Task processing method and device

Abstract

The embodiment of the invention provides a task processing method. For the to-be-processed task, determining a to-be-executed task user according to the initial position and a preset coverage area range; determining a target execution user according to the current execution information and the historical task execution attribute of the to-be-executed user and the task attribute information of the to-be-processed task; determining a to-be-selected path according to the initial position, the target position, the position data of the task in the current execution information and map data corresponding to the target area; and determining a target driving path from the at least one to-be-selected path according to the target cost. According to the technical scheme of the embodiment of the invention, the target execution user is determined according to the current execution information of the user, the historical task execution attribute and the attribute information of the task, and the target driving path is determined according to the target cost corresponding to the path, so that the matching work of the task and the execution user can be automatically completed, and the most economical path is determined; the actual distribution time is effectively shortened; and the logistics cost is reduced.

Description

Technical Field

[0001] The disclosed embodiments relate to the technical field of order distribution, and more particularly to a task processing method and device. Background Art

[0002] With the rapid development of e-commerce and local life service industries, consumers have put forward higher requirements for the timeliness, flexibility and accuracy of logistics and delivery, especially in the fields of food delivery, express delivery and instant delivery. For example, when an order task is generated on the consumer side, the logistics and delivery system will receive the information when the order task is placed, such as the merchant address, target delivery address and other information. At this time, the system will display the user's order information, and can arrange staff to complete the delivery task and provide the delivery route for the delivery personnel.

[0003] At present, many delivery services in the industry are still at the stage of relying on manual dispatch or free order grabbing. These two modes are not only inefficient, but also lead to frequent problems of unbalanced resource allocation. For example, in some high-density order areas, there may be a phenomenon of delivery personnel gathering to take orders, while orders in low-density areas may face the dilemma of no one paying attention. In terms of delivery route planning when delivery personnel deliver, most of the existing solutions are only based on simple calculations based on static map data, and fail to effectively incorporate variables such as dynamic traffic conditions, resulting in the final planned delivery route not being the optimal solution. Faced with such high-frequency and real-time order demands, the traditional manual dispatching method is obviously unable to meet the speed of market changes. Its inefficient order allocation mechanism not only causes a decrease in delivery timeliness, but also causes a huge waste of resources. Summary of the invention

[0004] The disclosed embodiments provide a task processing method and device, which can automatically complete the matching work between tasks and execution users, and determine the most economical route, effectively shortening the actual delivery time and reducing logistics costs.

[0005] In a first aspect, an embodiment of the present disclosure provides a task processing method, the method comprising:

[0006] For at least one task to be processed, according to the starting position of the task to be processed and the preset coverage area range, determine at least one user to be tasked to be executed in a target area; wherein the target area is an area determined based on the starting position and the preset coverage area range;

[0007] Determining a target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one task execution user, and the task attribute information of the task to be processed;

[0008] Determine at least one path to be selected according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area;

[0009] A target cost corresponding to the at least one path to be selected is determined, and a target driving path is determined from the at least one path to be selected according to the target cost.

[0010] In a second aspect, an embodiment of the present invention further provides a task processing device, the device comprising:

[0011] A task execution user determination module is used to determine, for at least one task to be processed, at least one task execution user located in a target area according to a starting position of the task to be processed and a preset coverage area range; wherein the target area is an area determined based on the starting position and the preset coverage area range;

[0012] A target execution user determination module, used to determine a target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one task to be executed user and the task attribute information of the task to be processed;

[0013] A path determination module to be selected, used to determine at least one path to be selected according to the starting position, the target position of the task to be processed, the position data of the task being executed in the current execution information, and the map data corresponding to the target area;

[0014] The target driving path determination module is used to determine the target cost corresponding to the at least one path to be selected, and determine the target driving path from the at least one path to be selected according to the target cost.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0016] one or more processors;

[0017] a storage device for storing one or more programs,

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the task processing method as described in any one of the embodiments of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to perform a task processing method as described in any one of the embodiments of the present invention.

[0020] The technical solution of the disclosed embodiment determines the target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the user to be executed, as well as the task attribute information of the task to be processed, and determines the target driving path according to the target cost corresponding to the path to be selected. It solves the problems of uneven order distribution, reduced delivery efficiency and non-optimal delivery routes caused by inefficient order distribution mechanism, and achieves the effect of automatically completing the matching of tasks and execution users, determining the most economical route, and effectively shortening the actual delivery time and reducing logistics costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0022] Figure 1 It is a flowchart of a task processing method provided by an embodiment of the present disclosure;

[0023] Figure 2 It is a flowchart of a task processing method provided by an embodiment of the present disclosure;

[0024] Figure 3 It is a module diagram of a task processing method provided by an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of a task processing method provided by an embodiment of the present disclosure;

[0026] Figure 5 A schematic diagram of the structure of a task processing device provided by an embodiment of the present invention;

[0027] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0029] Before introducing the embodiments of the present disclosure, an exemplary description of the application scenarios of the embodiments of the present disclosure can be given. The technical solution provided by the embodiments of the present disclosure can be applied to any scenario where it is necessary to determine the target task execution user corresponding to the task to be processed, and how the target task execution user executes the task to be processed. Optionally, the task to be processed can be a delivery task, and the target task execution user can be a delivery user.

[0030] Embodiment 1

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0032] Figure 1 It is a flowchart of a task processing method provided by an embodiment of the present disclosure. The embodiment of the present disclosure is applicable to the situation of determining the target execution user and the target driving path when executing a delivery task. The method can be executed by a task processing device, which can be implemented in the form of software and / or hardware. The hardware can be an electronic device such as a server, and the electronic device can execute the task processing method provided by this technical solution.

