Logistics distribution path determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a logistics distribution path determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining path planning data corresponding to a target distribution task, and determining an initial distribution path according to the path planning data; processing the initial delivery path based on a path optimization algorithm, and determining at least two to-be-selected delivery paths; inputting the to-be-selected delivery path and the path planning data into a delivery path determination model to obtain a path evaluation score corresponding to the to-be-selected delivery path; and determining a target delivery path from the at least two to-be-selected delivery paths according to the path evaluation score. Based on the above technical scheme, the initial delivery path is determined according to the path planning data, and then the initial delivery path is further optimized through the path optimization algorithm and the delivery path determination model to determine the target path, so that the efficiency of determining the logistics delivery path is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics route planning, and in particular to a method, device, electronic device and storage medium for determining a logistics distribution route. Background Art

[0002] With the continuous development of computer technology and logistics technology, in order to reduce the cost in the logistics distribution process, corresponding distribution routes are usually planned for different distribution tasks, thereby reducing distribution costs and improving distribution efficiency.

[0003] However, existing methods for planning item delivery routes often plan delivery routes corresponding to delivery tasks based on single demand information and planning algorithms. When the delivery situation is more complicated and the delivery environment changes in real time, there are problems of low efficiency and difficulty in adapting to dynamic changes. Summary of the invention

[0004] The present invention provides a method, device, electronic device and storage medium for determining a logistics distribution path. An initial distribution path is determined according to path planning data, and the initial distribution path is optimized according to a path optimization algorithm and a distribution path determination model to determine a target distribution path, so as to improve the optimization efficiency of the logistics distribution path.

[0005] According to one aspect of the present invention, a method for determining a logistics distribution path is provided, comprising:

[0006] Acquire path planning data corresponding to the target delivery task, and determine an initial delivery path according to the path planning data;

[0007] Processing the initial delivery path based on a path optimization algorithm to determine at least two delivery paths to be selected, wherein the path optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm;

[0008] Inputting the delivery path to be selected and the path planning data into a delivery path determination model to obtain a path evaluation score corresponding to the delivery path to be selected, wherein the delivery path determination model is a neural network model trained based on historical delivery paths and historical path planning data;

[0009] A target delivery path is determined from the at least two delivery paths to be selected according to the path evaluation score.

[0010] According to another aspect of the present invention, there is provided a device for determining a logistics distribution path, comprising:

[0011] An initial path determination module is used to obtain path planning data corresponding to the target delivery task and determine an initial delivery path according to the path planning data;

[0012] A path optimization module, used for processing the initial delivery path based on a path optimization algorithm to determine at least two delivery paths to be selected, wherein the path optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm;

[0013] a path evaluation module, used for inputting the delivery path to be selected and the path planning data into a delivery path determination model to obtain a path evaluation score corresponding to the delivery path to be selected, wherein the delivery path determination model is a neural network model trained based on historical delivery paths and historical path planning data;

[0014] The target path determination module is used to determine a target delivery path from the at least two delivery paths to be selected according to the path evaluation score.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the logistics distribution path described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a logistics distribution path described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains the path planning data corresponding to the target delivery task, determines the initial delivery path according to the path planning data, processes the initial delivery path based on the path optimization algorithm, determines at least two delivery paths to be selected, inputs the delivery paths to be selected and the path planning data into the delivery path determination model, obtains the path evaluation score corresponding to the delivery paths to be selected, and determines the target delivery path from the at least two delivery paths to be selected according to the path evaluation score. Based on the above technical solution, the initial delivery path is determined according to the path planning data, and then the initial delivery path is further optimized to determine the target path through the path optimization algorithm and the delivery path determination model, thereby improving the efficiency of determining the logistics delivery path.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for determining a logistics distribution path provided by an embodiment of the present invention;

[0024] Figure 2 It is a structural block diagram of a device for determining a logistics distribution path provided by an embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1This is a flow chart of a method for determining a logistics distribution path provided by an embodiment of the present invention. This embodiment is applicable to the situation of ... The method can be executed by a device for determining a logistics distribution path. The device for determining a logistics distribution path can be implemented in the form of hardware and / or software. The device for determining a logistics distribution path can be configured in an electronic device, which can be a terminal device or a server device, etc. Figure 1 As shown, the method includes:

[0030] S110, acquiring path planning data corresponding to the target delivery task, and determining an initial delivery path according to the path planning data.

