Order delivery scheme determination method and device, computer equipment and medium

Abstract

The invention provides an order delivery scheme determination method and device, computer equipment and a medium, and belongs to the field of data processing. The method comprises the steps that cargo information and delivery route information of each freight order in a plurality of freight orders to be processed currently are acquired, and the delivery route information indicates at least one delivery route associated with the corresponding freight order and current delivery time of each delivery route in the at least one delivery route; obtaining load information of each vehicle in the at least one vehicle; at least one first delivery scheme is generated according to the load information of each vehicle and the cargo information and delivery route information of each freight order, and each first delivery scheme in the at least one first delivery scheme indicates the number of freight orders distributed to each vehicle and the delivery route sequence; and based on the distribution time of each first distribution scheme in the at least one first distribution scheme, determining a target distribution scheme.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a method, device, computer equipment and medium for determining a user order delivery plan. Background Art

[0002] In the process of logistics transportation, it is usually necessary to assign multiple orders to multiple vehicles for delivery. Currently, the weight and volume of the goods in each order can be used to match the corresponding model and load of the vehicle to perform order allocation. However, this method does not take into account the delivery route planning of the vehicle when delivering multiple orders, resulting in longer delivery time, reduced delivery efficiency, and increased delivery costs. Summary of the invention

[0003] It would be advantageous to provide a mechanism that alleviates, mitigates, or even eliminates one or more of the above-mentioned problems.

[0004] According to one aspect of the present disclosure, a method for determining an order delivery plan is provided, comprising: obtaining cargo information and delivery route information of each freight order among a plurality of freight orders currently to be processed, wherein the delivery route information indicates at least one delivery route associated with the corresponding freight order and a current delivery time of each of the at least one delivery route; obtaining load information of each of at least one vehicle; generating at least one first delivery plan based on the load information of each vehicle and the cargo information and delivery route information of each freight order, wherein each of the at least one first delivery plan indicates the number of freight orders allocated to each vehicle and a delivery route sequence; and determining a target delivery plan based on the delivery time of each of the at least one first delivery plan.

[0005] According to one aspect of the present disclosure, a device for determining an order delivery plan is provided, comprising: a first acquisition module, configured to acquire cargo information and delivery route information of each freight order among a plurality of freight orders currently to be processed, wherein the delivery route information indicates at least one delivery route associated with the corresponding freight order and a current delivery time of each of the at least one delivery route; a second acquisition module, configured to acquire load information of each of at least one vehicle; a generation module, configured to generate at least one first delivery plan based on the load information of each vehicle and the cargo information and delivery route information of each freight order, wherein each of the at least one first delivery plan indicates the number of freight orders allocated to each vehicle and the order of delivery routes; and a determination module, configured to determine a target delivery plan based on the delivery time of each of the at least one first delivery plan.

[0006] According to one aspect of the present disclosure, a computer device is provided, comprising: at least one processor; and at least one memory having instructions stored thereon, wherein when the instructions are executed by the at least one processor, the at least one processor executes any one of the above methods.

[0007] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which instructions are stored. When the instructions are executed by one or more processors, the one or more processors are caused to execute any one of the above methods.

[0008] According to one aspect of the present disclosure, a computer program product is provided, comprising instructions, and when the instructions are executed by one or more processors, the one or more processors are caused to perform any of the above methods.

[0009] According to the embodiments of the present disclosure, when allocating freight orders to vehicles, not only is it considered whether the goods themselves match the model and load capacity of the vehicle, but also the possible delivery routes for each order and the delivery time for each delivery route are further introduced. This enables the provision of delivery routes with higher delivery efficiency while allocating freight orders that meet cargo restrictions to vehicles, thereby effectively reducing delivery costs and improving the user experience of drivers and users.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0012] Figure 1 is a schematic diagram illustrating an example system in which the various methods described herein may be implemented according to an exemplary embodiment;

[0013] Figure 2 is a flow chart illustrating a method of determining an order allocation scheme according to an exemplary embodiment;

[0014] Figure 3 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment;

[0015] Figure 4 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment;

[0016] Figure 5is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment;

[0017] Figure 6 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment;

[0018] Figure 7 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment;

[0019] Figure 8 is a schematic block diagram illustrating a device for determining an order allocation scheme according to an exemplary embodiment; and

[0020] Fig. 9 is a schematic block diagram illustrating an exemplary computer device that can be applied to the exemplary embodiments. DETAILED DESCRIPTION

[0021] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0022] The terms used in the description of various examples described in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. As used herein, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". In addition, the terms "and / or" and "at least one of..." cover any one of the listed items and all possible combinations.