[0033] like Figure 1 As shown, the method includes:

[0034] S110: for at least one task to be processed, according to the starting position of the task to be processed and the preset coverage area, determining at least one user to be executed by the task to be processed in a target area; wherein the target area is an area determined based on the starting position and the preset coverage area;

[0035] Among them, the pending tasks can be tasks initiated on the client and need to be executed by other users. Optionally, the pending tasks can be tasks in the same city or tasks outside the same city. Correspondingly, if applied in a distribution scenario, the same city tasks and tasks outside the same city are mainly aimed at distribution tasks. For example, distribution tasks can include tasks in the fields of food delivery, same-city express delivery, fresh food, medical supplies, clothing and auto parts, and express delivery. In this embodiment, a pending task is taken as an example for explanation.

[0036] For each pending task, if the task type is different, the corresponding starting location will be different. For example, when the task type is transporting from a certain place to the location of the ordering user, the starting location can be a certain place; when the task type is delivering from the location of the ordering user to other addresses, the starting location is the location of the ordering user. Among them, the preset coverage area range refers to a certain area range determined in advance when delivering pending tasks, and refers to the coverage area range where the starting location of the pending task is located. This range covers all factors that need to be considered in the delivery of pending tasks, and its purpose is to define and standardize the goals, processes and results of the work. The preset coverage area range is to make the work or plan more targeted and efficient. It can reduce deviations and errors in work or plans and prevent waste of time and resources.

[0037] The target area refers to an area determined based on the starting position of the task to be processed and the preset coverage area range.

[0038] The task execution users to be processed refer to all task execution users located in the target area who can execute a task to be processed.

[0039] Specifically, for a certain delivery task to be processed, firstly, a target area is determined according to the starting address of the task to be processed and a preset coverage area range, and then all task execution users who can execute the delivery task in the target area are determined.

[0040] For example, if there is a drug delivery task at this time, the target area at this time is determined according to the address of the merchant selected by the drug ordering user and the preset coverage area to which the address of the selected merchant belongs, and then based on the target area, all task execution users in the area who can perform the delivery task are determined.

[0041] S120, determining a target execution user corresponding to the task to be processed according to current execution information and historical task execution attributes of at least one user to be tasked, and task attribute information of the task to be processed;

[0042] The current execution information includes the distance between the delivery location of the task to be processed and the current location of the user to be tasked and the number of orders currently on hand by the user to be tasked.

[0043] The historical task execution attributes include the average delivery time of each user to be tasked based on historical data, and the historical task execution attributes also include the familiarity of each user to be tasked with the delivery area.

[0044] The task attribute information of the pending task refers to the priority of the pending task determined based on the requirements of the order customer and the order type and other data.

[0045] Among them, the target execution user refers to all the users to be tasked in the target area. The execution user corresponding to the task to be processed is determined based on the distance between the delivery location of the task to be processed and the current location of the user to be tasked, the number of orders currently on hand by the user to be tasked, the historical average delivery time of the user to be tasked, the familiarity of the user to be tasked with the delivery area, and the priority information of the task to be processed.

[0046] Optionally, for at least one user to execute a task, obtain the first task number and current location information of the executing task in the current execution information of the user to execute the task, as well as obtain the average delivery time in the historical task execution attributes and the familiarity attribute value evaluated for the user to execute the task; determine the first distance based on the current location information and the starting position; determine the evaluation attributes of the user to execute the task based on the first distance, the first task number, the average delivery time, the familiarity attribute value, the task type in the task attribute information, and the corresponding weight value; and select the user to execute the task with the largest evaluation attribute as the target execution user.

[0047] The first task quantity refers to the number of orders currently in hand for each task-to-be-executed user. For example, the number of tasks currently in hand for user A to be executed is 10, that is, the first task quantity is equal to 10.

[0048] The current location information refers to the location information of each user who is waiting to perform a task.

[0049] Among them, the average delivery time refers to the average delivery time of each user to be tasked with execution in the historical task execution. It should be noted that the average delivery time is based on the number of delivery orders and delivery distance of the user to be tasked with execution within the historical period, and the average delivery time of the user to be tasked with execution can be updated in the system once a week.

[0050] Among them, the familiarity attribute refers to the familiarity value of each user to be tasked with the delivery area. It should be noted that the familiarity of each user to be tasked with the delivery area can be directly obtained, which can be measured by the historical delivery area coverage frequency and other methods, or can be obtained by other methods. The determination method of the familiarity attribute is not limited in this embodiment. Optionally, the update frequency of the familiarity of the user to be tasked with the delivery area in the system can be once a month.

[0051] The first distance is the distance between the current location information of each user to be tasked and the specific address to which the order needs to be delivered.

[0052] The task type refers to the priority of the pending task, and the priority of the pending task is determined based on the requirements of the order customer and the order type and other data.

[0053] Among them, the weight values ​​include multiple ones, namely, the weight corresponding to the first distance at the current moment, the weight corresponding to the first task quantity at the current moment, the weight corresponding to the average delivery time at the current moment, the weight corresponding to the familiarity attribute value at the current moment, and the weight of the task type in the task attribute information at the current moment. It should be noted that the weights of the first distance, the first task quantity, the average delivery time, the familiarity attribute value, and the task type in the task attribute information change dynamically with time and the real-time status of the system. For example, during rush hour, in order to evacuate orders as quickly as possible, the weight of the first distance may be relatively reduced, while the weight of the first task quantity may be relatively increased, so as to encourage users to take on tasks with a small number of orders first, and avoid delays in delivery due to too many orders for a user to be tasked; for orders with a high-priority task type in the task attribute information, when the system detects such orders, the weight of the task type in the task attribute information will be significantly increased over a period of time to ensure that such orders are given priority; if the number of first tasks in a certain coverage area surges and the familiarity attribute values ​​of users to be tasked are generally low, then the weight of the familiarity attribute value will be increased accordingly, so as to encourage users to be tasked to become familiar with the delivery business in that area and improve overall delivery efficiency.

[0054] The evaluation attribute refers to the result calculated by each user to be tasked based on their respective first distance, first task quantity, average delivery time, familiarity attribute value, task type in the task attribute information, and corresponding weight value.

[0055] Specifically, after obtaining each user to be tasked with executing a task, the evaluation attributes of each user to be tasked with executing a task are calculated based on the first distance, the first number of tasks, the average delivery time, the familiarity attribute value, the task type in the task attribute information, and the corresponding weight value at that moment. The user to be tasked with executing a task having the largest evaluation attribute is selected as the target execution user.