[0031] The delivery task may be a delivery task generated according to a logistics order, and correspondingly, the target delivery task may be understood as a delivery task that requires path planning. The path planning data may be data required for planning a logistics path corresponding to the delivery task. The initial delivery path may be an unoptimized path generated according to the path planning data.

[0032] Specifically, after obtaining the logistics order, a corresponding delivery task is generated according to the logistics order. For example, the corresponding delivery task may be generated according to the associated information of the logistics order. The delivery task may include information associated with the logistics order, such as the delivery location, delivery time, delivery area, delivery quantity and other information. Then, the corresponding path planning data is obtained according to the target delivery task, and the initial delivery path is determined based on the path planning data. Exemplarily, road condition information may be obtained through real-time traffic data, historical traffic flow information and road restrictions (such as one-way streets, construction areas, etc.). The corresponding path planning data may be obtained through GPS positioning equipment, public data of the traffic management department or the information interface of a third-party traffic information service provider, and the initial delivery path is further determined based on the path planning data.

[0033] On the basis of the above technical solution, the obtaining of path planning data corresponding to the target delivery task includes: determining a delivery location corresponding to the target delivery task; determining a target task area corresponding to the target delivery task based on the delivery location, and obtaining path planning data corresponding to the target task area.

[0034] The delivery location may be a delivery location corresponding to the delivery task, and the delivery location includes a delivery start point and a delivery end point. It should be noted that the delivery end point may be a customer address or a designated delivery location, and the same delivery task may include one or more delivery end points. The target task area may be a delivery area corresponding to the delivery task. The path planning data includes traffic condition data, weather data, and obstacle data.

[0035] Specifically, the delivery location corresponding to the target delivery task is determined, and then the target task area corresponding to the target delivery task is determined based on the delivery location, and the path planning data corresponding to the target task area is obtained. Exemplarily, a target task area including the delivery starting point and all delivery ending points can be determined based on the locations of the delivery starting point and all delivery ending points. For example, the coordinate information corresponding to the delivery location can be determined, and the target task area corresponding to the delivery task can be established based on the coordinate information and the preset area. For example, a rectangular area can be established based on the coordinate information of the delivery starting point and the delivery ending point, and the rectangular area can be enlarged according to a preset ratio to determine the target task area, and then the path planning data corresponding to the target task area can be obtained, such as obtaining traffic status data through real-time traffic data API, public data of traffic management departments, or third-party traffic information service providers. Traffic status data includes road congestion, traffic flow, accident information, construction areas, etc.; obtaining weather data through meteorological stations, weather forecast websites or APIs; and determining obstacle data corresponding to the target area through GIS systems, notifications from traffic management departments, or high-precision maps.

[0036] On the basis of the above technical solution, after obtaining the path planning data corresponding to the target task area, it also includes: determining the data format corresponding to the path planning data, and when the data format is inconsistent with the preset data format, converting the data format of the path planning data into the preset data format.

[0037] The data format can be understood as the data organization and representation used to describe the path planning data, for example, it can be a text format in character form or a compressed format in binary data form. The preset data format can be a pre-set data format, which can be an XML format.

[0038] Specifically, determine the data format corresponding to the path planning data, and when the data format is inconsistent with the preset data format, convert the data format of the path planning data into the preset data format. For example, determine the data source corresponding to each path planning data, and then for each data source, check the format of the path planning data provided by it, compare the data format provided by each data source with the preset data format to determine whether it is consistent with the preset data format. When the data format is inconsistent with the preset data format, convert the data format of the path planning data into the preset data format.

[0039] On the basis of the above technical solution, the determining of the initial delivery path according to the path planning data includes: obtaining the time constraint and the distance constraint corresponding to the target delivery task; determining each path node to be verified corresponding to the target delivery task according to the time constraint, the distance constraint and the path planning data, and determining the initial delivery path based on the each path node to be verified.

