Task allocation method, apparatus, device, medium, and program product

Abstract

This invention relates to a task allocation method, apparatus, device, medium, and program product. In this method, the historical similarity allocation gain and matching value corresponding to each task are determined based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be allocated, and the requirement parameters of each task. The task execution cost corresponding to each task is calculated based on the location and speed of each mobile device to be allocated, as well as the task location and preset basic cost of each task. A single-objective optimization model is then constructed and solved to obtain the allocation result for each mobile device to be allocated and each task. This solution, by constructing a single-objective optimization model and solving it using matching value, historical allocation gain, and task execution cost, can improve the matching degree between mobile devices and tasks.

Description

Technical Field

[0001] This invention relates to the field of task scheduling technology, specifically to a task allocation method, apparatus, equipment, medium, and program product. Background Technology

[0002] In complex scenarios such as smart cities, emergency response, and large-scale logistics, mobile devices need to handle multiple tasks, often requiring multiple mobile devices to work together to perform different tasks, which necessitates task allocation. Mobile devices can be robots, drones, autonomous vehicles, etc.

[0003] In the prior art, task allocation typically involves determining the distance between each mobile device and the task's location for each task, and then assigning the task to the mobile device with the smallest distance from the task's location.

[0004] In summary, existing task allocation methods only consider the distance between the mobile device and the task's location, resulting in a low matching degree between the mobile device and the task. Summary of the Invention

[0005] One objective of this invention is to provide a task allocation method to solve the problem that existing task allocation methods only consider the distance between the mobile device and the task's location, resulting in a low matching degree between the mobile device and the task; a second objective is to provide a task allocation method; a third objective is to provide an electronic device; a fourth objective is to provide a readable storage medium; and a fifth objective is to provide a computer program product.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] Firstly, this application provides a task allocation method, including:

[0008] Based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task;

[0009] Based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task, calculate the task execution cost corresponding to each mobile device to be assigned and each task.

[0010] A single-objective optimization model is constructed based on the matching value of each mobile device to be assigned to each task, historical allocation gains, and task execution costs.

[0011] Solving the single-objective optimization model yields the allocation results for each mobile device to be assigned and each task, and the allocation results are used to indicate whether the task is assigned to the mobile device to be assigned.

[0012] Furthermore, the step of determining the historical similarity allocation gain and matching value between each mobile device to be assigned and each task based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task includes:

[0013] Generate a capability vector for each mobile device to be assigned based on its capability parameters;

[0014] Generate a requirement vector for each task based on the requirement parameters for each task;

[0015] Based on the task execution environment description text, the capability vector of each mobile device to be assigned, and the requirement vector of each task, generate a task execution process description vector corresponding to each mobile device to be assigned and each task.

[0016] Based on the capability vector of each mobile device to be assigned and the requirement vector of each task, generate the matching degree between each mobile device to be assigned and each task;

[0017] Based on the multiple historical task execution process description vectors, and the task execution process description vector and matching degree of each mobile device to be assigned to each task, the historical similarity allocation gain of each mobile device to be assigned to each task is determined.

[0018] Based on the matching degree between each mobile device to be assigned and each task, and the preset base value of each task, calculate the matching value between each mobile device to be assigned and each task.

[0019] Further, determining the historical similarity allocation gain for each mobile device to be assigned and each task based on the multiple historical task execution process description vectors, and the task execution process description vector and matching degree corresponding to each task, includes:

[0020] For each historical task execution process description vector, each mobile device to be assigned, and each task, based on the historical task execution process description vector, the task execution process description vector corresponding to the mobile device to be assigned and the task, and the matching degree, determine the candidate similarity allocation gain corresponding to the historical task execution process description vector, the mobile device to be assigned, and the task.

[0021] For each mobile device to be assigned and each task, the maximum value among the historical task execution process description vector and all candidate similarity assignment gains corresponding to the mobile device to be assigned and the task is taken as the historical similarity assignment gain corresponding to the mobile device to be assigned and the task.

[0022] Furthermore, the capability parameters for each mobile device to be assigned include speed, health index, current energy and resource consumption;

[0023] The required parameters for each task include urgency, health index, energy requirement, and resource consumption.

[0024] Furthermore, the objective function of the single-objective optimization model is: max ;

[0025] in, This represents the historical similarity allocation gain between the i-th mobile device to be assigned and the j-th task. This represents the matching value between the i-th mobile device to be assigned and the j-th task. This represents the task execution cost corresponding to the i-th mobile device to be assigned and the j-th task. This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M;

[0026] The objective function represents minimizing task execution cost while maximizing matching value and historical allocation gain.

[0027] Furthermore, the single-objective optimization model includes a first constraint and a second constraint;

[0028] The first constraint is: ;

[0029] The second constraint is: ;

[0030] in, This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M;

[0031] The first constraint means that a task can only be assigned to one mobile device.

[0032] The second constraint means that the number of tasks assigned to a mobile device is less than or equal to 1.

[0033] Furthermore, the method also includes:

[0034] During task execution, the health index of each target mobile device is acquired in real time. The target mobile devices are the mobile devices to be assigned tasks.

