Task allocation method and device, computer equipment and storage medium

Abstract

The invention relates to the field of computer science, and discloses a task allocation method and device, computer equipment and a storage medium, and the method specifically comprises the steps: obtaining an agent set, at least one candidate task corresponding to each agent, and task information and task matching information of each candidate task; selecting an execution task corresponding to each agent from the candidate tasks of each agent; based on the task information and the task matching information, performing fitness calculation to obtain a target fitness value; on the basis of the target fitness value and the historical execution task, updating the execution task corresponding to each agent, and skipping to the task information and task matching information based on each candidate task to carry out fitness calculation to obtain the target fitness value; and when the target fitness value does not change any more, the target execution task of each agent is determined from the task set based on the obtained target fitness value and the historical execution task, and the task allocation capability, adaptability and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer science, and particularly to a task allocation method, apparatus, computer device, and storage medium. Background Art

[0002] In a multi-agent system (MAS), the task allocation problem (TAP) is a classical optimization problem, which involves how to efficiently allocate a set of tasks to multiple agents so as to optimize the overall performance of the system (such as completion time, resource consumption, coordination efficiency, etc.). Existing task allocation methods usually rely on meta-heuristic algorithms such as genetic algorithms, ant colony algorithms, and particle swarm algorithms. However, when facing high-dimensional and high-complexity task allocation problems, these algorithms often encounter problems such as slow convergence speed and local optimal solutions. Therefore, a new task allocation method is needed to solve the problems of poor allocation ability, poor adaptability, and low efficiency in task allocation. Summary of the Invention

[0003] In view of this, the present invention provides a task allocation method, apparatus, computer device, and storage medium to solve the problems of poor allocation ability, poor adaptability, and low efficiency in task allocation.

[0004] In a first aspect, the present invention provides a task allocation method, which includes:

[0005] Obtain a set of agents, at least one candidate task corresponding to each agent in the set of agents, task information and task matching information of each candidate task; the task matching information is used to indicate the matching degree between the candidate task and the corresponding agent;

[0006] Select the execution task corresponding to each agent from at least one candidate task of each agent;

[0007] Based on the task information and task matching information of each candidate task, perform fitness calculation to obtain a target fitness value;

[0008] Based on the target fitness value and historical execution tasks, update the execution task corresponding to each agent, and jump to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value;

[0009] Until the target fitness value no longer changes, determine the target execution task of each agent from the task set based on the obtained target fitness value and the historical execution tasks.

[0010] The task allocation method provided by the embodiments of the present invention obtains an agent set, at least one candidate task corresponding to each agent in the agent set, the task information and task matching information of each candidate task; and uses the task matching information to indicate the matching degree between the candidate task and the corresponding agent, and reselects the execution task corresponding to each agent from at least one candidate task of each agent; based on the task information and task matching information of each candidate task, fitness calculation is performed to obtain a target fitness value; the calculation simplicity and competitiveness of determining the target fitness value are improved; based on the target fitness value and the historical execution tasks, the execution tasks corresponding to each agent are updated, and it jumps to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value; iterative calculation of the target fitness value and iterative update of the execution tasks are performed, realizing dynamic adjustment of the execution tasks, and thus realizing the efficiency of task allocation; until the target fitness value no longer changes, based on the obtained target fitness value and the historical execution tasks, the target execution tasks of each agent are determined from the task set, improving the accuracy of determining the target execution tasks, and further improving the task allocation ability, adaptability and efficiency of task allocation.

[0011] In an alternative embodiment, the task information includes task complexity, task requirement value, and task execution time; the performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value includes:

[0012] Based on the task complexity, task requirement value, and task execution time of each candidate task, perform task sorting on at least one candidate task corresponding to each agent to obtain a task sorting result;

[0013] Based on the task sorting result and the task matching information, determine the target fitness value.

[0014] The task allocation method provided by the embodiments of the present invention performs task sorting on candidate tasks through the task complexity, task requirement value, and task execution time included in the task information, and then determines the target fitness based on the task sorting result and the task matching information, improving the accuracy and rationality of determining the target fitness value. Further, the accuracy and rigor of using the target fitness value to determine the execution task are improved.

[0015] In an alternative embodiment, the method further includes:

[0016] Obtain a task allocation rule;

[0017] Based on the target fitness value, the historical execution tasks, and the task allocation rule, update the execution tasks corresponding to each agent.

[0018] The task allocation method provided by the embodiments of the present invention updates the execution tasks corresponding to each agent through the target fitness value, historical execution tasks, and task allocation rules, improving the rationality and rigor of updating the execution tasks, and further improving the rationality and accuracy of the target execution tasks.