[0040] Among them, the time constraint can be the specified delivery time corresponding to the delivery task, and the distance constraint can be understood as the specified driving distance corresponding to the delivery task. The path node to be verified can be understood as the logistics node corresponding to the task area of ​​the target delivery task, such as the starting point, end point, and transfer point corresponding to the logistics task, such as the intersection, nail intersection, highway intersection, etc., which may affect the path selection.

[0041] Specifically, the time constraints (such as the start time, end time, and time windows of each point) and distance constraints (such as the maximum driving distance, shortest path requirements, etc.) of the target delivery task are obtained, and then the key logistics nodes related to the target delivery task are identified based on the task area and path planning data. It should be noted that the path node to be verified can be the starting point, that is, the starting point of the delivery task, such as a warehouse or distribution center; it can be the end point, as well as the transfer point, a place where it may be necessary to stop or transfer during the delivery process, such as a sorting center, distribution station, etc., and it can also be a special node in the delivery process, such as a crossroads, a nail intersection, a highway intersection, etc. Then, the initial delivery path is determined based on the determined path nodes to be verified. For example, a logistics delivery path can be determined based on the path nodes to be verified, and the time cost and distance cost corresponding to the delivery path can be calculated, and then the initial delivery path that meets the time constraints and distance constraints is determined based on the time cost and distance cost corresponding to the delivery path.

[0042] On the basis of the above technical solution, the determining of the initial delivery path based on the path nodes to be verified includes: determining the node evaluation data corresponding to the path nodes to be verified based on the node cost evaluation function; and determining the initial delivery path according to the node evaluation data and the path nodes to be verified.

[0043] The node cost evaluation function may be a pre-set function for calculating the cost required for path nodes, and correspondingly, the node evaluation data may be an evaluation value calculated by the node cost evaluation function for evaluating the cost required for each node.

[0044] Specifically, after determining the path nodes to be verified corresponding to the target delivery task, the node evaluation data corresponding to each path node to be verified is calculated according to the preset node cost evaluation function, and then the initial delivery path that meets the constraint conditions is determined according to the node evaluation data and the path nodes to be verified. For example, the historical cost value corresponding to the path node to be verified can be determined, that is, the cost value of the path node to be verified from the delivery starting point, and the estimated cost value of the path node to be verified from the end point can be determined, and then the node evaluation data corresponding to the current path node to be verified can be determined according to the historical cost value and the estimated cost value. Further, starting from the delivery starting point, the node with the smallest node evaluation data is selected as the current node. If the current node is the target node, the path search is successful, and the path from the starting point to the end point is traced back. If it is not the target node, the node evaluation data of other path nodes corresponding to the current path node to be verified is determined, and the node with the smallest node evaluation data is repeatedly selected as the current node until the current node is the delivery end point.

[0045] S120: Process the initial delivery path based on a path optimization algorithm to determine at least two delivery paths to be selected.

[0046] The path optimization algorithm may be a pre-set algorithm for further optimizing the initial path to determine the path to be selected, and the path optimization algorithm may be a genetic algorithm or a particle swarm optimization algorithm. The delivery path to be selected may be understood as a path obtained by the path optimization algorithm.

[0047] Specifically, the initial distribution path is processed based on the path optimization algorithm to determine at least two distribution paths to be selected. The path optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm. For example, the initial distribution path, time constraint, distance constraint, and node cost evaluation function are used as inputs of the algorithm, and then the initial distribution path is iteratively optimized using the genetic algorithm or the particle swarm optimization algorithm. In each iteration, a new set of path candidates is generated according to the algorithm rules, and the fitness of each candidate path is evaluated. Such as the logistics path length cost, the logistics path time cost, etc., and the optimal solution set is updated, and then at least two distribution paths to be selected. It should be noted that the distribution path to be selected is a path processed by the path optimization algorithm, which is a better solution obtained according to the node cost evaluation function and the time constraint and distance constraint under the conditions of satisfying the time constraint and the distance constraint. The distribution path to be selected provides a variety of feasible options for logistics distribution, which can be selected or further adjusted according to actual conditions (such as traffic conditions, customer needs, etc.). S130, the distribution path to be selected and the path planning data are input into the distribution path determination model to obtain the path evaluation score corresponding to the distribution path to be selected.