[0035] For each target mobile device, if the health index of the target mobile device obtained at the current time is less than the preset health index threshold, an alarm will be issued.

[0036] Furthermore, the method also includes:

[0037] Calculate the average health index of the target mobile device within a preset verification period prior to the current time;

[0038] If the average value is less than the preset health index threshold, then a new mobile device to be assigned is determined, the capability parameters of the new mobile device to be assigned are reacquired, and the requirement parameters and task location of each task are updated.

[0039] Based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters, location and speed of each new mobile device to be assigned, the updated requirement parameters and task location of each task, and the preset basic cost of each task, a new single-objective optimization model is constructed.

[0040] Based on the health index of each new mobile device to be assigned, the objective function of the new single-objective optimization model is updated to obtain the optimized single-objective optimization model.

[0041] Solving the optimized single-objective optimization model yields the allocation results for each new mobile device to be assigned and each task.

[0042] Secondly, this application provides a task allocation device, comprising:

[0043] Processing module, used for:

[0044] Based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task;

[0045] Based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task, calculate the task execution cost corresponding to each mobile device to be assigned and each task.

[0046] The model building module is used to build a single-objective optimization model based on the matching value of each mobile device to be assigned to each task, historical allocation gains, and task execution costs.

[0047] The allocation module is used to solve the single-objective optimization model to obtain the allocation result corresponding to each mobile device to be allocated and each task. The allocation result is used to indicate whether the task is allocated to the mobile device to be allocated.

[0048] Thirdly, this application provides an electronic device, comprising:

[0049] Processor, memory, communication interface;

[0050] The memory is used to store the executable instructions of the processor;

[0051] The processor is configured to execute the task allocation method described in any of the first aspects by executing the executable instructions.

[0052] Fourthly, this application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the task allocation method described in any of the first aspects.

[0053] Fifthly, this application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the task allocation method described in any of the first aspects.

[0054] The beneficial effects of this invention are:

[0055] (1) This application determines the historical similarity allocation gain and matching value between each mobile device to be assigned and each task based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task; and calculates the task execution cost between each mobile device to be assigned and each task based on the location and speed of each mobile device to be assigned, as well as the task location and preset basic cost of each task. Then, based on the matching value, historical allocation gain, and task execution cost between each mobile device to be assigned and each task, a single-objective optimization model is constructed; and the allocation result between each mobile device to be assigned and each task is obtained by solving the model. This solution can improve the matching degree between mobile devices and tasks by constructing a single-objective optimization model and solving it based on the matching value, historical allocation gain, and task execution cost between the mobile devices to be assigned and the task.

[0056] (2) This application obtains the allocation result by constructing a single-objective optimization model and solving it. The single-objective optimization model takes into account the task execution cost. Executing the task according to the allocation result can reduce the task execution cost.

[0057] (3) This application obtains the allocation result by constructing a single-objective optimization model and solving it. The single-objective optimization model takes into account the historical allocation gain, which can improve the allocation success rate. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0059] Figure 1 A flowchart illustrating an embodiment of the task allocation method provided in this application;

[0060] Figure 2 A flowchart illustrating Embodiment 2 of the task allocation method provided in this application;

[0061] Figure 3 A flowchart illustrating Embodiment 3 of the task allocation method provided in this application;

[0062] Figure 4 A schematic diagram of the structure of an embodiment of the task allocation device provided in this application;

[0063] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application.

[0064] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0065] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0066] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0067] In complex scenarios such as smart cities, emergency response, and large-scale logistics, multiple tasks need to be handled, often requiring multiple mobile devices to work collaboratively to perform different tasks. This necessitates task allocation. Mobile devices can include robots, drones, autonomous vehicles, and more.

[0068] In existing technologies, task allocation typically involves determining the distance between each mobile device and the task's location for each task, and then assigning the task to the mobile device with the shortest distance. However, considering only the distance between the mobile device and the task's location leads to a low matching rate between the mobile device and the task.

[0069] To address the problems existing in the prior art, the inventors discovered during the task execution process of researching task allocation methods that different mobile devices have different task execution capabilities, and different tasks also have different task requirements. Capabilities and requirements correspond, so allocation can be performed based on the capability parameters of the mobile devices and the requirement parameters of the tasks, improving the matching degree between mobile devices and tasks. Based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters, location, and speed of each mobile device to be allocated, as well as the requirement parameters, task location, and preset basic cost of each task, the matching value, historical allocation gain, and task execution cost corresponding to each mobile device to be allocated and each task are determined, and a single-objective optimization model is constructed; then, the allocation result corresponding to each mobile device to be allocated and each task is obtained. Based on the above inventive concept, the task allocation scheme in this application was designed.

[0070] The task allocation method in this application can be executed by a computer, a server, a terminal device, etc. This application does not limit it. The following description uses a computer as an example.

[0071] The following examples illustrate the application scenarios of the task allocation method provided in this application.

[0072] For example, in this application scenario, users need multiple vehicles to travel to different locations and perform different display actions, such as vehicles jumping, vehicle windows rising and falling in sync with music, and vehicles swaying left and right. Staff determine the task execution environment description text and multiple tasks. The tasks are vehicle action display tasks, each with corresponding requirement parameters, task location, and preset basic cost. Each vehicle is a mobile device, and each mobile device can transmit its status, capability parameters, location, and speed to the computer. The status of the mobile device is either active, idle, or malfunctioning.