[0056] S130, determining at least one path to be selected according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area;

[0057] The starting position refers to the current position coordinates of the target executing user.

[0058] The target location of the task to be processed refers to the location coordinates of the delivery target address of the task to be delivered.

[0059] Since the target execution user generally does not complete one order before executing another order, multiple orders may be delivered by the target execution user. The location data of the executing task in the current execution information refers to the location coordinates of the executing task in the current execution information of the target execution user.

[0060] Among them, map data refers to the route data corresponding to the marked area in the navigation APP database obtained by the system.

[0061] Among them, the path to be selected refers to all the paths to be selected from the current location coordinates of the target execution user to the location coordinates of the delivery target address of the task to be delivered, based on the location coordinates of the currently executing task and the acquired route information corresponding to the target area.

[0062] Specifically, when the target execution user executes the pending task, at least one path to be selected will be determined according to the navigation APP database, that is, there are generally more than two routes from the current location of the target execution user to the location of the delivery target address of the pending delivery task. The system obtains all routes, that is, all paths to be selected.

[0063] S140, determining a target cost corresponding to at least one path to be selected, and determining a target driving path from the at least one path to be selected according to the target cost;

[0064] The target cost refers to the cost of the target execution user when executing a path to be selected. The smaller the cost value, the more the path to be selected can meet the needs of the user, proving that the probability of the path to be selected is greater.

[0065] The target driving path refers to the path with the smallest target cost among all the paths to be selected.

[0066] Specifically, for all the paths to be selected, the corresponding target costs are calculated. After the target cost values ​​of all the paths to be selected are obtained, the driving path with the smallest corresponding target cost value is determined as the target driving path.

[0067] The technical solution of the disclosed embodiment is, for at least one task to be processed, based on the starting position of the task to be processed and the preset coverage area, at least one user to be executed in the target area is determined, then, based on the current execution information and historical task execution attributes of at least one user to be executed, and the task attribute information of the task to be processed, the target execution user corresponding to the task to be processed is determined, further, based on the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area, at least one path to be selected is determined, finally, the target cost corresponding to at least one path to be selected is determined, and the target driving path is determined from at least one path to be selected according to the target cost. This technical solution can automatically complete the matching of tasks and execution users, determine the most economical route, and effectively shorten the actual delivery time and reduce logistics costs.

[0068] Embodiment 2

[0069] Figure 2 This is a flowchart of a task processing method provided by an embodiment of the present invention. Based on the above embodiment, the step S140 is described in detail. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here.

[0070] like Figure 2 As shown, the method specifically comprises the following steps:

[0071] S210: for at least one task to be processed, determining at least one user to be executed by the task to be processed located in a target area according to a starting position of the task to be processed and a preset coverage area range; wherein the target area is an area determined based on the starting position and the preset coverage area range;

[0072] S220, determining a target execution user corresponding to the task to be processed according to current execution information and historical task execution attributes of at least one user to be tasked, and task attribute information of the task to be processed;

[0073] S230, determining at least one path to be selected according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area;

[0074] S240, for at least one path to be selected, determining at least one to-be-processed sub-segment corresponding to the path to be selected according to the intersection information corresponding to the path to be selected;

[0075] The sub-segment to be processed is a part of the road, and the sub-segment to be processed refers to a section of the road between two intersections.

[0076] For example, if there are two intersections from position A to position B, then the corresponding intersection information is two, and at this time there are three sub-segments from position A to position B.

[0077] S250, for at least one sub-segment to be selected, determining the segment cost of the sub-segment to be selected according to the attribute information of the sub-segment to be selected and the target cost function corresponding to the sub-segment to be selected;

[0078] In this embodiment, the attribute information includes the road section distance, vehicle attributes, theoretical speed, and the average speed corresponding to the target execution user.

[0079] The road segment distance refers to the distance between the current location of the target execution user and the target location of the task to be processed. For example, the distance from node i to node j is distance(i,j). The vehicle attribute refers to the attribute information of the vehicle used by the target execution user when delivering the task to be processed. In general, the target execution user will use electric vehicles or motor vehicles for delivery.

[0080] Among them, the theoretical speed refers to the free flow speed of a certain section of road, that is, the theoretical speed when there is no congestion, and is represented by v0.

[0081] Among them, the average speed corresponding to the target execution user refers to the average speed of the target execution user when delivering on the sub-section. For example, the speeds of electric vehicles and motor vehicles are different, which is represented by v.

[0082] Among them, the target cost function corresponding to the sub-segment to be selected refers to a function that takes the attribute information of the sub-segment to be selected and the weights corresponding to each distance attribute, time attribute and congestion attribute as input, and takes the segment cost result of the sub-segment to be selected as output. The target cost function corresponding to the sub-segment is: C(i,j)=w1×distance(i,j)+w2×T(i,j)+w3×congestion(i,j). w1, w2 and w3 are the corresponding weights respectively, distance(i,j) represents the distance attribute, T(i,j) represents the time attribute, and congestion(i,j) represents the congestion attribute. In this embodiment, according to the segment distance in the vehicle attributes, the unit distance cost corresponding to the segment distance and the unit time cost corresponding to the segment travel time are determined; according to the theoretical speed and the average speed, the unit travel cost corresponding to the travel cost is determined.

[0083] The unit distance cost refers to the cost per unit distance. For example, for electric vehicle delivery, the unit distance cost may range from [0.03-0.15] yuan / meter. The electric vehicle delivery cost considers the battery consumption, vehicle depreciation, and operation and maintenance costs allocated to the unit distance. For motor vehicle delivery, the unit distance cost may range from [0.05-0.3] yuan / meter. The motor vehicle delivery cost is higher, including fuel costs, vehicle depreciation, maintenance and repairs, and driver wages allocated to the unit distance.