[0019] In an alternative embodiment, updating the execution tasks corresponding to each agent based on the target fitness value, the historical execution tasks, and the task allocation rules includes:

[0020] Determine the first execution task of each agent based on the target fitness value and the historical execution tasks;

[0021] Match based on the task allocation rules and the first execution task of each agent to obtain a task rule result;

[0022] When the task rule result indicates that the first execution task of each agent complies with the task allocation rules, update the execution tasks corresponding to each agent based on the first execution task of each agent.

[0023] The task allocation method provided by the embodiments of the present invention first determines the first execution task based on the target fitness value and the historical execution tasks, and then matches based on the determined first execution task and the task allocation rules to update the execution tasks corresponding to each agent, improving the dynamics and accuracy of updating the execution tasks.

[0024] In an alternative embodiment, before jumping to the step of calculating the target fitness value based on the task information and task matching information of each candidate task, the method further includes:

[0025] Obtain the updated execution tasks of each agent;

[0026] Match the updated execution tasks of each agent to obtain a matching result;

[0027] When the matching result indicates that there are no identical tasks among the updated execution tasks of each agent, jump to the step of calculating the target fitness value based on the task information and task matching information of each candidate task.

[0028] The task allocation method provided by the embodiments of the present invention realizes the high efficiency of candidate task execution by matching the updated execution tasks, and further improves the efficiency of task allocation.

[0029] In an alternative embodiment, the method further includes:

[0030] When the matching result indicates that there are identical tasks among the updated execution tasks of the agents, re - execute the step of updating the execution tasks corresponding to the agents based on the target fitness value and the historical execution tasks.

[0031] The task allocation method provided by the embodiments of the present invention, by re - executing the step of updating the execution tasks corresponding to each agent based on the target fitness value and the historical execution tasks when the matching result indicates that there are identical tasks among the updated execution tasks, improves the rationality of the technical solution of this application, and further improves the rationality and efficiency of task execution, as well as the rigor of task allocation.

[0032] In an alternative embodiment, the method further includes:

[0033] Obtain a preset number of updates;

[0034] Update the execution tasks corresponding to the agents based on the preset number of updates.

[0035] The task allocation method provided by the embodiments of the present invention updates the execution tasks corresponding to each agent through the preset number of updates, realizes the dynamic adjustment of execution tasks, and further improves the dynamics and applicability of task allocation.

[0036] In a second aspect, the present invention provides a task allocation device, which includes:

[0037] An acquisition module, configured to acquire an agent set, at least one candidate task corresponding to each agent in the agent set, task information of each candidate task, and task matching information; the task matching information is used to indicate the matching degree between the candidate task and the corresponding agent;

[0038] A selection module, configured to select the execution task corresponding to each agent from at least one candidate task of each agent;

[0039] A calculation module, configured to perform fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value;

[0040] An update module, configured to update the execution tasks corresponding to the agents based on the target fitness value and the historical execution tasks, and jump to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value;

[0041] A determination module, configured to determine the target execution tasks of the agents from the task set based on the obtained target fitness value and the historical execution tasks until the target fitness value no longer changes.

[0042] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the task allocation method according to the first aspect or any corresponding embodiment thereof.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, and the computer instructions are used to cause a computer to execute the task allocation method according to the first aspect or any corresponding embodiment thereof.

[0044] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, and the computer instructions are used to cause a computer to execute the task allocation method according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 is a flowchart of the task allocation method according to an embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of the task allocation device according to an embodiment of the present invention;

[0048] Figure 3 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0050] In a multi-agent system (MAS), the task allocation problem (TAP) is a classical optimization problem, which involves how to efficiently allocate a set of tasks to multiple agents to optimize the overall system performance (such as completion time, resource consumption, coordination efficiency, etc.). Existing task allocation methods usually rely on meta-heuristic algorithms such as genetic algorithms, ant colony algorithms, and particle swarm algorithms. However, when facing high-dimensional and high-complexity task allocation problems, these algorithms often encounter problems such as slow convergence speed and local optimal solutions. In particular, in some applications in large-scale, dynamic, and complex environments, a good task allocation method is often more needed to support application services. Therefore, this application proposes a new task allocation method.

[0051] According to an embodiment of the present invention, an embodiment of a task allocation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0052] In this embodiment, a task allocation method is provided, which can be used in task allocation applications. Figure 1 It is a flowchart of the task allocation method according to an embodiment of the present invention, as Figure 1 shown, and this process includes the following steps:

[0053] Step S101, obtain the agent set, at least one candidate task corresponding to each agent in the agent set, the task information and task matching information of each candidate task.