[0073] After the staff inputs the requirements parameters, task location, and preset basic cost of each task into the computer, the computer will select idle mobile devices as mobile devices to be assigned. Then, it will retrieve multiple historical task execution process description vectors from the stored data, and combine them with the task execution environment description text, the capability parameters of each mobile device to be assigned, and the requirements parameters of each task to determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task.

[0074] The computer then calculates the task execution cost for each mobile device to be assigned and each task based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task.

[0075] Then, based on the matching value of each mobile device to be assigned to each task, the historical allocation gain, and the task execution cost, a single-objective optimization model is constructed.

[0076] Solving the single-objective optimization model yields the allocation results for each mobile device to be assigned and each task. The allocation results are used to indicate whether the task is assigned to the mobile device to be assigned.

[0077] Subsequently, the computer assigns a task to each mobile device to be assigned. If the assignment result indicates that the task should be assigned to the mobile device to be assigned, then the task is sent to the mobile device to be assigned. The mobile device to be assigned executes the task, the vehicle travels to the task's starting point, and performs an action demonstration.

[0078] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of the various devices included in the scenario, nor do they limit the interaction method between devices. In the specific application of the solution, it can be set according to actual needs.

[0079] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0080] Figure 1 This is a flowchart illustrating an embodiment of the task allocation method provided in this application. This embodiment describes how, after determining the matching value, historical allocation gain, and task execution cost of each mobile device to be allocated and each task based on the capability parameters, location, and speed of the mobile device to be allocated, as well as the task's requirement parameters, task location, and preset basic cost, a single-objective optimization model is constructed to solve for the allocation result corresponding to each mobile device to be allocated and each task. The method in this embodiment can be implemented through software, hardware, or a combination of both. Figure 1 As shown, this task allocation method specifically includes the following steps:

[0081] S101: Based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task.

[0082] When assigning tasks, staff input the required parameters, location, and preset base cost for each task into the computer. Each mobile device also transmits its status, capability parameters, location, and speed to the computer; the mobile device's status is indicated as active, idle, or faulty. The computer will select idle mobile devices as those to be assigned and will also retrieve multiple historical task execution process description vectors from stored data.

[0083] It should be noted that the task can be a vehicle action demonstration task, a robot action demonstration task, a search and rescue task, a charging rescue task, a transportation task, an unloading task, a cargo sorting task, etc. This application embodiment does not limit the task and can be determined according to the actual situation.

[0084] In this step, after the computer obtains the task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, it determines the historical similarity allocation gain and matching value of each mobile device to be assigned and each task based on these data.

[0085] The capability parameters for each mobile device to be assigned include speed, health index, current energy, and resource consumption. The requirement parameters for each task include urgency, required health index, required energy, and required resource consumption. Capability parameters may also include load capacity, and requirement parameters may include required load capacity. Load capacity and required load capacity can be computational load, storage load, traffic load, weight, etc. This application embodiment does not limit the capability parameters, requirement parameters, load capacity, and required load capacity; they can be determined according to actual circumstances.

[0086] It's important to note that the health index characterizes the health level of a mobile device; a higher health index indicates a healthier device. Current energy can be the remaining battery power or fuel level. The demand health index characterizes the health level required for the mobile device to complete a task. Demand energy can be the amount of battery power or fuel required to complete the task.

[0087] Specifically, a capability vector is generated for each mobile device to be assigned, based on its capability parameters. This involves normalizing each data point in the capability parameters and combining them into a vector to obtain the capability vector.

[0088] Based on the requirement parameters for each task, a requirement vector is generated for each task. This involves normalizing each data point in the requirement parameters and then combining them into a vector to obtain the requirement vector.

[0089] It's important to note that elements at the same positions in the capability vector and demand vector correspond. For example, in the capability vector, the first element is the normalized speed, the second is the normalized health index, the third is the normalized load, the fourth is the normalized current energy, and the fifth is the normalized resource consumption. Similarly, in the demand vector, the first element is the normalized urgency level, the second is the normalized demand health index, the third is the normalized demand load, the fourth is the normalized demand energy, and the fifth is the normalized demand resource consumption. Urgency corresponds to speed because higher urgency requires greater speed and faster response times.

[0090] Then, based on the task execution environment description text, the capability vector of each mobile device to be assigned, and the requirement vector of each task, a task execution process description vector corresponding to each mobile device to be assigned and each task is generated.

[0091] In other words, the task execution environment description text is vectorized to obtain the task execution environment vector. Then, for each mobile device to be assigned and each task, the task execution environment vector, the capability vector of the mobile device to be assigned, and the requirement vector of the task are concatenated to obtain the task execution process description vector corresponding to the mobile device to be assigned and the task.

[0092] It should be noted that the task execution environment description text is text describing the environment in which the mobile device performs the task, including the location of obstacles, the location of resource distribution, road condition information, etc. This application embodiment does not limit the content of the task execution environment description text, and it can be determined according to the actual situation.