[0048] The delivery path determination model is a neural network model trained based on historical delivery paths and historical path planning data. The path evaluation score can be understood as an evaluation result for evaluating the current delivery path to be selected.

[0049] Specifically, the delivery path to be selected and the path planning data are input into the delivery path determination model to obtain the path evaluation score corresponding to the delivery path to be selected. For example, the delivery path to be selected and the path planning data are used as input, and the delivery path determination model will use its internal neural network structure to process these data, and then the neurons in the model will perform weighted calculations on the input data to extract key information and features from the input data. The path evaluation score corresponding to each delivery path to be selected is output through a multi-layer network structure.

[0050] It should be noted that a large amount of historical distribution path data and historical path planning data are collected during the training of the model. For example, the geographical location of the node, traffic conditions, time windows, customer demand, capacity and speed limits of the distribution vehicles, etc. can be obtained through multiple channels such as the operation records of the logistics company, GPS tracking system, and traffic database. After obtaining the data, the data is preprocessed and the preprocessed data is divided into a training set and a validation set. The model is trained using the training set data. During the training process, the output of the model is calculated through forward propagation, and the gradient is calculated and the model parameters are updated through back propagation until the performance of the model on the validation set reaches the preset standard or reaches the predetermined training round. After the training is completed, the model needs to be evaluated to verify its performance, such as calculating the performance indicators of the model, such as accuracy, recall rate, F1 score, etc. to evaluate the quality of the model.

[0051] S140: Determine a target delivery path from the at least two delivery paths to be selected according to the path evaluation scores.

[0052] Among them, the target delivery path can be understood as the delivery path corresponding to the target delivery task.

[0053] Specifically, a target delivery path is determined from at least two delivery paths to be selected according to the path evaluation scores. For example, the path evaluation scores of all the delivery paths to be selected are compared, the paths are sorted according to the scores, and the target delivery path is determined from the delivery paths to be selected according to the sorting result. The higher the path evaluation score, the better the path to be selected corresponding to the current path evaluation score. Therefore, the delivery paths to be selected can be sorted according to the path evaluation scores, and the delivery path to be selected with the highest path evaluation score can be used as the target delivery path according to the sorting result.

[0054] On the basis of the above technical solution, after determining the target delivery path from the at least two delivery paths to be selected according to the path evaluation score, it also includes: when feedback information corresponding to the target path is obtained, re-determining the delivery path corresponding to the target delivery task according to the feedback information.

[0055] Among them, the feedback information can be understood as the real-time feedback sent by the delivery personnel corresponding to the target delivery route.

[0056] Specifically, after determining the target delivery path, the target delivery path can be pushed to the delivery personnel who are performing the target delivery task, and it can be detected whether the target delivery personnel sends feedback information. If feedback information is detected, the impact on the delivery path is evaluated by identifying the key issues and influencing factors in the feedback, and the consequences that may result if no adjustments are made are predicted. Based on the analysis results of the feedback information, if the analysis results of the feedback information indicate that it is necessary to recalculate the path evaluation score or generate a new delivery path to be selected, the delivery path determination model can be called for further processing. For example, new data (such as updated traffic conditions, customer needs, etc.) can be input into the model, and the model can be re-run to generate a new path evaluation score or a delivery path to be selected.

[0057] On the basis of the above technical solution, after determining the target delivery path from the at least two delivery paths to be selected according to the path evaluation score, it also includes: updating the path planning data based on a preset data update cycle, and when a change is detected in the path planning data, determining the delivery path corresponding to the target delivery task according to the updated path planning data.

[0058] The preset data update cycle may be a pre-set data update time, for example, the path planning data associated with the target delivery task may be updated every 30 minutes.