[0023] In the process of logistics transportation, it is usually necessary to assign multiple orders to multiple vehicles for delivery. In the related art, the weight and volume of the goods of each order can be used to match the corresponding model and load of the vehicle to perform order allocation. However, this method does not take into account the delivery route planning of the vehicle when delivering multiple orders, resulting in a longer delivery time, reduced delivery efficiency, and increased delivery costs.

[0024] In view of the above situation, the present disclosure proposes a method and apparatus for determining an order allocation plan, as well as a computer device, a computer-readable storage medium, and a computer program product.

[0025] According to the method disclosed in the present invention, when allocating freight orders to vehicles, not only is it considered whether the goods themselves match the model and load capacity of the vehicle, but the possible delivery routes for each order and the delivery time for each delivery route are further introduced. This allows for the allocation of freight orders that meet the cargo restrictions to vehicles while specifically planning delivery routes with higher delivery efficiency, thereby effectively reducing delivery costs and improving the user experience of drivers and users.

[0026] Exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0027] Figure 1 is a schematic diagram illustrating an example system 100 in which the various methods described herein may be implemented, according to an example embodiment.

[0028] refer to Figure 1 The system 100 includes a client device 110 , a server 120 , and a network 130 that communicatively couples the client device 110 and the server 120 .

[0029] The client device 110 includes a display 114 and a client application (APP) 112 that can be displayed via the display 114. The client application 112 can be an application that needs to be downloaded and installed before running or a small program (liteapp) as a lightweight application. In the case where the client application 112 is an application that needs to be downloaded and installed before running, the client application 112 can be pre-installed on the client device 110 and activated. In the case where the client application 112 is a small program, the user 102 can directly run the client application 112 on the client device 110 by searching for the client application 112 in the host application (for example, by the name of the client application 112, etc.) or scanning the graphic code of the client application 112 (for example, a bar code, a QR code, etc.), without installing the client application 112. In some embodiments, the client device 110 can be any type of mobile computer device, including a mobile computer, a mobile phone, a wearable computer device (for example, a smart watch, a head-mounted device, including smart glasses, etc.) or other types of mobile devices. In some embodiments, client device 110 may alternatively be a stationary computer device, such as a desktop computer, a server computer, or other type of stationary computer device.

[0030] The server 120 is typically a server deployed by an Internet Service Provider (ISP) or an Internet Content Provider (ICP). The server 120 may represent a single server, a cluster of multiple servers, a distributed system, or a cloud server that provides basic cloud services (such as cloud databases, cloud computing, cloud storage, and cloud communications). It will be understood that although Figure 1The server 120 is shown in FIG. 1 in communicating with only one client device 110 , but the server 120 may provide background services for multiple client devices simultaneously.

[0031] Examples of network 130 include a local area network (LAN), a wide area network (WAN), a personal area network (PAN), and / or a combination of communication networks such as the Internet. Network 130 can be a wired or wireless network. In some embodiments, the data exchanged through network 130 is processed using technologies and / or formats including hypertext markup language (HTML), extensible markup language (XML), etc. In addition, encryption technologies such as secure socket layer (SSL), transport layer security (TLS), virtual private network (VPN), Internet protocol security (IPsec) can also be used to encrypt all or some links. In some embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0032] For the purpose of the embodiments of this disclosure, Figure 1 In the example of , the client application 112 may be an application for providing communication, decision-making, etc. for all parties involved in logistics transportation and distribution. Correspondingly, the server 120 may be a server used together with such an application.

[0033] Figure 2 is a flow chart illustrating a method 200 of determining an order allocation plan according to an exemplary embodiment.

[0034] Please refer to Figure 2 , the order allocation scheme determination method 200 includes:

[0035] Step 210: Obtain cargo information and delivery route information of each of the multiple freight orders currently to be processed, wherein the delivery route information indicates at least one delivery route associated with the corresponding freight order and a current delivery time of each of the at least one delivery route;

[0036] Step 220: Obtaining load information of each of at least one vehicle;

[0037] Step 230: Generate at least one first delivery plan based on the load information of each vehicle, and the cargo information and delivery route information of each freight order, wherein each of the at least one first delivery plan indicates the number of freight orders allocated to each vehicle and the order of delivery routes; and

[0038] Step 240: Determine a target delivery plan based on the delivery time of each first delivery plan in at least one first delivery plan.