[0093] It should be noted that the task execution process description vector determined during this task allocation process will be stored and used as the historical task execution process description vector in the next task allocation process.

[0094] It is also necessary to generate a matching degree between each mobile device to be assigned and each task based on the capability vector of each mobile device to be assigned and the requirement vector of each task.

[0095] According to the formula Calculate the matching degree between the mobile device to be assigned and the task. Among them, This represents the matching degree between the i-th mobile device to be assigned and the j-th task. This represents the k-th element in the capability vector of the i-th mobile device to be assigned. This represents the k-th element in the requirement vector for the j-th task. This represents the preset matching weight of the k-th element, where K represents the number of elements in the capability vector, which is also the number of elements in the demand vector.

[0096] The larger the value, the better it can meet the requirements of the k-th element in the capability vector of the i-th mobile device to be assigned.

[0097] It should be noted that the preset matching degree weight is greater than or equal to 0 and less than or equal to 1. It can be 0, 0.3, 0.5, 0.7, 0.9, 1, etc. This application embodiment does not limit the preset matching degree weight, and it can be determined according to the actual situation.

[0098] After obtaining the matching degree, based on the description vectors of the execution process of multiple historical tasks, as well as the description vector of the execution process of each mobile device to be assigned and the matching degree of each task, the historical similarity allocation gain of each mobile device to be assigned and each task is determined.

[0099] That is, for each historical task execution process description vector, each mobile device to be assigned, and each task, based on the historical task execution process description vector, the task execution process description vector of the mobile device to be assigned and the task corresponding to the task, and the matching degree, the candidate similarity allocation gain corresponding to the historical task execution process description vector, the mobile device to be assigned and the task are determined.

[0100] For each mobile device to be assigned and each task, the maximum value among the historical task execution process description vector and all candidate similarity assignment gains corresponding to the mobile device to be assigned and the task is taken as the historical similarity assignment gain corresponding to the mobile device to be assigned and the task.

[0101] According to the formula The calculation involves the description vector of the historical task execution process, the candidate similarity assignment gain corresponding to the mobile device to be assigned, and the task. Indicates the first The historical task execution process description vector, the candidate similarity allocation gain corresponding to the i-th mobile device to be assigned, and the j-th task are all considered. This indicates a preset similarity weight. This represents the matching degree between the i-th mobile device to be assigned and the j-th task. Indicates the first A vector describing the execution process of a historical task. This represents the task execution process description vector corresponding to the i-th mobile device to be assigned and the j-th task.

[0102] It should be noted that the preset similarity weight is greater than or equal to 0 and less than or equal to 1. It can be 0, 0.3, 0.5, 0.7, 0.9, 1, etc. This application embodiment does not limit the preset similarity weight, which can be determined according to the actual situation.

[0103] After obtaining the matching degree, it is also necessary to calculate the matching value of each mobile device to be assigned and each task based on the matching degree of each mobile device to be assigned and each task, as well as the preset basic value of each task.

[0104] According to the formula Calculate the matching value between the mobile device to be assigned and the task. Among them, This represents the matching value between the i-th mobile device to be assigned and the j-th task. This indicates the preset matching value weight. This represents the matching degree between the i-th mobile device to be assigned and the j-th task. This represents the preset basic value of the j-th task.

[0105] It should be noted that the preset matching value weight is greater than or equal to 0 and less than or equal to 1. It can be 0, 0.3, 0.5, 0.7, 0.9, 1, etc. The preset base value can be 10, 50, 100, 1000, 10000, etc. This application embodiment does not limit the preset similarity weight or the preset base value; they can be determined according to the actual situation.

[0106] S102: Based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task, calculate the task execution cost corresponding to each mobile device to be assigned and each task.

[0107] In this step, the computer obtains the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task. Based on this data, it calculates the task execution cost corresponding to each mobile device to be assigned and each task.

[0108] Specifically, the distance between each mobile device to be assigned and each task can be calculated based on the location of each mobile device and the task location of each task, and then the distance can be calculated using the formula. Calculate the task execution cost corresponding to the mobile device to be assigned and the task. Among them, This represents the task execution cost corresponding to the i-th mobile device to be assigned and the j-th task. This represents the distance between the i-th mobile device to be assigned and the j-th task. This represents the speed of the i-th mobile device to be allocated. This represents the preset base cost of the j-th task. Indicates the preset distance weight. This indicates the preset time weight. Here, the distance between the mobile device to be assigned and the task, the time it takes for the mobile device to move to the task location, and the preset base cost are used to characterize the task execution cost.

[0109] It should be noted that the preset distance weight and preset time weight are greater than or equal to 0 and less than or equal to 1, and can be 0, 0.3, 0.5, 0.7, 0.9, 1, etc. The preset base cost can be 0.1, 0.2, 0.5, 1, 10, 50, 100, 1000, 10000, etc. This application embodiment does not limit the preset distance weight, preset time weight, and preset base cost, and they can be determined according to the actual situation.

[0110] It should be noted that the execution order of steps S101 and S102 can be as follows: step S101 can be executed first, followed by step S102; step S102 can be executed first, followed by step S101; or steps S101 and S102 can be executed simultaneously. This embodiment does not limit the execution order of steps S101 and S102, and it can be determined according to the actual situation.