[0084] In this embodiment, the congestion coefficient is determined according to the theoretical speed and the average speed; the distance coefficient is determined according to the section distance; the time to be adjusted is determined according to the congestion coefficient, the distance coefficient, the theoretical speed, the average speed and the section distance; the time to be adjusted is adjusted according to the time adjustment coefficient to obtain the travel time of the sub-section to be selected. The time function is: T(i,j)=k d ×distance(i,j)+k c ×CI(i,j)×distance(i,j))=(k d +k c ×CI(i,j))×distance(i,j). Where distance(i,j) represents the distance between the sections, k d Represents the distance coefficient, k c and CI(i,j) are the values ​​obtained by the theoretical speed and the average speed.

[0085] Among them, the congestion coefficient is determined based on the theoretical speed and the average speed. Generally, when the road is in a state of light congestion, that is, the average vehicle speed is about 70%-80% of the free flow speed, the congestion coefficient can range from [0.1-0.3]. When the road is in a state of moderate congestion, that is, the average vehicle speed is about 40%-60% of the free flow speed, the congestion coefficient can range from [0.3-0.6]. When the road is in a state of severe congestion, that is, the average vehicle speed is about 20%-30% of the free flow speed, the congestion coefficient can range from [0.6-1.0].

[0086] The distance coefficient is determined based on the distance of the road section. Different delivery methods, such as electric vehicles and motor vehicles, have different speeds, which will affect the value of the distance coefficient. For example, walking speed is relatively slow, and it takes longer to travel the same distance, so the distance coefficient should be larger when delivering on foot; while the speed of motor vehicles is fast, the distance coefficient is relatively small.

[0087] Considering the adaptability of electric vehicles under different road conditions, the distance coefficient value range may be [0.015, 0.04] minutes / meter. For motor vehicle delivery, the distance coefficient value range is set to [0.005, 0.02] minutes / meter.

[0088] For electric vehicles, first determine its basic time unit. Assuming the average speed of electric vehicles is 5 meters per second, then its basic time unit is 1 divided by the speed, that is, 0.2 seconds per meter, which is 0.0033 minutes per meter when converted to minutes per meter. For motor vehicles, assuming the average speed of motor vehicles is 20 meters per second, then its basic time unit is 1 divided by the speed, that is, 0.05 seconds per meter, which is 0.00083 minutes per meter when converted to minutes per meter. The basic time unit is adjusted when the duration is to be adjusted. It is related to the congestion coefficient, distance coefficient, theoretical speed, average speed and road section distance.

[0089] Among them, the unit time cost refers to the cost corresponding to a unit of time. For example, for electric vehicle delivery, the unit time cost may range from [0.3-1.5] yuan / minute. The cost of electric vehicles waiting for task execution users considers battery consumption and vehicle maintenance costs allocated to unit time. For motor vehicle delivery, the unit time cost may range from [0.5-2.0] yuan / minute. The cost of motor vehicles waiting for task execution users is higher, including wages, fuel costs, vehicle maintenance and other costs allocated to unit time.

[0090] Among them, the unit travel cost refers to the cost corresponding to the unit travel. For example, in a state of light congestion, the unit travel cost may range from [0.1-0.3] yuan / congestion unit. The impact of light congestion on the cost is relatively small. In a state of moderate congestion, the unit travel cost may range from [0.3-0.6] yuan / congestion unit. The impact of moderate congestion on the cost is moderate. In a state of heavy congestion, the unit travel cost may range from [0.6-1.0] yuan / congestion unit. Heavy congestion has a greater impact on the cost.

[0091] In this embodiment, the section cost of the sub-section to be selected is determined based on the unit distance cost, the unit time cost, the unit travel cost and the target cost function, including: calculating the product of the unit distance cost and the section distance to obtain the distance cost corresponding to the distance cost function; calculating the product of the unit travel cost and the travel time to obtain the time cost corresponding to the time cost function; calculating the product of the unit travel cost, the congestion coefficient, the congestion index and the congestion time to obtain the congestion cost; wherein the congestion index is determined based on the theoretical speed and the average speed; and obtaining the section cost of the sub-section to be selected by weighted processing of the distance cost, the time cost and the congestion cost.

[0092] S260, determining a target cost of the path to be selected based on all road segment costs associated with the path to be selected;

[0093] Specifically, after calculating the costs of all sub-segments associated with each path to be selected, the segment costs of all sub-segments associated with each path to be selected are added together, and the sum is the target cost of a path to be selected.

[0094] S270, determining a target cost corresponding to at least one path to be selected, and determining a target driving path from the at least one path to be selected according to the target cost;

[0095] In this embodiment, the to-be-selected path with the minimum target cost is used as the target driving path.

[0096] The method further includes, when the target execution user executes the to-be-processed task according to the target driving path and detects that the congestion coefficient of at least one to-be-selected sub-segment exceeds a preset congestion coefficient threshold, or the congestion cost is greater than a preset cost threshold, re-execute the step of determining the target driving path with the current position as the starting point of the path planning;

[0097] For example, when the congestion level of a certain section on the current path of the user to be tasked exceeds a preset threshold, the system will recalculate the path. For example, if the congestion level of a certain section changes from light congestion to heavy congestion and exceeds the heavy congestion threshold, the current location is used as the starting point of the path planning, and the step of determining the target driving path is re-executed.

[0098] For example, when calculating the total comprehensive cost of the path, the proportion of congestion cost increases significantly, making the current path no longer the best choice. For example, due to a traffic incident, the unit cost of congestion on a certain road section increases, or the degree of congestion continues to increase, causing the total comprehensive cost of the path to increase significantly, exceeding a certain tolerance range (such as the cost increase exceeds (20%) of the original path cost), it will trigger the path recalculation, at which time the current location is used as the starting point of the path planning, and the step of determining the target driving path is re-executed.

[0099] The method also includes, when it is detected that the target execution user has not executed the task to be processed according to the target driving path, re-determining at least one path to be selected according to the current position of the target execution user, and determining the target driving path from the at least one path to be selected.