[0039] Based on this, when allocating freight orders to vehicles, not only is it considered whether the goods themselves match the model and load capacity of the vehicle, but the possible delivery routes for each order and the delivery time of each delivery route are further introduced. This allows us to allocate freight orders that meet cargo restrictions to vehicles while specifically planning delivery routes with higher delivery efficiency, effectively reducing delivery costs and improving the user experience of drivers and users.

[0040] In step 210, the cargo information may be, for example, the total weight and total volume of the cargo of the corresponding freight order, so as to determine whether any vehicle can carry the corresponding freight order based on the cargo information. For example, each freight order may include one or more types of cargo, and the cargo information may specifically include the type, model, and quantity of each cargo, so that the total weight and total volume of the cargo can be estimated.

[0041] In step 210, each freight order has a corresponding delivery starting point and delivery destination. By calling a map API (Application Programming Interface), at least one delivery route can be determined for the freight order based on the delivery starting point and delivery destination of the freight order, and the current delivery time of each delivery route can be determined by the distance and current traffic conditions of the delivery route to generate the delivery route information of the freight order. For example, the current traffic conditions can be, for example, whether the delivery route is currently open to traffic, whether there is traffic congestion on the delivery route, and the estimated time it takes for a vehicle to pass through a traffic congestion section, etc.

[0042] In step 220, the load information may be, for example, the vehicle weight and vehicle volume of the corresponding vehicle, so as to determine whether the corresponding vehicle can carry the corresponding freight order based on the load information. In one example, the corresponding vehicle is empty, and the load information may be the empty vehicle weight and empty vehicle volume; in another example, the corresponding vehicle has already carried part of the goods, and the load information may be the remaining vehicle weight and remaining vehicle volume.

[0043] In step 220, at least one vehicle may be, for example, a self-operated vehicle of the current logistics platform and a vehicle operated by a third-party platform. For example, the self-operated vehicles of the current logistics platform may be preferentially allocated to reduce the difficulty of allocation, thereby better managing vehicles.

[0044] According to some embodiments, step 230 includes:

[0045] Step 231: Taking the load information of each vehicle and the cargo information of each freight order as constraint conditions, and according to the delivery route information of each freight order, multiple freight orders and at least one vehicle are arranged and combined to generate at least one first delivery plan.

[0046] This ensures that each freight order will be transported smoothly in each delivery plan, avoiding the situation where the vehicle cannot be transported due to insufficient cargo space and weight after arriving at the designated delivery starting point, thereby improving the effectiveness of the first delivery plan generated.

[0047] In step 231, each delivery route associated with each freight order is a directed line segment, so each of the above delivery routes can be used as a basic unit of permutation and combination. Specifically, when generating each first delivery plan, a related delivery route is selected for each freight order, and then the combination formed by the delivery routes associated with multiple freight orders is arranged to generate multiple first delivery plans in accordance with the constraint conditions.

[0048] In step 240 , the target delivery plan may be, for example, the first delivery plan with the shortest delivery time, so as to maximize the delivery efficiency.

[0049] If there are multiple target delivery plans with the shortest delivery time, for example, the target delivery plan with the lowest cost can be further determined based on factors such as the number of vehicles deployed, the price of the vehicles deployed, the overall connectivity of the delivery routes of each vehicle, and the utilization rate of the vehicle space and weight of each vehicle.

[0050] Figure 3 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment.

[0051] According to some embodiments, Figure 3 As shown, after step 220, method 200 further includes:

[0052] Step 310: for each freight order, according to the load information of each vehicle and the cargo information corresponding to the freight order, select one or more vehicles satisfying a first condition from at least one vehicle to establish a first association relationship with the freight order, wherein the first condition indicates that the cargo of the freight order satisfies the load limit of the corresponding vehicle; and

[0053] Step 320: Generate at least one first delivery plan based on the first association relationship of each freight order, the load information of each vehicle, and the cargo information and delivery route information of each freight order.

[0054] By first matching multiple freight orders with at least one vehicle in terms of cargo, vehicle space and vehicle weight on a one-to-one basis, it is possible to exclude vehicles that are unable to carry the freight order alone for each freight order, effectively reducing the number of first distribution plans generated, simplifying subsequent calculations and improving processing efficiency.