[0111] S103: Construct a single-objective optimization model based on the matching value of each mobile device to be assigned to each task, historical allocation gains, and task execution costs.

[0112] In this step, after the computer obtains the matching value, historical allocation gain, and task execution cost of each mobile device to be assigned to each task, it can construct a single-objective optimization model.

[0113] Specifically, the objective function of the single-objective optimization model is: max .

[0114] The single-objective optimization model includes a first constraint and a second constraint.

[0115] The first constraint is: .

[0116] The second constraint is: .

[0117] in, This represents the historical similarity allocation gain between the i-th mobile device to be assigned and the j-th task. This represents the matching value between the i-th mobile device to be assigned and the j-th task. This represents the task execution cost corresponding to the i-th mobile device to be assigned and the j-th task. This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M. When the value is 0, it indicates that the j-th task has not been assigned to the i-th pending mobile device; When the value is 1, it means that the j-th task is assigned to the i-th mobile device to be assigned.

[0118] The objective function aims to maximize matching value and historical allocation gain while minimizing task execution cost.

[0119] The first constraint states that a task can only be assigned to one mobile device.

[0120] The second constraint states that the number of tasks assigned to a mobile device is less than or equal to 1.

[0121] S104: Solve the single-objective optimization model to obtain the allocation results of each mobile device to be assigned and each task.

[0122] In this step, after the computer constructs a single-objective optimization model, it solves the single-objective optimization model to obtain the allocation result corresponding to each mobile device to be assigned and each task. The allocation result is used to indicate whether the task is assigned to the mobile device to be assigned.

[0123] Subsequently, for each mobile device to be assigned and each task corresponding to it, if the assignment result is used to indicate that the task is assigned to the mobile device to be assigned, the computer sends the task to the mobile device to be assigned so that the mobile device to be assigned can execute the task.

[0124] It should be noted that the algorithm for solving the single-objective optimization model can be simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization, etc. The embodiments of this application do not limit the algorithm for solving the single-objective optimization model, and it can be determined according to the actual situation.

[0125] The task allocation method provided in this embodiment determines the historical similarity allocation gain and matching value between each mobile device to be allocated and each task based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be allocated, and the requirement parameters of each task. It then calculates the task execution cost between each mobile device to be allocated and each task based on the location and speed of each mobile device to be allocated, as well as the task location and preset basic cost of each task. Furthermore, it constructs a single-objective optimization model based on the matching value, historical allocation gain, and task execution cost of each mobile device to be allocated and each task, and solves this model to obtain the allocation result for each mobile device to be allocated and each task. This solution, by constructing a single-objective optimization model based on the matching value, historical allocation gain, and task execution cost of the mobile device to be allocated and the task, and solving it to obtain the allocation result, can improve the matching degree between mobile devices and tasks, increase the allocation success rate, and reduce task execution costs.

[0126] Figure 2 This is a flowchart illustrating a second embodiment of the task allocation method provided in this application. Based on the above embodiments, this application embodiment explains the situation where, during task execution, the computer detects a low health index of the mobile device and issues an alarm. For example... Figure 2 As shown, this task allocation method specifically includes the following steps:

[0127] S201: During task execution, acquire the health index of each target mobile device in real time.

[0128] After the computer completes the task assignment, it designates the mobile device to be assigned the task as the target mobile device. The target mobile device then changes its status to "working" and executes the task.

[0129] In this step, during the execution of the task, in order to detect any abnormalities in the mobile devices in a timely manner and ensure the smooth completion of the task, the computer needs to obtain the health index of each target mobile device in real time.

[0130] The target mobile device will send its own health index to the computer in real time, and the computer can obtain the health index of each target mobile device in real time.

[0131] S202: For each target mobile device, if the health index of the target mobile device obtained at the current time is less than the preset health index threshold, an alarm will be issued.

[0132] In this step, after the computer obtains the health index, if the health index of the target mobile device obtained at the current time is less than the preset health index threshold, it indicates that the target mobile device is abnormal, and an alarm is issued.

[0133] It should be noted that the health index is a value greater than or equal to 0 and less than or equal to 1. The preset health index threshold can be 0.1, 0.2, 0.3, etc. This application embodiment does not limit the preset health index threshold, which can be determined according to the actual situation.

[0134] The task allocation method provided in this embodiment can issue an alarm when the health index of the target mobile device is detected to be less than a preset health index threshold. This allows users to know the actual health status of the mobile device in a timely manner, thereby improving the timeliness of mobile device maintenance, enhancing the security of the mobile device, and ensuring the sequential completion of tasks.

[0135] Figure 3 This is a flowchart illustrating a third embodiment of the task allocation method provided in this application. Based on the above embodiments, this application describes the situation where the computer determines that the target mobile device has malfunctioned, and then reassigns tasks. Figure 3 As shown, this task allocation method specifically includes the following steps:

[0136] S301: Calculate the average health index of the target mobile device within the preset verification time before the current moment.

[0137] In this step, after the computer issues an alarm, it indicates that the target mobile device is abnormal. It may not be faulty, but the mobile device cannot work when it is faulty. Therefore, in order to determine whether the target mobile device is abnormal, the average value of the health index of the target mobile device within the preset verification time before the current time is calculated.