[0100] For example, if the task execution user deviates from the original planned route due to special reasons (such as taking the wrong road, temporarily changing the route to avoid small-scale congestion, etc.), the system will recalculate the route according to the new location of the task execution user. At the same time, the system will consider the traffic conditions and order distribution around the new location, re-execute the steps of determining at least one path to be selected according to the current location of the target execution user, and determine the target driving path from the at least one path to be selected.

[0101] It should be noted that when the system receives new traffic information, such as a traffic accident or road construction information on a certain road section, it will evaluate the impact of these events on the current path of the user to be tasked. If the accident or construction section is located on the current path of the user to be tasked, or will have a serious impact on the traffic conditions of surrounding roads (such as causing a significant increase in the congestion index of surrounding roads), the system will recalculate the path to avoid these unfavorable factors. If traffic control situations occur, such as temporary road closures, traffic restrictions, etc., and these control measures affect the current path of the user to be tasked, the system will replan the path in a timely manner.

[0102] It should also be noted that when a new order is inserted into the system, and according to the order allocation algorithm, such as the order allocation heuristic algorithm based on multi-factor dynamic weights, it is determined that the order is to be taken over by the current user to execute the task, and the delivery location of the new order makes the original path no longer the optimal path (for example, the delivery location of the new order is significantly different from the direction of the original path, or the total comprehensive cost of the original path increases significantly after the new order is added), the system will recalculate the delivery path to take into account the delivery of the new order.

[0103] It should also be noted that if the priority of an order changes, the system will re-evaluate the route. In order to give priority to the delivery of high-priority orders, it may be necessary to adjust the route of the user to be executed, even if it means giving up some lower-priority orders on the original route.

[0104] It should also be noted that if the work status of the user to be tasked changes, such as a vehicle failure (for motor vehicle or electric vehicle delivery), resulting in a decrease in delivery speed or inability to continue to deliver according to the original planned route, the system will re-plan the route. When recalculating the route, it may consider reallocating some orders to other users to be tasked or adjusting the delivery order and route to minimize the impact on the delivery service.

[0105] The technical solution of the disclosed embodiment is as follows: for the path to be selected, according to the intersection information corresponding to the path to be selected, at least one sub-segment to be processed corresponding to the path to be selected is determined; then, for the sub-segment to be processed, according to the attribute information of the sub-segment to be selected and the target cost function corresponding to the sub-segment to be selected, the segment cost of the sub-segment to be selected is determined; then, based on all segment costs associated with the path to be selected, the target cost of the path to be selected is determined; further, the target cost corresponding to at least one path to be selected is determined; finally, the target driving path is determined from at least one path to be selected according to the target cost. The target driving path is determined according to the target cost corresponding to the path, and the path can be dynamically adjusted and recalculated according to the specific situation to determine the most economical route, effectively shortening the actual delivery time and reducing logistics costs.

[0106] Embodiment 3

[0107] As an optional embodiment of the above embodiment, the solution provided by the embodiment of the present invention is introduced, and a detailed description is given below.

[0108] Figure 3 is a module diagram of a task processing method provided by an embodiment of the present disclosure. Figure 3 As shown, the task processing method consists of the following core modules.

[0109] Order data management module: The order data management module is responsible for receiving and processing all order information from the same city or different cities, including but not limited to order demand time, delivery location, priority, etc. The order data management module updates the order status in real time through big data technology to ensure the accuracy and timeliness of the information.

[0110] Deliveryman data management module: The deliveryman data management module manages the location information, work status, current order volume and delivery capacity of all deliverymen in the same city or in different cities. The deliveryman data management module uses GPS positioning technology to obtain the movement trajectory and location of deliverymen in real time, providing basic data support for order allocation and route planning.

[0111] Order intelligent allocation module: The order intelligent allocation module uses a heuristic algorithm to realize the automatic allocation of orders based on order data and delivery personnel data. The order intelligent allocation module uses an algorithm to quickly screen out the potential optimal delivery personnel combination, and the precise algorithm further optimizes the allocation details to ensure the global optimal allocation.

[0112] Intelligent real-time path planning module and intelligent dynamic scheduling module: The intelligent real-time path planning module and intelligent dynamic scheduling module use real-time traffic data and delivery personnel location information to calculate the optimal delivery path. The intelligent real-time path planning module and intelligent dynamic scheduling module use heuristic algorithms to dynamically adjust the path by combining historical traffic data, order location and delivery personnel's driving route to ensure the shortest delivery time.

[0113] Big data analysis and optimization module: The big data analysis and optimization module continuously optimizes the system's dispatching and routing algorithms by analyzing data such as orders, delivery personnel, and traffic, thereby improving resource utilization and delivery efficiency. The big data analysis and optimization module uses machine learning and deep learning technologies to analyze historical data and continuously improve system performance.

[0114] Order intelligent allocation, execution and tracking module: Based on the big data analysis and optimization module, intelligent order allocation, execution and tracking are realized.

[0115] In actual application scenarios, the system is reflected in the following aspects: order reception and management: the system receives orders from customers in real time, and the order data management module stores and updates the order information to the database, including the delivery destination, delivery time requirements, order priority and other information; real-time update of the deliveryman's location: the deliveryman management module uses GPS positioning and mobile data to track the location, current work status (whether idle), current order volume and other information of all deliverymen in real time, and uploads them to the system; intelligent allocation and path planning: the intelligent order allocation module combines algorithms to analyze the priority of each order and the status of the deliveryman, automatically selects the best deliveryman, and plans the optimal delivery path. The path planning module also considers real-time traffic conditions and dynamically adjusts the delivery route; order execution and tracking: once the allocation is completed, the order will be automatically distributed to the deliveryman's terminal device, and the deliveryman will deliver according to the planned route. The system tracks the order execution in real time and adjusts the deliveryman's task allocation in a timely manner through the dynamic scheduling module; optimization and feedback: the big data analysis module continuously collects data such as order completion, deliveryman performance, customer feedback, etc., optimizes the next order allocation and path planning strategy, and improves the overall efficiency of the system.

[0116] Next, the heuristic algorithm is described in detail.