[0055] Figure 4is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment.

[0056] According to some embodiments, the cargo information includes cargo weight and cargo volume, such as Figure 4 As shown, after step 220, method 200 further includes:

[0057] Step 410: Determine the cargo density of each freight order according to the cargo weight and cargo volume of the freight order;

[0058] Step 420: sort the multiple freight orders according to the cargo density to obtain a loading and unloading sequence, wherein the loading and unloading sequence is used to indicate the loading and unloading order of the multiple freight orders; and

[0059] Step 430: Generate at least one first delivery plan based on the loading and unloading sequence, the load information of each vehicle, and the cargo information and delivery route information of each freight order.

[0060] Based on this, based on the nature of the goods in each freight order, it can be determined that when the same vehicle delivers multiple orders, it will give priority to receiving heavy goods with higher density (more resistant to pressure, and placed at the bottom of the loading space), and then receive bulky goods with lower density (easily crushed, and placed at the top of the loading space) to reduce the damage rate of goods during delivery.

[0061] In addition, by introducing the loading and unloading sequence as one of the constraints for generating the first delivery plan, the number of first delivery plans that need to be generated can be effectively reduced, thereby greatly reducing the processing difficulty.

[0062] In step 420, for a freight order including multiple types of goods, when determining the order of loading and unloading the freight order, the density of goods with the largest weight or quantity in the freight order may be used as the cargo density of the freight order for sorting; or, the weight of each type of goods may be determined according to its weight or quantity proportion, and the cargo density of the freight order may be determined by weighted calculation for sorting; or, in order to further reduce the difficulty of processing, the average value of the cargo density of multiple types of goods may be directly calculated as the cargo density of the freight order for sorting.

[0063] In step 430, illustratively, based on the method in the above step 231, with the load information of each vehicle and the cargo information of each freight order as constraints, and according to the loading and unloading sequence and the delivery route information of each freight order, multiple freight orders and at least one vehicle are arranged and combined to generate at least one first delivery plan.

[0064] It should be noted that, in step 430, the load information of each vehicle and the cargo information of each freight order are used as constraints, which not only indicates that the vehicle's load space and load limit must match the total volume and total weight of the cargo, but also indicates that the loading and unloading sequence, the vehicle's load space and load limit, and the cargo volume and weight of each freight order need to be comprehensively considered to generate a first delivery plan.

[0065] The above contents are explained in detail here through specific examples.

[0066] In one example, order No. 1 (a bulk cargo) and order No. 2 (a heavy cargo) are assigned to vehicle No. 1. In response to the fact that the cargo of the two orders cannot be placed at the bottom of the cargo space of vehicle No. 1 at the same time (or, the cargo of the two orders must be stacked), when generating the first delivery plan, the delivery route of vehicle No. 1 is planned based on the order of loading and unloading, that is, first pick up the cargo from the delivery starting point of order No. 1, and then pick up the cargo from the delivery starting point of order No. 2, so as to reduce the possibility of cargo damage in the generated first delivery plan.

[0067] In another example, also referring to the above example, in response to the fact that the bottom of the cargo space of vehicle No. 1 can simultaneously hold goods of two orders, a first first delivery plan for picking up the goods at the delivery starting point of order No. 1 first and a second first delivery plan for picking up the goods at the delivery starting point of order No. 2 first can be generated simultaneously, so that the final target delivery plan can be determined based on the delivery time later. This can reduce the possibility of damage to the goods in the generated first delivery plan while increasing the selectivity of the first delivery plan, so as to increase the probability of ultimately selecting the optimal target delivery plan.

[0068] It should be understood that the above examples are for illustrative purposes only, and in the specific implementation process, the number of freight orders corresponding to vehicle No. 1 may also be greater than 2, and there is no limitation on this.

[0069] Figure 5 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment.

[0070] According to some embodiments, Figure 5 As shown, after step 220, method 200 further includes:

[0071] Step 510: for each vehicle,

[0072] Step 511, obtaining the current location information of the vehicle;

[0073] Step 512: Determine the vehicle delivery range according to the current location information of the vehicle; and

[0074] Step 513: Select one or more freight orders satisfying a second condition from at least one freight order to establish a second association relationship with the vehicle, wherein the second condition indicates that the corresponding freight order is associated with at least one delivery route that partially or completely falls within the delivery range of the vehicle; and

[0075] Step 520: Generate at least one first delivery plan based on the second association relationship of each vehicle, the load information of each vehicle, and the cargo information and delivery route information of each freight order.