[0138] It should be noted that the preset verification time can be 30 seconds, 1 minute, 5 minutes, etc. This application embodiment does not limit the preset verification time, which can be determined according to the actual situation.

[0139] S302: If the average value is less than the preset health index threshold, then a new mobile device to be assigned is determined, the capability parameters of the new mobile device to be assigned are obtained again, and the requirement parameters and task location of each task are updated.

[0140] In this step, the computer obtains the average health index of the target mobile device within the preset verification period before the current time, and then determines whether the average value is less than the preset health index threshold. If the average value is less than the preset health index threshold, it indicates that the target mobile device is abnormal and the task needs to be reassigned.

[0141] Specifically, the computer needs to re-determine new mobile devices to be assigned. This requires the computer to send a first state modification instruction to the target mobile device, which then changes its state to faulty. The computer then sends a second state modification instruction to all other target mobile devices besides the target one, causing these target mobile devices to change their state to idle. Since each mobile device transmits its state, capability parameters, location, and speed to the computer, the computer will identify idle mobile devices as new mobile devices to be assigned and will also be able to obtain the capability parameters, location, and speed of these new mobile devices.

[0142] The computer also needs to update the requirement parameters and task location for each task. Since the target mobile device is the mobile device to which the task has been assigned, the current location of the target mobile device that assigned the task is used as the updated task location for each task.

[0143] For each task, the mobile device sends the task completion status to the computer during task execution. The computer then uses the product of the task's urgency and completion status as the updated urgency. The task's demand health index remains unchanged. The computer uses the product of the task's energy demand and completion status as the updated energy demand. Finally, the computer uses the product of the task's resource consumption and completion status as the updated resource consumption.

[0144] It should be noted that if the average value is greater than or equal to the preset health index threshold, it means that the target mobile device is only experiencing occasional abnormalities and can continue to work without the need for task reassignment.

[0145] It should be noted that the computer can also update the task execution environment description text by adding the current location of the target mobile device as the obstacle location to the original task execution environment description text, thus obtaining a new task execution environment description text.

[0146] S303: Based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters, location and speed of each new mobile device to be assigned, the updated requirement parameters and task location of each task, and the preset basic cost of each task, construct a new single-objective optimization model.

[0147] In this step, the computer reacquires the capability parameters of the new mobile devices to be assigned, updates the requirement parameters and task location for each task, and then constructs a new single-objective optimization model based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters, location and speed of each new mobile device to be assigned, the updated requirement parameters and task location for each task, and the preset base cost for each task.

[0148] It should be noted that this step is similar to the implementation process of steps S101-S103 in Embodiment 1, and will not be described again here.

[0149] S304: Based on the health index of each new mobile device to be assigned, update the objective function of the new single-objective optimization model to obtain the optimized single-objective optimization model.

[0150] In this step, after the computer constructs a new single-objective optimization model, in order to further improve the allocation accuracy, the objective function of the new single-objective optimization model can be updated according to the health index of each new mobile device to be allocated, resulting in an optimized single-objective optimization model.

[0151] The objective function of the optimized single-objective optimization model is: max .

[0152] in, This represents the historical similarity allocation gain between the i-th new mobile device to be assigned and the j-th task. This represents the matching value between the i-th new mobile device to be assigned and the j-th task. This represents the task execution cost corresponding to the i-th new mobile device to be assigned and the j-th task. This represents the allocation result between the i-th new mobile device to be assigned and the j-th task. The value is 0 or 1, where N represents the number of new mobile devices to be assigned, and M represents the number of tasks. N is greater than or equal to M. This indicates a preset risk weight. This represents the health index of the i-th new mobile device to be assigned. This indicates the preset health index threshold. This indicates a pre-defined nonlinear function that is positively correlated with the independent variable. When the value is 0, it means that the j-th task has not been assigned to the i-th new mobile device to be assigned. When the value is 1, it means that the j-th task is assigned to the i-th new mobile device to be assigned.

[0153] The greater the difference between the health index and the preset health index threshold, the lower the risk of mobile device failure, and the more tasks should be assigned to it. Therefore, an additional function was added to the original objective function. .

[0154] It should be noted that the preset risk weight is greater than or equal to 0 and less than or equal to 1. It can be 0, 0.3, 0.5, 0.7, 0.9, 1, etc. This application embodiment does not limit the preset risk weight, which can be determined according to the actual situation.

[0155] It should be noted that the preset nonlinear function can be... , , In this application, the embodiments do not limit the preset nonlinear function, which can be determined according to the actual situation.

[0156] S305: Solve the optimized single-objective optimization model to obtain the allocation results of each new mobile device to be assigned and each task.

[0157] It should be noted that this step is similar to the task execution process of step S104 in Embodiment 1, and will not be described again here.

[0158] The task allocation method provided in this embodiment ensures the normal execution of tasks by reallocating tasks after detecting a fault in the target mobile device.

[0159] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0160] Figure 4 A schematic diagram of the structure of an embodiment of the task allocation device provided in this application; as shown Figure 4 As shown, the task allocation device 40 includes:

[0161] Processing module 41 is used for:

[0162] Based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task;

[0163] Based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task, calculate the task execution cost corresponding to each mobile device to be assigned and each task.