[0117] Suppose the order set is O = {o1,o2,…,o n}, the set of delivery personnel is D = {d1,d2,…,d m}. For each order o i and delivery personnel j , define a comprehensive evaluation function E(o i， d j ) to measure the delivery personnel j For ordersi The degree of fitness. i， d j )=w1(t)×[1 / distance(o i , d j )]+w2(t)×[1 / order_count(d j )]+w3(t)×priority(o i )×[1 / delivery_time(d j )]+w4(t)×[1 / area_familiarity(d j )],in:

[0118] distance(o i , d j ) indicates order o i Delivery location and delivery personnel j The distance from the current position. order_count(d j ) represents the delivery person d j The number of orders currently on hand. priority(o i ) indicates order o i delivery_time(d j ) represents the delivery person d j Average delivery time (based on historical data). area_familiarity(d j ) represents the delivery person d j Familiarity with the delivery area (which can be measured by historical delivery area coverage frequency, etc.). w1(t), w2(t), w3(t), w4(t) are the weight coefficients of the four factors of distance, order quantity, priority and regional familiarity at the moment, which change dynamically with time and the real-time status of the system.

[0119] The input is detailed information of the order set O, including the delivery location, priority, expected delivery time, etc. of the order; relevant data of the delivery personnel set D, such as the current location of the delivery personnel, the number of orders on hand, historical delivery time, familiarity with each area, etc.; real-time traffic information (used to assist in determining the dynamic adjustment of distance weights) and order priority rules set by the system.

[0120] The output is for each order o i Assign the most suitable delivery person j .

[0121] The algorithm flow is:

[0122] Step 1: Initialize the weight coefficients w1(0), w2(0), w3(0), and w4(0) to preset values ​​(set according to the system's initial strategy and historical experience). Step 2: For each order: traverse the set of couriers. For each couriers, calculate the value of the comprehensive evaluation function. According to the calculation results, record the couriers with the largest value of the comprehensive evaluation function as the potential best couriers for the order. Step 3: Adjust the weight coefficients according to real-time conditions, monitor real-time information such as traffic conditions, order priority distribution, and regional order density; update the weight coefficients according to the set dynamic adjustment principles. Step 4: Re-evaluate the potential best couriers: For each order, traverse the couriers set again, recalculate the value of the comprehensive evaluation function (using the updated weight coefficients); and finally determine the couriers with the largest value of the comprehensive evaluation function as the best couriers for the order. Through this order allocation heuristic algorithm based on multi-factor dynamic weights, the system can dynamically adjust the order allocation strategy according to various real-time conditions, more flexibly adapt to the complex and changeable same-city delivery environment, and improve the intelligence and efficiency of order allocation.

[0123] The technical solution of the disclosed embodiment can effectively meet the high timeliness and flexibility requirements in order processing, maximize the use of distribution resources, and shorten the delivery time.

[0124] Next, the MPPARTD path optimization algorithm is described in detail.

[0125] Assume the current position of the delivery person is P0(x0,y0), and the order delivery location is P n (x n ,y n ), passing through n-1 possible path nodes P i (x i ,y i )(i=1,2,…,n-1), the road travel time function is T(i,j), which represents the estimated travel time from node i to node j, which is affected by real-time traffic conditions and road properties. The path cost function is C(i,j), which represents the comprehensive cost from node i to node j (including distance cost, time cost, congestion cost, etc.). The total estimated travel time of the path is T total =T total =∑i=0,n-1T(i,i+1). The total comprehensive cost of the path C total For: C total=∑i=0,n-1C(i,i+1) Where C(i,j) can be calculated by the following formula: C(i,j)=w1×distance(i,j)+w2×T(i,j)+w3×congestion(i,j). Here, distance(i,j) represents the actual geographical distance from node i to node j, which can be calculated by coordinates. T(i,j) is the estimated travel time as mentioned above; congestion(i,j) represents the congestion level from node i to node j (which can be obtained through real-time traffic data, such as congestion index, vehicle density, etc.), w1, w2 and w3 are weight coefficients, which can be adjusted according to actual conditions. The goal of the system is to find a path that makes C total Minimum, that is, taking into account both time and comprehensive cost optimization.

[0126] The input is the current location coordinates of the delivery person (x0, y0); the coordinates of the order delivery location (x n ,y n ). Real-time traffic data, including congestion conditions of each road section (congestion index, vehicle density, etc.), estimated travel time, etc. City map data, including road network structure, node information, etc.

[0127] The output is the optimal path from the delivery person's current location to the order delivery location, which consists of a series of path node coordinates.

[0128] The algorithm process is as follows: Step 1: Data acquisition and preprocessing: The system first obtains the coordinates of the delivery person's current location, the coordinates of the order delivery location, as well as real-time traffic data and city map data; preprocesses the acquired data, such as converting the map data into a format suitable for algorithm processing, cleaning and integrating the traffic data, and ensuring the accuracy and availability of the data. Step 2: Path search: Starting from the current location of the delivery person, the algorithm uses the current location as the starting point and the order delivery location as the end point to search for possible paths in the road network constructed by the city map data. For each possible path, calculate its total estimated travel time and total comprehensive cost. Step 3: Optimal path selection Based on the calculation results, compare the T of all possible paths. total and C total Value; Select C total The shortest path is taken as the optimal path; if there are multiple paths that meet the conditions, a secondary selection can be made based on other factors (such as the impact of the familiarity of the path on the delivery person, etc.).

[0129] The technical solution of the disclosed embodiment determines the target execution user based on the user's current execution information, historical task execution attributes, and attribute information of the task, and determines the target driving path based on the target cost corresponding to the path. It can automatically complete the matching of tasks and execution users, determine the most economical route, and effectively shorten the actual delivery time and reduce logistics costs.

[0130] Embodiment 4

[0131] Figure 5 is a structural diagram of a task processing device provided by an embodiment of the present disclosure, such as Figure 5 As shown, the device includes: a task execution user determination module 410, a target execution user determination module 420, a path to be selected determination module 430 and a target driving path determination module 440.