[0076] By first performing one-to-one location matching between multiple freight orders and at least one vehicle, it is possible to exclude vehicles with freight orders that are too far away for each vehicle, effectively reducing the number of first delivery plans generated, simplifying subsequent calculations, and improving processing efficiency.

[0077] In step 512, illustratively, the current position of the vehicle may be used as the center of the circle and the first distance may be radiated outward to obtain a circular geographical area as the vehicle delivery range.

[0078] In step 512, illustratively, one or more shipping orders satisfying the second condition may be determined based on a clustering algorithm such as K-means or DBSCAN.

[0079] In step 513, in order to avoid the situation where one of the starting point or the end point of the freight order is closer to the vehicle and the other is farther from the vehicle, the specific ratio of the above-mentioned "partial or all" can be set based on the length of the delivery route. For example, for a delivery route with a shorter distance, the specific ratio of "partial or all" can be set to be relatively low (e.g., when more than 20% of the delivery route falls within the vehicle delivery range, the second association relationship is established); and for a delivery route with a longer distance, the specific ratio of "partial or all" can be set to be relatively high (e.g., when more than 70% of the delivery route falls within the vehicle delivery range, the second association relationship is established).

[0080] Figure 6 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment.

[0081] According to some embodiments, Figure 6 As shown, after step 220, method 200 further includes:

[0082] Step 610: Obtain a vehicle score for each vehicle, wherein the vehicle score is used to indicate the age and condition of the corresponding vehicle; and

[0083] Step 620: Determine at least one first delivery plan based on the vehicle score and load information of each vehicle, and the cargo information and delivery route information of each freight order.

[0084] Vehicles can be screened by their age and condition, with priority given to vehicles with better age and condition. This can effectively reduce the possibility of subsequent breakdowns due to poor vehicle condition affecting overall order delivery.

[0085] In step 610, the vehicle condition includes, but is not limited to, the newness of the vehicle, whether the vehicle has been inspected on time, and whether the vehicle has an accident record.

[0086] Figure 7 is a partial flow chart illustrating a method for determining another order allocation scheme according to an exemplary embodiment.

[0087] According to some embodiments, Figure 7 As shown, after step 220, method 200 further includes:

[0088] Step 710: Obtain the driver score of each vehicle, wherein the vehicle score is used to indicate the driving experience and user satisfaction of the driver of the corresponding vehicle; and

[0089] Step 720: Determine at least one first delivery plan based on the driver score and load information of each vehicle, and the cargo information and delivery route information of each freight order.

[0090] Vehicles can be screened based on their driver experience and user satisfaction, with priority given to vehicles with experienced drivers and high user satisfaction. This can effectively reduce the possibility of subsequent traffic accidents or driver personal reasons affecting the overall order delivery.

[0091] According to some embodiments, in response to an update of at least one of the weight information of each vehicle, the cargo information of each freight order, and the delivery route information of each freight order, at least one first delivery plan is updated based on the updated weight information of each vehicle, the cargo information of each freight order, and the delivery route information of each freight order.

[0092] Exemplarily, the number of freight orders allocated to each of at least one vehicle may be equal to zero or greater than zero, there may be unallocated freight orders among multiple freight orders, and there may be newly added vehicles and freight orders at different times. Therefore, by tracking the current vehicle and order conditions in real time, dynamic adjustment of the order allocation plan can be achieved, thereby further improving the integrity and effectiveness of the target allocation plan.

[0093] In one or more embodiments, the above-mentioned multiple embodiments may be combined to generate a first delivery plan based on multi-dimensional factors at the same time, thereby effectively reducing the number of first delivery plans finally generated and reducing the difficulty of calculation.

[0094] Exemplarily, the load information of each vehicle and the cargo information of each freight order can be used as constraints, and based on the delivery route information of each freight order, the first association relationship of each freight order, the loading and unloading sequence, the second association relationship of each vehicle, the vehicle score of each vehicle, and the driver score of each vehicle, multiple freight orders and at least one vehicle can be arranged and combined to generate at least one first delivery plan.

[0095] For example, different weights may be assigned to the delivery route information of each freight order, the first association relationship of each freight order, the loading and unloading sequence, the second association relationship of each vehicle, the vehicle score of each vehicle, and the driver score of each vehicle, so that the generated first delivery plan is better adapted to the current application scenario.