[0164] The model building module is used to build a single-objective optimization model based on the matching value of each mobile device to be assigned to each task, historical allocation gains, and task execution costs.

[0165] The allocation module 42 is used to solve the single-objective optimization model to obtain the allocation result of each mobile device to be allocated and each task. The allocation result is used to indicate whether the task is allocated to the mobile device to be allocated.

[0166] Furthermore, processing module 41 is specifically used for:

[0167] Generate a capability vector for each mobile device to be assigned based on its capability parameters;

[0168] Generate a requirement vector for each task based on the requirement parameters for each task;

[0169] Based on the task execution environment description text, the capability vector of each mobile device to be assigned, and the requirement vector of each task, generate a task execution process description vector corresponding to each mobile device to be assigned and each task.

[0170] Based on the capability vector of each mobile device to be assigned and the requirement vector of each task, generate the matching degree between each mobile device to be assigned and each task;

[0171] Based on multiple historical task execution process description vectors, as well as the task execution process description vector and matching degree of each mobile device to be assigned to each task, the historical similarity allocation gain of each mobile device to be assigned to each task is determined.

[0172] Based on the matching degree between each mobile device to be assigned and each task, and the preset base value of each task, calculate the matching value between each mobile device to be assigned and each task.

[0173] Furthermore, processing module 41 is specifically used for:

[0174] For each historical task execution process description vector, each mobile device to be assigned, and each task, the candidate similarity allocation gain corresponding to the historical task execution process description vector, the mobile device to be assigned, and the task execution process description vector and matching degree are determined.

[0175] For each mobile device to be assigned and each task, the maximum value among the historical task execution process description vector and all candidate similarity assignment gains corresponding to the mobile device to be assigned and the task is taken as the historical similarity assignment gain corresponding to the mobile device to be assigned and the task.

[0176] Furthermore, the capability parameters for each mobile device to be assigned include speed, health index, current energy and resource consumption;

[0177] The required parameters for each task include urgency, health index, energy requirement, and resource consumption.

[0178] Furthermore, the objective function of the single-objective optimization model is: max ;

[0179] in, This represents the historical similarity allocation gain between the i-th mobile device to be assigned and the j-th task. This represents the matching value between the i-th mobile device to be assigned and the j-th task. This represents the task execution cost corresponding to the i-th mobile device to be assigned and the j-th task. This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M;

[0180] The objective function aims to maximize matching value and historical allocation gain while minimizing task execution cost.

[0181] Furthermore, the single-objective optimization model includes a first constraint and a second constraint;

[0182] The first constraint is: ;

[0183] The second constraint is: ;

[0184] in, This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M;

[0185] The first constraint states that a task can only be assigned to one mobile device.

[0186] The second constraint states that the number of tasks assigned to a mobile device is less than or equal to 1.

[0187] Furthermore, processing module 41 is also used for:

[0188] During task execution, the health index of each target mobile device is acquired in real time. The target mobile devices are the mobile devices to be assigned tasks.

[0189] For each target mobile device, if the health index of the target mobile device obtained at the current moment is less than the preset health index threshold, an alarm will be issued.

[0190] Furthermore, processing module 41 is also used for:

[0191] Calculate the average health index of the target mobile device within the preset verification time before the current moment;

[0192] If the average value is less than the preset health index threshold, then a new mobile device to be assigned is determined, the capability parameters of the new mobile device to be assigned are reacquired, and the requirement parameters and task location of each task are updated.

[0193] Based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters, location and speed of each new mobile device to be assigned, the updated requirement parameters and task location of each task, and the preset base cost of each task, a new single-objective optimization model is constructed.

[0194] Based on the health index of each new mobile device to be assigned, the objective function of the new single-objective optimization model is updated to obtain the optimized single-objective optimization model.

[0195] Solve the optimized single-objective optimization model to obtain the allocation results of each new mobile device to be assigned and each task.

[0196] The task allocation device provided in this embodiment is used to execute the technical solutions in any of the aforementioned method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0197] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 5 As shown, the electronic device 50 includes:

[0198] Processor 51, memory 52, and communication interface 53;

[0199] Memory 52 is used to store executable instructions of processor 51;

[0200] The processor 51 is configured to execute the technical solutions in any of the foregoing method embodiments by executing executable instructions.

[0201] Optionally, the memory 52 can be either standalone or integrated with the processor 51.

[0202] Optionally, when the memory 52 is a device independent of the processor 51, the electronic device 50 may further include:

[0203] Bus 54, memory 52 and communication interface 53 are connected to processor 51 through bus 54 and complete communication with each other. Communication interface 53 is used to communicate with other devices.

[0204] Optionally, the communication interface 53 can be implemented using a transceiver. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write databases, and read-only databases). The memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0205] Bus 54 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0206] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0207] The electronic device is used to execute the technical solutions in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0208] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0209] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing method embodiments.

