Logistics distribution path planning method and system, and computer equipment

Abstract

The invention provides a logistics distribution path planning method and system, and computer equipment. The method comprises the steps that historical distribution path information is acquired, data processing is carried out on the historical distribution path information, and the historical distribution path information comprises starting point information, terminal point information, passing point information and time information. Distribution parameters are determined according to the processed historical distribution path information, an intelligent path recommendation model is determined based on the distribution parameters, and the distribution parameters comprise the route length, the number of passing points and traffic condition information. And finally, to-be-delivered position information and traffic information are obtained, a path planning route is output based on the traffic information and the to-be-delivered position information through the path intelligent recommendation model, and the to-be-delivered position information at least comprises one of to-be-delivered starting point information, to-be-delivered terminal point information, to-be-delivered passing point information and to-be-delivered time information.

Description

Technical Field

[0001] The present application relates to the field of logistics technology, and in particular to a logistics distribution path planning method, system, and computer equipment. Background Art

[0002] With the rapid development of the logistics industry, the market demand for efficient, accurate, and customer-oriented services is growing. In the logistics and distribution industry, the selection and planning of delivery routes are of great significance to improving delivery efficiency and reducing costs. However, the existing delivery routes basically rely on the manual judgment of salesmen, and the existing recommendation methods are often based on simple distance calculations or static route planning, which fails to fully consider the dynamic information and implicit rules in historical delivery routes. Therefore, how to perform route planning to improve delivery efficiency is currently an urgent problem to be solved. Summary of the invention

[0003] Based on this, a logistics distribution path planning method, system, and computer equipment are provided to solve the above technical problems.

[0004] A logistics distribution path planning method, comprising:

[0005] Acquire historical delivery path information and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information;

[0006] Determine delivery parameters according to the processed historical delivery path information, and determine a route intelligent recommendation model based on the delivery parameters, wherein the delivery parameters include route length, number of waypoints, and traffic condition information;

[0007] The delivery location information and traffic information are obtained, and the path planning route is output based on the traffic information and the delivery location information through the path intelligent recommendation model, wherein the delivery location information includes at least one of the delivery starting point information, delivery destination information, delivery waypoint information, and delivery time information.

[0008] In one embodiment, the step of obtaining historical delivery path information and performing data processing on the historical delivery path information includes:

[0009] The historical delivery path information is obtained, and the historical delivery path information is denoised and normalized.

[0010] In one embodiment, the step of determining the delivery parameters according to the processed historical delivery path information, and determining the route intelligent recommendation model based on the delivery parameters includes:

[0011] Dividing the processed historical delivery route information into time periods and determining delivery parameters within each time period;

[0012] The corresponding intelligent path recommendation model is determined according to the delivery parameters in each time period, wherein the intelligent path recommendation model is a recurrent neural network recommendation model or a long short-term memory network recommendation model.

[0013] In one embodiment, the step of obtaining the to-be-delivered location information and traffic information, and outputting a path planning route based on the traffic information and the to-be-delivered location information through the path intelligent recommendation model includes:

[0014] Receive an operation instruction input by a user, and obtain the to-be-delivered location information according to the operation instruction;

[0015] Traffic information is obtained, and a path planning route is output based on the traffic information and the location information to be delivered through the path intelligent recommendation model.

[0016] In one embodiment, the method further comprises:

[0017] Obtaining real-time traffic information and determining an estimated time of arrival based on the route planning;

[0018] Obtaining the delivery time information in the delivery location information;

[0019] Determine whether to issue a delivery abnormality alarm based on the to-be-delivered time information and the estimated arrival time.

[0020] In one embodiment, the step of determining whether to issue a delivery abnormality alarm based on the to-be-delivered time information and the estimated arrival time includes:

[0021] If the estimated arrival time is before the pending delivery time information, no delivery abnormality alarm is issued;

[0022] If the estimated arrival time is after the to-be-delivered time information, a delivery abnormality alarm is issued, wherein the delivery abnormality alarm includes a telephone alarm and / or an audio alarm.

[0023] A logistics distribution path planning system, comprising:

[0024] An acquisition module, used to acquire historical delivery path information and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information;

[0025] a processing module connected to the acquisition module, the processing module being used to determine the delivery parameters according to the processed historical delivery path information, and to determine the intelligent path recommendation model based on the delivery parameters, the delivery parameters including the route length, the number of waypoints and traffic condition information;

[0026] A planning module is connected to the processing module, and is used to obtain the location information and traffic information of the delivery location, and output the path planning route based on the traffic information and the location information of the delivery location through the path intelligent recommendation model. The location information of the delivery location includes at least one of the starting point information of the delivery location, the end point information of the delivery location, the passing point information of the delivery location, and the time information of the delivery location.