[0059] Specifically, the path planning data is updated regularly according to a preset data update cycle. When a change in the path planning data is detected, for example, when vehicle congestion is detected, the delivery path corresponding to the target delivery task is re-determined based on the updated path planning data. When a change in the path planning data is detected, the delivery path determination model or algorithm is called, and the updated data is used as input to recalculate the path evaluation score and determine the delivery path corresponding to the target delivery task.

[0060] The technical solution of the embodiment of the present invention obtains the path planning data corresponding to the target delivery task, determines the initial delivery path according to the path planning data, processes the initial delivery path based on the path optimization algorithm, determines at least two delivery paths to be selected, inputs the delivery paths to be selected and the path planning data into the delivery path determination model, obtains the path evaluation score corresponding to the delivery paths to be selected, and determines the target delivery path from the at least two delivery paths to be selected according to the path evaluation score. Based on the above technical solution, the initial delivery path is determined according to the path planning data, and then the initial delivery path is further optimized to determine the target path through the path optimization algorithm and the delivery path determination model, thereby improving the efficiency of determining the logistics delivery path.

[0061] Embodiment 2

[0062] Figure 2 A schematic diagram of a device for determining a logistics distribution path provided by an embodiment of the present invention. Figure 2 As shown, the device includes: an initial path determination module 210, a path optimization module 220, a path evaluation module 230 and a target path determination module 240.

[0063] An initial path determination module is used to obtain path planning data corresponding to the target delivery task and determine an initial delivery path according to the path planning data;

[0064] A path optimization module, used for processing the initial delivery path based on a path optimization algorithm to determine at least two delivery paths to be selected, wherein the path optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm;

[0065] a path evaluation module, used for inputting the delivery path to be selected and the path planning data into a delivery path determination model to obtain a path evaluation score corresponding to the delivery path to be selected, wherein the delivery path determination model is a neural network model trained based on historical delivery paths and historical path planning data;

[0066] The target path determination module is used to determine a target delivery path from the at least two delivery paths to be selected according to the path evaluation score.

[0067] On the basis of the above technical solution, the initial path determination module is used to determine the delivery location corresponding to the target delivery task, wherein the delivery location includes a delivery starting point and a delivery ending point; based on the delivery location, a target task area corresponding to the target delivery task is determined, and path planning data corresponding to the target task area is obtained, wherein the path planning data includes traffic condition data, weather data and obstacle data.

[0068] On the basis of the above technical solution, the initial path determination module is used to obtain the time constraints and distance constraints corresponding to the target delivery task; determine each path node to be verified corresponding to the target delivery task according to the time constraints, the distance constraints and the path planning data, and determine the initial delivery path based on the each path node to be verified.

[0069] On the basis of the above technical solution, the initial path determination module is used to determine the node evaluation data corresponding to each of the path nodes to be verified based on the node cost evaluation function; and determine the initial delivery path according to the node evaluation data and each of the path nodes to be verified.

[0070] On the basis of the above technical solution, the device also includes: a feedback optimization module, which is used to, after determining the target delivery path from the at least two delivery paths to be selected according to the path evaluation score, when feedback information corresponding to the target path is obtained, re-determine the delivery path corresponding to the target delivery task according to the feedback information.

[0071] On the basis of the above technical solution, the device also includes: a feedback optimization module, which is used to update the path planning data based on a preset data update cycle after determining the target delivery path from the at least two delivery paths to be selected according to the path evaluation score, and when a change in the path planning data is detected, determine the delivery path corresponding to the target delivery task according to the updated path planning data.

[0072] On the basis of the above technical solution, the initial path determination module is used to determine the data format corresponding to the path planning data, and when the data format is inconsistent with the preset data format, convert the data format of the path planning data into the preset data format.

[0073] The technical solution of the embodiment of the present invention obtains the path planning data corresponding to the target delivery task, determines the initial delivery path according to the path planning data, processes the initial delivery path based on the path optimization algorithm, determines at least two delivery paths to be selected, inputs the delivery paths to be selected and the path planning data into the delivery path determination model, obtains the path evaluation score corresponding to the delivery paths to be selected, and determines the target delivery path from the at least two delivery paths to be selected according to the path evaluation score. Based on the above technical solution, the initial delivery path is determined according to the path planning data, and then the initial delivery path is further optimized to determine the target path through the path optimization algorithm and the delivery path determination model, thereby improving the efficiency of determining the logistics delivery path.