[0210] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0211] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0212] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0213] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0214] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0215] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0216] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A task allocation method, characterized in that, include: Based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task; Based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task, calculate the task execution cost corresponding to each mobile device to be assigned and each task. A single-objective optimization model is constructed based on the matching value of each mobile device to be assigned to each task, historical allocation gains, and task execution costs. Solving the single-objective optimization model yields the allocation results for each mobile device to be assigned and each task, and the allocation results are used to indicate whether the task is assigned to the mobile device to be assigned.

2. The method according to claim 1, characterized in that, The step of determining the historical similarity allocation gain and matching value between each mobile device to be assigned and each task based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task includes: Generate a capability vector for each mobile device to be assigned based on its capability parameters; Generate a requirement vector for each task based on the requirement parameters for each task; Based on the task execution environment description text, the capability vector of each mobile device to be assigned, and the requirement vector of each task, generate a task execution process description vector corresponding to each mobile device to be assigned and each task. Based on the capability vector of each mobile device to be assigned and the requirement vector of each task, generate the matching degree between each mobile device to be assigned and each task; Based on the multiple historical task execution process description vectors, and the task execution process description vector and matching degree of each mobile device to be assigned to each task, the historical similarity allocation gain of each mobile device to be assigned to each task is determined. Based on the matching degree between each mobile device to be assigned and each task, and the preset base value of each task, calculate the matching value between each mobile device to be assigned and each task.

3. The method according to claim 2, characterized in that, The step of determining the historical similarity allocation gain for each mobile device to be assigned and each task based on the multiple historical task execution process description vectors, and the task execution process description vector and matching degree corresponding to each task, includes: For each historical task execution process description vector, each mobile device to be assigned, and each task, based on the historical task execution process description vector, the task execution process description vector corresponding to the mobile device to be assigned and the task, and the matching degree, determine the candidate similarity allocation gain corresponding to the historical task execution process description vector, the mobile device to be assigned, and the task. For each mobile device to be assigned and each task, the maximum value among the historical task execution process description vector and all candidate similarity assignment gains corresponding to the mobile device to be assigned and the task is taken as the historical similarity assignment gain corresponding to the mobile device to be assigned and the task.

4. The method according to claim 1, characterized in that, The capability parameters for each mobile device to be assigned include speed, health index, current energy and resource consumption; The required parameters for each task include urgency, health index, energy requirement, and resource consumption.

5. The method according to claim 1, characterized in that, The objective function of the single-objective optimization model is: max ; in, This represents the historical similarity allocation gain between the i-th mobile device to be assigned and the j-th task. This represents the matching value between the i-th mobile device to be assigned and the j-th task. This represents the task execution cost corresponding to the i-th mobile device to be assigned and the j-th task. This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M; The objective function represents minimizing task execution cost while maximizing matching value and historical allocation gain.

6. The method according to claim 1, characterized in that, The single-objective optimization model includes a first constraint and a second constraint. The first constraint is: ; The second constraint is: ; in, This represents the allocation result between the i-th mobile device to be assigned and the j-th task. It can be 0 or 1, where N represents the number of mobile devices to be assigned, M represents the number of tasks, and N is greater than or equal to M; The first constraint means that a task can only be assigned to one mobile device. The second constraint means that the number of tasks assigned to a mobile device is less than or equal to 1.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: During task execution, the health index of each target mobile device is acquired in real time. The target mobile devices are the mobile devices to be assigned tasks. For each target mobile device, if the health index of the target mobile device obtained at the current time is less than the preset health index threshold, an alarm will be issued.

8. The method according to claim 7, characterized in that, The method further includes: Calculate the average health index of the target mobile device within a preset verification period prior to the current time; If the average value is less than the preset health index threshold, then a new mobile device to be assigned is determined, the capability parameters of the new mobile device to be assigned are reacquired, and the requirement parameters and task location of each task are updated. Based on the task execution environment description text, multiple historical task execution process description vectors, the capability parameters, location and speed of each new mobile device to be assigned, the updated requirement parameters and task location of each task, and the preset basic cost of each task, a new single-objective optimization model is constructed. Based on the health index of each new mobile device to be assigned, the objective function of the new single-objective optimization model is updated to obtain the optimized single-objective optimization model. Solving the optimized single-objective optimization model yields the allocation results for each new mobile device to be assigned and each task.

9. A task allocation device, characterized in that, include: Processing module, used for: Based on the obtained task execution environment description text, multiple historical task execution process description vectors, the capability parameters of each mobile device to be assigned, and the requirement parameters of each task, determine the historical similarity allocation gain and matching value of each mobile device to be assigned and each task; Based on the location and speed of each mobile device to be assigned, as well as the task location and preset base cost of each task, calculate the task execution cost corresponding to each mobile device to be assigned and each task. The model building module is used to build a single-objective optimization model based on the matching value of each mobile device to be assigned to each task, historical allocation gains, and task execution costs. The allocation module is used to solve the single-objective optimization model to obtain the allocation result corresponding to each mobile device to be allocated and each task. The allocation result is used to indicate whether the task is allocated to the mobile device to be allocated.

10. An electronic device, characterized in that, include: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the task allocation method according to any one of claims 1 to 8 by executing the executable instructions.

11. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the task allocation method according to any one of claims 1 to 8.

12. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, is used to implement the task allocation method according to any one of claims 1 to 8.

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