[0027] In one embodiment, the logistics distribution path planning system further includes:

[0028] The alarm module is connected to the planning module and is used to obtain real-time traffic information, determine the estimated arrival time according to the path planning route, obtain the delivery time information in the delivery location information, and determine whether to issue a delivery abnormality alarm according to the delivery time information and the estimated arrival time.

[0029] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in any one of Embodiments 1 to 6 above when executing the computer program.

[0030] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of Embodiments 1 to 6 above.

[0031] Compared with the prior art, the above-mentioned logistics distribution path planning method, system, and computer device first obtain historical distribution path information and perform data processing on the historical distribution path information, wherein the historical distribution path information includes starting point information, end point information, waypoint information, and time information. Secondly, the distribution parameters are determined according to the processed historical distribution path information, and the path intelligent recommendation model is determined based on the distribution parameters, wherein the distribution parameters include route length, number of waypoints, and traffic conditions information. Finally, the location information and traffic information to be delivered are obtained, and the path planning route is output based on the traffic information and the location information to be delivered through the path intelligent recommendation model, wherein the location information to be delivered includes at least one of the starting point information to be delivered, the end point information to be delivered, the waypoint information to be delivered, and the time information to be delivered. The present application adopts the above-mentioned method, analyzes the historical distribution path information, and comprehensively considers dynamic information such as time factors and traffic conditions, so that the output path planning route is more in line with the actual situation and improves the accuracy of the recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flow chart of a logistics distribution path planning method provided in one embodiment of the present application;

[0034] Figure 2 A structural block diagram of a logistics distribution path planning system provided in one embodiment of the present application;

[0035] Figure 3 This is a diagram of the internal structure of a computer device provided in one embodiment of the present application.

[0036] Description of reference numerals:

[0037] 10. Logistics distribution path planning system; 100. Acquisition module; 200. Processing module; 300. Planning module. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0039] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings). In the description of this application, it should be understood that the orientation or position relationship indicated by the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to this application.

[0040] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0041] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0043] See also Figure 1 , an embodiment of the present application provides a logistics distribution path planning method. The method includes:

[0044] S102: Acquire historical delivery path information and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information.

[0045] In some embodiments, historical delivery path information may be obtained through a data center, and data processing may be performed on the historical delivery path information. Specifically, the data center may collect historical pick-up and dispatch route data (i.e., historical delivery path information), including information such as the starting point, the end point, the waypoints, and the timestamp. In some embodiments, the data center pre-processes the collected and obtained historical delivery path information. Specifically, the historical delivery path information may be denoised, normalized, and the like to ensure the quality of the data information.

[0046] S104: Determine delivery parameters according to the processed historical delivery path information, and determine a path intelligent recommendation model based on the delivery parameters, wherein the delivery parameters include route length, number of waypoints and traffic condition information.

[0047] In some embodiments, the data center can determine the delivery parameters based on the processed historical delivery path information, and determine the path intelligent recommendation model based on the delivery parameters. Specifically, the data center can extract key delivery parameters from the processed historical delivery path information, such as route length, number of waypoints, traffic conditions and other information. Then determine the path intelligent recommendation model based on the extracted key delivery parameters. In some embodiments, the intelligent recommendation model can use deep learning technology, such as recurrent neural networks (RNN) or long short-term memory networks (LSTM) to capture dependencies in sequence data. By training the model, it can learn the implicit rules in the dispatch routes in the historical delivery path information.

[0048] S106: Obtaining the location information and traffic information of the delivery location, and outputting the path planning route based on the traffic information and the location information of the delivery location through the path intelligent recommendation model, wherein the location information of the delivery location includes at least one of the starting point information of the delivery location, the end point information of the delivery location, the passing point information of the delivery location, and the time information of the delivery location.