[0074] The device for determining a logistics distribution path provided in the embodiment of the present invention can execute the method for determining a logistics distribution path provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0075] Embodiment 3

[0076] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0077] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0078] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0079] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a logistics distribution path.

[0080] In some embodiments, the method for determining the logistics distribution path may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the logistics distribution path described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the logistics distribution path in any other appropriate manner (e.g., by means of firmware).

[0081] 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.

[0082] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program 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.

[0083] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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.

[0084] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0085] The systems and techniques described herein may be implemented in a computing system that includes backend 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 frontend 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 backend components, middleware components, or frontend 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), a blockchain network, and the Internet.

[0086] A computing 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 client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0087] 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 described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0088] The above specific implementations do not constitute a limitation on the protection scope of the present invention. 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 invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a logistics distribution path, characterized in that: include: Acquire path planning data corresponding to the target delivery task, and determine an initial delivery path according to the path planning data; Processing the initial delivery path based on a path optimization algorithm to determine at least two delivery paths to be selected, wherein the path optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm; Inputting the delivery path to be selected and the path planning data into a delivery path determination model to obtain a path evaluation score corresponding to the delivery path to be selected, wherein the delivery path determination model is a neural network model trained based on historical delivery paths and historical path planning data; A target delivery path is determined from the at least two delivery paths to be selected according to the path evaluation score.

2. The method according to claim 1, characterized in that The step of obtaining the path planning data corresponding to the target delivery task includes: Determine a delivery location corresponding to the target delivery task, wherein the delivery location includes a delivery start point and a delivery end point; A target task area corresponding to the target delivery task is determined based on the delivery location, and path planning data corresponding to the target task area is acquired, wherein the path planning data includes traffic condition data, weather data, and obstacle data.

3. The method according to claim 1, characterized in that The determining of the initial delivery path according to the path planning data comprises: Obtaining time constraints and distance constraints corresponding to the target delivery task; The path nodes to be verified corresponding to the target delivery task are determined according to the time constraint, the distance constraint and the path planning data, and the initial delivery path is determined based on the path nodes to be verified.

4. The method according to claim 3, characterized in that: The determining the initial delivery path based on the path nodes to be verified includes: Determining node evaluation data corresponding to each of the path nodes to be verified based on a node cost evaluation function; The initial delivery path is determined according to the node evaluation data and the path nodes to be verified.

5. The method according to claim 1, characterized in that After determining the target delivery path from the at least two delivery paths to be selected according to the path evaluation score, the method further includes: When feedback information corresponding to the target path is obtained, the delivery path corresponding to the target delivery task is re-determined according to the feedback information.

6. The method according to claim 1, characterized in that After determining the target delivery path from the at least two delivery paths to be selected according to the path evaluation score, the method further includes: The path planning data is updated based on a preset data update cycle, and when a change in the path planning data is detected, a delivery path corresponding to the target delivery task is determined according to the updated path planning data.

7. The method according to claim 2, characterized in that After acquiring the path planning data corresponding to the target task area, the method further includes: A data format corresponding to the path planning data is determined, and when the data format is inconsistent with a preset data format, the data format of the path planning data is converted into the preset data format.

8. A device for determining a logistics distribution path, characterized in that: include: An initial path determination module is used to obtain path planning data corresponding to the target delivery task and determine an initial delivery path according to the path planning data; A path optimization module, used for processing the initial delivery path based on a path optimization algorithm to determine at least two delivery paths to be selected, wherein the path optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm; a path evaluation module, used for inputting the delivery path to be selected and the path planning data into a delivery path determination model to obtain a path evaluation score corresponding to the delivery path to be selected, wherein the delivery path determination model is a neural network model trained based on historical delivery paths and historical path planning data; The target path determination module is used to determine a target delivery path from the at least two delivery paths to be selected according to the path evaluation score.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining a logistics distribution path as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a logistics distribution path according to any one of claims 1 to 7 when executed.

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