[0132] The module 410 for determining the user to execute the task to be processed is used to determine, for at least one task to be processed, at least one user to execute the task to be processed located in a target area according to the starting position of the task to be processed and the preset coverage area range; wherein the target area is an area determined based on the starting position and the preset coverage area range; the module 420 for determining the target user to execute the task to be processed is used to determine the target user to execute the task corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one user to execute the task to be processed, and the task attribute information of the task to be processed; the module 430 for determining the path to be selected is used to determine at least one path to be selected according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area; the module 440 for determining the target driving path is used to determine the target cost corresponding to the at least one path to be selected, and determine the target driving path from the at least one path to be selected according to the target cost.

[0133] On the basis of the above-mentioned technical solutions, the target execution user determination module 420 includes: for the at least one user to execute the task, obtaining the first task quantity and current location information of the executing task in the current execution information of the user to execute the task, and obtaining the average delivery time in the historical task execution attributes and the familiarity attribute value evaluated for the user to execute the task; determining the first distance according to the current location information and the starting position; determining the evaluation attribute of the user to execute the task based on the first distance, the first task quantity, the average delivery time, the familiarity attribute value, the task type in the task attribute information and the corresponding weight value; and taking the user to execute the task with the largest evaluation attribute as the target execution user.

[0134] Based on the above technical solutions, the target driving path determination module 440 includes a sub-segment determination sub-module for processing, a sub-segment cost determination sub-module, a target cost determination sub-module for a path to be selected, and a target driving path determination sub-module.

[0135] A sub-segment to be processed determining sub-module, configured to determine, for the at least one path to be selected, at least one sub-segment to be processed corresponding to the path to be selected according to the intersection information corresponding to the path to be selected;

[0136] The sub-segment cost determination submodule is used to determine the segment cost of the at least one sub-segment to be selected according to the attribute information of the sub-segment to be selected and the target cost function corresponding to the sub-segment to be selected. The target cost function is composed of a distance cost function, a time cost function and a travel cost function, and the attribute information includes segment distance, vehicle attributes, theoretical speed and the average speed corresponding to the target execution user;

[0137] A target cost determination submodule for a path to be selected, configured to determine a target cost of the path to be selected based on all road segment costs associated with the path to be selected;

[0138] The target driving path determination submodule is used to select the to-be-selected path with the minimum target cost as the target driving path.

[0139] The sub-section cost determination submodule includes a unit distance cost and unit time cost determination unit, a unit travel cost determination unit, a section cost determination unit for a sub-section to be selected, and a travel time determination unit.

[0140] A unit distance cost and unit time cost determination unit, used to determine, according to the section distance in the vehicle attribute, a unit distance cost corresponding to the section distance and a unit time cost corresponding to the section travel time;

[0141] a unit passage cost determination unit, configured to determine a unit passage cost corresponding to the passage cost according to the theoretical speed and the average speed;

[0142] a section cost determination unit for the sub-section to be selected, configured to determine the section cost of the sub-section to be selected based on the unit distance cost, the unit time cost, the unit travel cost and the target cost function;

[0143] The travel time determination unit is used to determine the congestion coefficient according to the theoretical speed and the average speed; determine the distance coefficient according to the section distance; determine the time to be adjusted according to the congestion coefficient, the distance coefficient, the theoretical speed, the average speed and the section distance; adjust the time to be adjusted according to the time adjustment coefficient to obtain the travel time of the sub-section to be selected.

[0144] The section cost determination unit for the sub-section to be selected includes a distance cost determination sub-unit, a time cost determination sub-unit, a congestion cost determination sub-unit and a section cost determination sub-unit for the sub-section to be selected.

[0145] a distance cost determination subunit, configured to calculate the product of the unit distance cost and the road section distance to obtain a distance cost corresponding to the distance cost function;

[0146] A time cost determination subunit, used to calculate the product of the unit travel cost and the travel time to obtain the time cost corresponding to the time cost function;

[0147] A congestion cost determination subunit, configured to calculate the product of the unit travel cost, the congestion coefficient, the congestion index and the congestion duration to obtain the congestion cost; wherein the congestion index is determined based on the theoretical speed and the average speed;

[0148] The road section cost determination subunit of the sub-road section to be selected is used to obtain the road section cost of the sub-road section to be selected by weighted processing of the distance cost, the time cost and the congestion cost.

[0149] Based on the above technical solutions, a target driving path redetermination module is also included.

[0150] The target driving path redetermining module is used to, when the target execution user executes the pending task according to the target driving path and detects that the congestion coefficient of at least one sub-segment to be selected exceeds a preset congestion coefficient threshold, or the congestion cost is greater than a preset cost threshold, take the current position as the starting point of path planning and re-execute the step of determining the target driving path; when it is detected that the target execution user does not execute the pending task according to the target driving path, re-execute the step of determining at least one path to be selected according to the current position of the target execution user, and determine the target driving path from the at least one path to be selected.

[0151] The technical solution of the disclosed embodiment relates to a task processing method. The method includes: for at least one task to be processed, according to the starting position of the task to be processed and the preset coverage area, determining at least one user to be executed in the target area; wherein the target area is an area determined based on the starting position and the preset coverage area, then, according to the current execution information and historical task execution attributes of at least one user to be executed, and the task attribute information of the task to be processed, determining the target execution user corresponding to the task to be processed, further, according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area, determining at least one path to be selected, and finally, determining the target cost corresponding to at least one path to be selected, and determining the target driving path from at least one path to be selected according to the target cost. The technical solution of the disclosed embodiment determines the target execution user according to the user's current execution information, the historical task execution attributes, and the attribute information of the task, and determines the target driving path according to the target cost corresponding to the path, which can automatically complete the matching work between the task and the execution user, determine the most economical route, and effectively shorten the actual delivery time and reduce logistics costs.

[0152] The task processing device provided in the embodiments of the present disclosure can execute the task processing method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0153] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present disclosure.

[0154] Embodiment 5

[0155] Figure 6 Schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 6 , which shows an electronic device (eg, Figure 6 The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0156] like Figure 6 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit / output (I / O) interface 505 is also connected to the bus 504.