[0049] In some embodiments, the location information and traffic information to be delivered can be obtained through the data center, and the path intelligent recommendation model can output the path planning route based on the traffic information and the location information to be delivered. Specifically, when the user inputs information such as the starting point, the end point, and optional waypoints, the intelligent recommendation model recommends the best pick-up route (i.e., the path planning route) to the user based on factors such as current traffic information and the location information to be delivered. Among them, the path planning route may include information such as the specific route, estimated time, and cost estimate. In this way, users can make more informed decisions. This embodiment adopts the above method, by analyzing the historical delivery path information, and comprehensively considering dynamic information such as time factors and traffic conditions, so that the output path planning route is more in line with the actual situation and improves the accuracy of the recommendation.

[0050] In some embodiments, the step of determining the delivery parameters according to the processed historical delivery path information and determining the path intelligent recommendation model based on the delivery parameters includes: dividing the processed historical delivery path information into time periods and determining the delivery parameters within each time period; determining the corresponding path intelligent recommendation model according to the delivery parameters of each time period, wherein the path intelligent recommendation model is a recurrent neural network recommendation model or a long short-term memory network recommendation model.

[0051] In some embodiments, the processed historical delivery path information can be divided according to time periods. The specific time periods can be set according to actual needs. For example, 1pm to 4pm can be divided into a time period, and 5pm to 7pm can be divided into a time period. Since 5pm to 7pm is the peak commuting time, the traffic conditions are more complicated than 1pm to 4pm. After the time period division is completed, the delivery parameters within each time period can be further determined, and the corresponding path intelligent recommendation model can be determined according to the delivery parameters of each time period. In this way, the route selection rules of different time periods can be captured, so that the recommendation results of the path intelligent recommendation model are more in line with the actual situation.

[0052] In some embodiments, the intelligent route recommendation model may adopt a recurrent neural network (RNN) recommendation model or a long short-term memory network (LSTM) recommendation model, which can capture the data dependency in the processed historical delivery route information, thereby further improving the accuracy of the recommendation. In some embodiments, the intelligent route recommendation model may also be trained to enable it to learn the implicit rules of the routes in the historical delivery route information to adapt to the route recommendation needs in different scenarios.

[0053] In some embodiments, the step of obtaining the information of the location to be delivered and traffic information, and outputting the path planning route based on the traffic information and the location to be delivered through the path intelligent recommendation model includes: receiving an operation instruction input by a user, and obtaining the location to be delivered according to the operation instruction; obtaining traffic information, and outputting the path planning route based on the traffic information and the location to be delivered through the path intelligent recommendation model.

[0054] In some embodiments, the operation instruction may be a starting point instruction, an end point information instruction, and an optional waypoint information instruction, etc. The to-be-delivered location information is obtained according to the operation instruction input by the user. For example, if the input operation instruction is a starting point instruction, the to-be-delivered location information is obtained as the starting point location information; if the input operation instruction is an end point instruction, the to-be-delivered location information is obtained as the end point location information. In some embodiments, after obtaining the to-be-delivered location information, the traffic information of the current time period may be obtained synchronously, and the path planning route may be output based on the traffic information and the to-be-delivered location information through the path intelligent recommendation model, so that the path planning may be completed, so that the user can make a wise decision.

[0055] In some embodiments, the method further includes: obtaining real-time traffic information and determining an estimated arrival time based on the path planning route; obtaining delivery time information in the delivery location information; and determining whether to issue a delivery abnormality alarm based on the delivery time information and the estimated arrival time.

[0056] In some embodiments, if the estimated arrival time is before the time information to be delivered, no delivery abnormality alarm is performed. In other words, the logistics delivery time is still within the controllable range at this time, and there will be no timeout. In some embodiments, if the estimated arrival time is after the time information to be delivered, a delivery abnormality alarm is performed. In other words, the logistics delivery time may be timed out due to various factors. At this time, the data center can perform a delivery abnormality alarm. In some embodiments, the method of abnormal alarm is not limited, such as telephone alarm, audio alarm, etc. Specifically, when an abnormal alarm occurs, the data center can make a call with one click, push a message with one click, push a positioning reminder with one click, and so on. In this way, management personnel can be reminded to intervene in abnormal situations in a timely manner, thereby improving the operational efficiency and service quality of logistics distribution.

[0057] In some embodiments, when an abnormal alarm occurs, the manager can accurately locate the alarm vehicle and the corresponding specific personnel through the data center, so as to facilitate targeted management and improvement of the personnel. In some embodiments, when there are multiple logistics distribution vehicles, the manager can view the en route status, operation route, and delivery results of each vehicle in real time through the data center, so as to monitor the overall data indicators of the logistics distribution outlets, including the first collection timeliness rate of scattered orders, the time of arrival at the port, the timeliness rate of receipt, the false receipt rate of the entire volume, the happy fruit response rate, the complaint rate, etc. At the same time, the quality of outlet operation can be discovered through data, and targeted improvements can be made to the indicators with poor operation, so as to improve the outlet operation efficiency and service quality.