[0096] In one example, when the current supply of vehicles is greater than the demand for order delivery, higher weights can be assigned to loading and unloading order, vehicle score, and driver score factors to screen out better vehicles to provide better order delivery services.

[0097] In another example, when the current supply of vehicles is less than the demand for order delivery, a higher weight can be assigned to the second association relationship of each vehicle to prioritize the delivery of nearby orders for vehicle planning, thereby improving overall delivery efficiency.

[0098] Although the various operations are depicted in the drawings as being in a particular order, this should not be understood as requiring that these operations must be performed in the particular order shown or in a sequential order, nor should it be understood as requiring that all the operations shown must be performed to obtain the desired results. For example, step 410 can be performed before step 310, or concurrently with step 310.

[0099] This embodiment of the application provides a device 800 for determining an order allocation plan. Figure 8 The order allocation scheme determination device 800 includes a first acquisition module 810, a second acquisition module 820, a generation module 830 and a determination module 840.

[0100] The first acquisition module 810 is configured to acquire cargo information and delivery route information of each of the multiple freight orders currently to be processed, wherein the delivery route information indicates at least one delivery route associated with the corresponding freight order and a current delivery time of each of the at least one delivery route.

[0101] The second acquisition module 820 is configured to acquire the load information of each vehicle of at least one vehicle.

[0102] The generation module 830 is configured to generate at least one first delivery plan based on the load information of each vehicle, and the cargo information and delivery route information of each freight order, wherein each of the at least one first delivery plan indicates the quantity and delivery route sequence of the freight orders assigned to each vehicle.

[0103] The determination module 840 is configured to determine a target delivery plan based on the delivery time of each delivery plan in the at least one first delivery plan.

[0104] The first acquisition module 810, the second acquisition module 820, the generation module 830 and the determination module 840 in the order allocation scheme determination device 800 may correspond to the following respectively: Figure 1 For the sake of brevity, steps 210 to 240 in the method 200 for determining an order allocation scheme are not described here in detail. It should be understood that, corresponding to the embodiment of the method 200 for determining an order allocation scheme, the embodiment of the device 800 for determining an order allocation scheme may also include more modules.

[0105] It should be noted that the functions of the various modules discussed herein can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module. The specific module execution action discussed herein includes that the specific module itself performs the action, or alternatively the specific module calls or otherwise accesses another component or module that performs the action (or performs the action together with the specific module). Therefore, the specific module that performs the action can include the specific module itself that performs the action and / or the specific module calls or otherwise accesses another module that performs the action. For example, the first acquisition module 810 and the second acquisition module 820 can be combined into a single module in some embodiments.

[0106] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 8The modules described may be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules may be implemented as computer program codes / instructions configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules may be implemented as hardware logic / circuits. For example, in some embodiments, one or more of the first acquisition module 810, the second acquisition module 820, the generation module 830, and the determination module 840 may be implemented together in a system on chip (SoC). SoC may include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or one or more components in other circuits), and may optionally execute the received program code and / or include embedded firmware to perform functions.

[0107] According to another aspect of the present disclosure, a computer device is provided, which includes a memory, a processor, and instructions stored in the memory. When the instructions are executed by the processor, the processor executes the instructions to implement the steps of any method embodiment described above.

[0108] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, the steps of any method embodiment described above are implemented.

[0109] According to another aspect of the present disclosure, a computer program product is provided, which includes instructions, and when the instructions are executed by a processor, the steps of any method embodiment described above are implemented.

[0110] In the following, combined Fig. 9 Illustrative examples of such a computer device, non-transitory computer-readable storage medium, and computer program product are described.

[0111] Fig. 9 An example configuration of a computer device 900 is shown that may be used to implement the methods described herein.

[0112] Computer device 900 can be a variety of different types of devices. Examples of computer device 900 include, but are not limited to, desktop computers, server computers, laptop or netbook computers, mobile devices (e.g., tablet computers, cellular or other wireless phones (e.g., smart phones), notepad computers, mobile stations), wearable devices (e.g., glasses, watches), entertainment devices (e.g., entertainment appliances, set-top boxes communicatively coupled to display devices, game consoles), televisions or other display devices, automotive computers, and the like.

