the dispatching system is used to execute the dispatching method according to any one of claims 1 to 9, characterized in that: The dispatching system comprises: A task acquisition module is used to obtain the first carrier information of the logistics task; A task area construction module is used to construct a task area with the starting point of the logistics task in the first carrier information as the center, and obtain the second carrier information of the driver in the task area; A target vehicle determination module is used to determine the target vehicle in the mission area based on the comparison result between the actual carrying capacity of the current vehicles in the mission area and the estimated carrying demand; A task assignment module is used to send the logistics task to the task terminal of the target vehicle, and send instructions for confirming / not confirming the logistics task to be executed within a limited time period to the task terminal of the target vehicle; A confirmation request response module, which has a pre-trained recommendation model stored therein, is used to receive a confirmation request from a target vehicle task terminal, and input the historical carrier information of each driver corresponding to the target vehicle sending the confirmation request into the pre-trained recommendation model; The result output module is used to send the optimal result of the recommendation model to the logistics task initiator.

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