[0058] See also Figure 2 Another embodiment of the present application provides a logistics distribution path planning system 10. The logistics distribution path planning system 10 includes: an acquisition module 100, a processing module 200, and a planning module 300. The acquisition module 100 is used to acquire historical distribution path information and perform data processing on the historical distribution path information, and the historical distribution path information includes starting point information, end point information, waypoint information, and time information. The processing module 200 is connected to the acquisition module 100.

[0059] The processing module 200 is used to determine the delivery parameters according to the processed historical delivery path information, and determine the path intelligent recommendation model based on the delivery parameters, wherein the delivery parameters include the route length, the number of waypoints and traffic conditions. The planning module 300 is connected to the processing module 200, and the planning module 300 is used to obtain the location information and traffic information to be delivered, and output the path planning route based on the traffic information and the location information to be delivered through the path intelligent recommendation model, wherein the location information to be delivered includes at least one of the starting point information to be delivered, the end point information to be delivered, the waypoint information to be delivered, and the time information to be delivered.

[0060] In some embodiments, the specific structure of the acquisition module 100 is not limited, as long as it has the function of acquiring historical delivery path information and performing data processing on the historical delivery path information. Specifically, the acquisition module 100 can be a controller, a collector, etc. The specific structure of the acquisition module 100 can be selected according to actual needs and is not specifically limited here.

[0061] In some embodiments, the specific structure of the processing module 200 is not limited, as long as it has the function of determining the delivery parameters according to the processed historical delivery path information and determining the route intelligent recommendation model based on the delivery parameters. Specifically, the processing module 200 can be a microprocessor or an integrated controller. The specific structure of the processing module 200 can be selected according to actual needs and is not specifically limited here.

[0062] In some embodiments, the specific structure of the planning module 300 is not limited, as long as it has the function of obtaining the location information and traffic information to be delivered, and outputting the path planning route based on the traffic information and the location information to be delivered through the path intelligent recommendation model. Specifically, the planning module 300 can be a microprocessor or an integrated controller. The specific structure of the planning module 300 can be selected according to actual needs and is not specifically limited here. This embodiment analyzes the historical delivery path information and comprehensively considers dynamic information such as time factors and traffic conditions, so that the output path planning route is more in line with the actual situation and improves the accuracy of the recommendation.

[0063] In some embodiments, the logistics distribution path planning system further includes: an alarm module 400. The alarm module 400 is connected to the planning module. The alarm module 400 is used to obtain real-time traffic information, determine an estimated arrival time according to the path planning route, obtain the time information to be delivered in the location information to be delivered, and determine whether to issue a delivery abnormality alarm according to the time information to be delivered and the estimated arrival time.

[0064] In some embodiments, the specific structure of the alarm module 400 is not limited, as long as it has the function of obtaining real-time traffic information, determining the estimated arrival time according to the route planning route, obtaining the time information to be delivered in the location information to be delivered, and determining whether to issue a delivery abnormality alarm according to the time information to be delivered and the estimated arrival time. Specifically, the alarm module 400 can be a buzzer alarm, a telephone alarm, etc. The specific structure of the alarm module 400 can be selected according to actual needs and is not specifically limited here.

[0065] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a logistics distribution path planning method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0066] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0067] See also Figure 3 Another embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the logistics distribution path planning method described in any one of the above embodiments are implemented.

[0068] In one embodiment, the processor implements the following steps when executing the computer program:

[0069] S102: Acquire historical delivery path information, and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information;

[0070] S104: Determine delivery parameters according to the processed historical delivery path information, and determine a route intelligent recommendation model based on the delivery parameters, wherein the delivery parameters include route length, number of waypoints, and traffic condition information;

[0071] S106: Obtaining the location information and traffic information of the delivery location, and outputting the path planning route based on the traffic information and the location information of the delivery location through the path intelligent recommendation model, wherein the location information of the delivery location includes at least one of the starting point information of the delivery location, the end point information of the delivery location, the passing point information of the delivery location, and the time information of the delivery location.

[0072] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the logistics distribution path planning method described in any one of the above embodiments are implemented.