[0113] The computer device 900 may include at least one processor 902, memory 904, communication interface(s) 906, a display device 908, other input / output (I / O) devices 910, and one or more mass storage devices 912 that can communicate with each other, such as via a system bus 914 or other appropriate connection.

[0114] The processor 902 may be a single processing unit or multiple processing units, all of which may include a single or multiple computing units or multiple cores. The processor 902 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, the processor 902 may be configured to obtain and execute computer-readable instructions stored in the memory 904, mass storage device 912, or other computer-readable media, such as program codes of an operating system 916, program codes of an application program 918, program codes of other programs 920, and the like.

[0115] The memory 904 and the mass storage device 912 are examples of computer-readable storage media for storing instructions, which are executed by the processor 902 to implement the various functions described above. For example, the memory 904 can generally include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, the mass storage device 912 can generally include a hard disk drive, a solid-state drive, a removable medium, including external and removable drives, memory cards, flash memory, a floppy disk, an optical disk (e.g., CD, DVD), a storage array, a network attached storage, a storage area network, etc. The memory 904 and the mass storage device 912 can all be collectively referred to as memory or computer-readable storage media in this article, and can be a non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code, which can be executed by the processor 902 as a specific machine configured to implement the operations and functions described in the examples of this article. Multiple programs can be stored on the mass storage device 912. These programs include an operating system 916 , one or more application programs 918 , other programs 920 , and program data 922 , and they may be loaded into memory 904 for execution.

[0116] Although in Fig. 9 904 of the computer device 900, but modules 916, 918, 920, and 922, or portions thereof, may be implemented using any form of computer-readable media accessible by the computer device 900. As used herein, "computer-readable media" includes at least two types of computer-readable media, namely, computer-readable storage media and communication media.

[0117] Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information, such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device, or any other non-transmission medium that can be used to store information for access by a computer device. In contrast, communication media can embody computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism. Computer-readable storage media as defined herein do not include communication media.

[0118] One or more communication interfaces 906 are used to exchange data with other devices, such as through a network, direct connection, etc. Such communication interfaces can be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), a wired or wireless (such as IEEE 802.11 wireless LAN (WLAN)) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth™ interface, a Near Field Communication (NFC) interface, etc. The communication interface 906 can facilitate communication within a variety of network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. The communication interface 906 can also provide communication with external storage devices (not shown) such as storage arrays, network attached storage, storage area networks, etc.

[0119] In some examples, a display device 908 such as a monitor may be included for displaying information and images to the user. Other I / O devices 910 may be devices that receive various inputs from the user and provide various outputs to the user, and may include a touch input device, a gesture input device, a camera, a keyboard, a remote control, a mouse, a printer, an audio input / output device, and the like.

[0120] The technology described herein can be supported by these various configurations of the computer device 900, and is not limited to the specific examples of the technology described herein. For example, the function can also be implemented in whole or in part on the "cloud" by using a distributed system. The cloud includes and / or represents a platform for resources. The platform abstracts the underlying functions of the hardware (e.g., server) and software resources of the cloud. Resources can include applications and / or data that can be used when performing computing processing on a server away from the computer device 900. Resources can also include services provided through the Internet and / or through a subscriber network such as a cellular or Wi-Fi network. The platform can abstract resources and functions to connect the computer device 900 to other computer devices. Therefore, the implementation of the functions described herein can be distributed throughout the cloud. For example, the functions can be implemented partially on the computer device 900 and partially through a platform that abstracts the functions of the cloud.

[0121] Although the present disclosure has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description should be considered illustrative and schematic, not restrictive; the present disclosure is not limited to the disclosed embodiments. By studying the drawings, the disclosure and the appended claims, those skilled in the art will be able to understand and implement variations to the disclosed embodiments when practicing the claimed subject matter. In the claims, the word "comprising" does not exclude other elements or steps that are not listed, the indefinite article "a" or "an" does not exclude a plurality, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". The mere fact that certain measures are recorded in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A method for determining an order delivery plan, comprising: Obtaining cargo information and delivery route information of each of the multiple freight orders currently to be processed, wherein the delivery route information indicates at least one delivery route associated with the corresponding freight order and a current delivery time of each of the at least one delivery route; Obtaining load information of each of at least one vehicle; generating at least one first delivery plan according to the load information of each vehicle, and the cargo information and delivery route information of each freight order, wherein each of the at least one first delivery plan indicates the quantity and delivery route sequence of the freight orders allocated to each vehicle; and A target delivery plan is determined based on the delivery time of each of the at least one first delivery plan.