[0157] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0158] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0159] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0160] The electronic device provided by the embodiment of the present disclosure and the task processing method provided by the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0161] Embodiment 6

[0162] The embodiment of the present disclosure provides a computer storage medium on which a computer program is stored. When the program is executed by a processor, the task processing method provided by the above embodiment is implemented.

[0163] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0164] In some embodiments, the server can communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0165] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0166] For at least one task to be processed, according to the starting position of the task to be processed and the preset coverage area range, determine at least one user to be tasked to be executed in a target area; wherein the target area is an area determined based on the starting position and the preset coverage area range;

[0167] Determining a target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one task execution user, and the task attribute information of the task to be processed;

[0168] Determine at least one path to be selected according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area;

[0169] A target cost corresponding to the at least one path to be selected is determined, and a target driving path is determined from the at least one path to be selected according to the target cost.

[0170] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0171] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0173] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0174] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0175] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0176] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0177] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A task processing method, characterized in that: include: For at least one task to be processed, according to the starting position of the task to be processed and the preset coverage area range, determine at least one user to be tasked to be executed in a target area; wherein the target area is an area determined based on the starting position and the preset coverage area range; Determining a target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one task execution user, and the task attribute information of the task to be processed; Determine at least one path to be selected according to the starting position, the target position of the task to be processed, the location data of the task being executed in the current execution information, and the map data corresponding to the target area; A target cost corresponding to the at least one path to be selected is determined, and a target driving path is determined from the at least one path to be selected according to the target cost.

2. The method according to claim 1, characterized in that The step of determining a target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one task execution user, and the task attribute information of the task to be processed, comprises: For the at least one user to be tasked, obtaining the first task quantity and current location information of the task being executed in the current execution information of the user to be tasked, and obtaining the average delivery time in the historical task execution attributes and the familiarity attribute value evaluated for the user to be tasked; Determining a first distance according to the current position information and the starting position; Determine the evaluation attribute of the user to be tasked based on the first distance, the first number of tasks, the average delivery time, the familiarity attribute value, the task type in the task attribute information, and the corresponding weight value; The task execution user with the largest evaluation attribute is used as the target execution user.

3. The method according to claim 1, characterized in that The determining of the target cost corresponding to the at least one path to be selected includes: For the at least one path to be selected, determining at least one to-be-processed sub-segment corresponding to the path to be selected according to the intersection information corresponding to the path to be selected; For the at least one sub-segment to be selected, determining the segment cost of the sub-segment to be selected according to the attribute information of the sub-segment to be selected and the target cost function corresponding to the sub-segment to be selected; Determining a target cost of the path to be selected based on all road segment costs associated with the path to be selected; The target cost function is composed of a distance cost function, a time cost function and a travel cost function.

4. The method according to claim 3, characterized in that The attribute information includes the road section distance, vehicle attributes, theoretical speed, and the average speed corresponding to the target execution user. The determining the road section cost of the sub-road section to be selected according to the road section attribute information of the sub-road section to be selected and the target cost function includes: According to the road section distance in the vehicle attribute, determining a unit distance cost corresponding to the road section distance and a unit time cost corresponding to the road section travel time; Determining a unit passage cost corresponding to the passage cost according to the theoretical speed and the average speed; The section cost of the sub-section to be selected is determined according to the unit distance cost, the unit time cost, the unit travel cost and the target cost function.

5. The method according to claim 4, characterized in that The method further comprises: Determining the travel time corresponding to the sub-section to be selected; The determining of the travel time corresponding to the sub-section to be selected includes: determining a congestion coefficient according to the theoretical speed and the average speed; Determining a distance coefficient according to the road section distance; Determining the time to be adjusted according to the congestion coefficient, the distance coefficient, the theoretical speed, the average speed and the road section distance; The time to be adjusted is adjusted according to the time adjustment coefficient to obtain the travel time of the sub-section to be selected.

6. The method according to claim 4, characterized in that The determining the section cost of the sub-section to be selected according to the unit distance cost, the unit time cost, the unit travel cost and the target cost function includes: Calculating the product of the unit distance cost and the road section distance to obtain a distance cost corresponding to the distance cost function; Calculate the product of the unit travel cost and the travel time to obtain the time cost corresponding to the time cost function; Calculating the product of the unit travel cost, the congestion coefficient, the congestion index and the congestion duration to obtain the congestion cost; wherein the congestion index is determined based on the theoretical speed and the average speed; The section cost of the sub-section to be selected is obtained by weighting the distance cost, the time cost and the congestion cost.

7. The method according to claim 1, characterized in that The step of determining a target driving path from the at least one path to be selected according to the target cost includes: The path to be selected with the minimum target cost is taken as the target driving path.

8. The method according to claim 1, characterized in that The method further comprises: When the target execution user executes the task to be processed according to the target driving path, if it is detected that the congestion coefficient of at least one sub-segment to be selected exceeds the preset congestion coefficient threshold, or the congestion cost is greater than the preset cost threshold, the current position is used as the starting point of the path planning, and the step of determining the target driving path is re-executed.

9. The method according to claim 1, characterized in that: The method further comprises: When it is detected that the target execution user has not executed the task to be processed according to the target driving path, the step of re-determining at least one path to be selected according to the current position of the target execution user and determining the target driving path from the at least one path to be selected is performed.

10. A task processing device, characterized in that: include: A task execution user determination module is used to determine, for at least one task to be processed, at least one task execution user located in a target area according to a starting position of the task to be processed and a preset coverage area range; wherein the target area is an area determined based on the starting position and the preset coverage area range; A target execution user determination module, used to determine a target execution user corresponding to the task to be processed according to the current execution information and historical task execution attributes of the at least one task to be executed user and the task attribute information of the task to be processed; A path determination module to be selected, used to determine at least one path to be selected according to the starting position, the target position of the task to be processed, the position data of the task being executed in the current execution information, and the map data corresponding to the target area; The target driving path determination module is used to determine the target cost corresponding to the at least one path to be selected, and determine the target driving path from the at least one path to be selected according to the target cost.

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