[0073] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0074] S102: Acquire historical delivery path information, and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information;

[0075] S104: Determine delivery parameters according to the processed historical delivery path information, and determine a route intelligent recommendation model based on the delivery parameters, wherein the delivery parameters include route length, number of waypoints, and traffic condition information;

[0076] S106: Obtaining the location information and traffic information of the delivery location, and outputting the path planning route based on the traffic information and the location information of the delivery location through the path intelligent recommendation model, wherein the location information of the delivery location includes at least one of the starting point information of the delivery location, the end point information of the delivery location, the passing point information of the delivery location, and the time information of the delivery location.

[0077] The above-mentioned computer device and computer-readable storage medium analyze the historical delivery path information and comprehensively consider dynamic information such as time factors and traffic conditions, so that the output path planning route is more in line with the actual situation and improves the accuracy of the recommendation.

[0078] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0079] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A logistics distribution path planning method, characterized in that: include: Acquire historical delivery path information and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information; Determine delivery parameters according to the processed historical delivery path information, and determine a route intelligent recommendation model based on the delivery parameters, wherein the delivery parameters include route length, number of waypoints, and traffic condition information; The delivery location information and traffic information are obtained, and the path planning route is output based on the traffic information and the delivery location information through the path intelligent recommendation model, wherein the delivery location information includes at least one of the delivery starting point information, delivery destination information, delivery waypoint information, and delivery time information.

2. The logistics distribution path planning method according to claim 1, characterized in that: The step of obtaining historical delivery path information and performing data processing on the historical delivery path information includes: The historical delivery path information is obtained, and the historical delivery path information is denoised and normalized.

3. The logistics distribution path planning method according to claim 1, characterized in that: The step of determining the delivery parameters according to the processed historical delivery path information and determining the route intelligent recommendation model based on the delivery parameters includes: Dividing the processed historical delivery route information into time periods and determining delivery parameters within each time period; The corresponding intelligent path recommendation model is determined according to the delivery parameters in each time period, wherein the intelligent path recommendation model is a recurrent neural network recommendation model or a long short-term memory network recommendation model.

4. The logistics distribution path planning method according to claim 1, characterized in that: The steps of obtaining the information of the location to be delivered and traffic information, and outputting the path planning route based on the traffic information and the location to be delivered by the intelligent path recommendation model include: Receive an operation instruction input by a user, and obtain the to-be-delivered location information according to the operation instruction; Traffic information is obtained, and a path planning route is output based on the traffic information and the location information to be delivered through the path intelligent recommendation model.

5. The logistics distribution path planning method according to claim 4, characterized in that: The method further comprises: Obtaining real-time traffic information and determining an estimated time of arrival based on the route planning; Obtaining the delivery time information in the delivery location information; Determine whether to issue a delivery abnormality alarm based on the to-be-delivered time information and the estimated arrival time.

6. The logistics distribution path planning method according to claim 5, characterized in that: The step of determining whether to issue a delivery abnormality alarm based on the to-be-delivered time information and the estimated arrival time comprises: If the estimated arrival time is before the pending delivery time information, no delivery abnormality alarm is issued; If the estimated arrival time is after the to-be-delivered time information, a delivery abnormality alarm is issued, wherein the delivery abnormality alarm includes a telephone alarm and / or an audio alarm.

7. A logistics distribution path planning system, characterized in that: include: An acquisition module, used to acquire historical delivery path information and perform data processing on the historical delivery path information, wherein the historical delivery path information includes starting point information, end point information, waypoint information, and time information; a processing module connected to the acquisition module, the processing module being used to determine the delivery parameters according to the processed historical delivery path information, and to determine the intelligent path recommendation model based on the delivery parameters, the delivery parameters including the route length, the number of waypoints and traffic condition information; A planning module is connected to the processing module, and is used to obtain the location information and traffic information of the delivery location, and output the path planning route based on the traffic information and the location information of the delivery location through the path intelligent recommendation model. The location information of the delivery location includes at least one of the starting point information of the delivery location, the end point information of the delivery location, the passing point information of the delivery location, and the time information of the delivery location.

8. The logistics distribution path planning system according to claim 7, characterized in that: Also includes: The alarm module is connected to the planning module and is used to obtain real-time traffic information, determine the estimated arrival time according to the path planning route, obtain the delivery time information in the delivery location information, and determine whether to issue a delivery abnormality alarm according to the delivery time information and the estimated arrival time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 7 to 9 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 7 to 9 are implemented.

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