2. The method according to claim 1, wherein: Generating at least one first delivery plan according to the load information of each vehicle, and the cargo information and delivery route information of each freight order includes: Taking the load information of each vehicle and the cargo information of each freight order as constraints, and according to the delivery route information of each freight order, the multiple freight orders and the at least one vehicle are arranged and combined to generate the at least one first delivery plan.

3. The method according to claim 1, wherein: After acquiring the load information of each of the at least one vehicle, the method further includes: For each freight order, according to the load information of each vehicle and the cargo information corresponding to the freight order, select one or more vehicles satisfying a first condition from the at least one vehicle to establish a first association relationship with the freight order, wherein the first condition indicates that the cargo of the freight order satisfies the load limit of the corresponding vehicle; and The at least one first delivery plan is generated according to the first association relationship of each freight order, the load information of each vehicle, and the cargo information and delivery route information of each freight order.

4. The method according to claim 1, wherein: The cargo information includes cargo weight and cargo volume, and after acquiring the load information of each of the at least one vehicle, the method further includes: Determine the cargo density of each freight order based on the cargo weight and cargo volume of the freight order; sorting the multiple freight orders according to the cargo density to obtain a loading and unloading sequence, wherein the loading and unloading sequence is used to indicate the loading and unloading order of the multiple freight orders; and The at least one first delivery plan is generated according to the loading and unloading sequence, the load information of each vehicle, and the cargo information and delivery route information of each freight order.

5. The method according to claim 1, wherein: After acquiring the load information of each of the at least one vehicle, the method further includes: For each of the vehicles, Get the current location information of the vehicle; Determine the vehicle delivery range based on the current location information of the vehicle; and Selecting one or more freight orders satisfying a second condition from the at least one freight order to establish a second association relationship with the vehicle, wherein the second condition indicates that the corresponding freight order is associated with at least one delivery route that partially or completely falls within the delivery range of the vehicle; and The at least one first delivery plan is generated according to the second association relationship of each vehicle, the load information of each vehicle, and the cargo information and delivery route information of each freight order.

6. The method according to claim 1, wherein: After acquiring the load information of each of the at least one vehicle, the method further includes: Obtaining a vehicle score for each of the vehicles, wherein the vehicle score is used to indicate the age and condition of the corresponding vehicle; and The at least one first delivery plan is determined based on the vehicle score and load information of each vehicle, and the cargo information and delivery route information of each freight order.

7. The method according to claim 1, wherein: After acquiring the load information of each of the at least one vehicle, the method further includes: Obtaining a driver score for each vehicle, wherein the vehicle score is used to indicate the driving experience and user satisfaction of the driver of the corresponding vehicle; and The at least one first delivery plan is determined based on the driver score and load information of each vehicle, and the cargo information and delivery route information of each freight order.

8. The method according to any one of claims 1 to 7, wherein: In response to an update of at least one of the load information of each vehicle, the cargo information of each freight order, and the delivery route information of each freight order, the at least one first delivery plan is updated based on the updated load information of each vehicle, the cargo information of each freight order, and the delivery route information of each freight order.

9. A device for determining an order delivery plan, comprising: A first acquisition module is configured to acquire cargo information and delivery route information of each of the multiple freight orders currently to be processed, wherein the delivery route information indicates at least one delivery route associated with the corresponding freight order and a current delivery time of each of the at least one delivery route; A second acquisition module is configured to acquire load information of each of at least one vehicle; a generating module configured to generate at least one first delivery plan according to the load information of each vehicle, and the cargo information and delivery route information of each freight order, wherein each of the at least one first delivery plan indicates the quantity and delivery route sequence of the freight orders allocated to each vehicle; and The determination module is configured to determine a target delivery plan based on the delivery time of each first delivery plan in the at least one first delivery plan.

10. A computer device comprising: at least one processor; as well as At least one memory is communicatively connected to the at least one processor, the at least one memory stores instructions, and when the instructions are executed by the at least one processor individually or collectively, the computer device executes the method according to any one of claims 1 to 8.

11. A computer-readable storage medium storing instructions, which, when executed individually or collectively by one or more processors of a computer device, cause the computer device to perform the method of any one of claims 1 to 8.

12. A computer program product comprising instructions, which, when executed individually or collectively by one or more processors of a computer device, cause the computer device to perform the method of any one of claims 1 to 8.

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