[0054] In a specific embodiment, the agent set can be a set of individuals that need task allocation in the application; at least one candidate task can be an executable task corresponding to each agent set; the task matching information is used to indicate the matching degree between the candidate task and the corresponding agent; specifically, in the task allocation application, a set of all agents (unmanned aerial vehicles) and a task set are defined. Optionally, the defined agent set can be {A1, A2,..., An}; the defined task set can be {T1, T2,..., Tm}, where n represents the number of agents and m represents the number of tasks; further, each task in the task set is initialized and assigned, and correspondingly, the task information and task matching information corresponding to each task are initialized; correspondingly, after obtaining the instruction for task allocation, the agent set in the application, at least one candidate task corresponding to each agent in the agent set, the task information and task matching information of each candidate task are obtained.

[0055] Step S102: Select the execution tasks corresponding to each agent from at least one candidate task of each agent.

[0056] In a specific embodiment, the execution tasks corresponding to each agent may be the execution tasks determined from the candidate execution tasks corresponding to each agent; specifically, select the candidate tasks that each agent currently needs to execute from at least one candidate task of each agent, that is, the execution tasks.

[0057] Step S103: Based on the task information and task matching information of each candidate task, perform fitness calculation to obtain the target fitness value.

[0058] In a specific embodiment, the target fitness value may be the fitness degree information of the candidate task and the agent; specifically, input the previously obtained task information and task matching information of each candidate task into the fitness calculation function to perform fitness calculation, and then calculate the target fitness value.

[0059] Step S104: Based on the target fitness value and the historical execution tasks, update the execution tasks corresponding to each agent, and jump to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain the target fitness value.

[0060] In a specific embodiment, the historical execution tasks may be the previous execution tasks corresponding to each agent before updating the execution tasks; specifically, based on the target fitness value and the historical execution tasks, re-determine the execution tasks, that is, update the execution tasks. Further, after updating the execution tasks, re-perform the fitness calculation based on the task information and task matching information of each candidate task, and then re-determine the target fitness value. Further, based on the re-calculated target fitness value and the historical execution tasks, re-determine the execution tasks of each agent.

[0061] Step S105: Until the target fitness value no longer changes, based on the obtained target fitness value and the historical execution tasks, determine the target execution tasks of each agent from the task set.

[0062] In a specific embodiment, the target execution tasks may be the final tasks determined by each agent in the application; specifically, based on the previous iteration of calculating the target fitness value, iterate until the target fitness value no longer changes, and based on the finally determined target fitness value and the historical execution tasks, determine the target execution tasks corresponding to each agent from the task set.

[0063] In an alternative embodiment, the task information includes task complexity, task requirement value, and task execution time; the above step 103 includes:

[0064] Step 1031: Based on the task complexity, task requirement value, and task execution time of each candidate task, perform task ranking for at least one candidate task corresponding to each agent to obtain a task ranking result.

[0065] Step 1032: Based on the task ranking result and the task matching information, determine the target fitness value.

[0066] In a specific embodiment, the task complexity can be the complexity level of the candidate task; the task requirement value can be the requirement value of the agent for the candidate task; the task execution time can be the time when the candidate task can be executed. Specifically, based on the task complexity, task requirement value, and task execution time of each candidate task, perform task ranking for the candidate tasks corresponding to each agent. Correspondingly, obtain a task ranking result. Optionally, the first task ranking result can be determined based on the task requirement value, and then the first task ranking result can be updated based on the task complexity to determine the second task ranking result. Finally, the second task ranking result can be updated based on the task execution time of the candidate task to determine the task ranking result. Further, based on the task ranking result and the task matching information, determine the target fitness value. Specifically, determine the task ranking of each candidate task based on the task ranking result, and then based on this task ranking, compare the task matching information. Correspondingly, perform comparison calculations on the task matching information in sequence, that is, the first candidate task in the task ranking result is compared with other tasks for task matching information calculation in sequence, and the second candidate task is compared with other tasks for task matching information calculation in sequence. Optionally, finally determine the target fitness value. Optionally, the target fitness value can be represented by data values such as 1, 2, 3, etc.

[0067] In an alternative embodiment, the above step S104 further includes:

[0068] Step S1041: Obtain a task assignment rule.

[0069] Step 1042: Based on the target fitness value, the historical executed tasks, and the task assignment rule, update the executed tasks corresponding to each agent.

[0070] In a specific embodiment, the task assignment rule can be the rule information for assigning executed tasks to the agent. Optionally, the task assignment rule can include that during the process of updating the executed tasks, the pre-set task assignment rule can also be obtained. Then, based on the combination of the target fitness value and the historical executed tasks, and in combination with the task assignment rule, update the executed tasks corresponding to each agent. Specifically, re-select an executed task from the candidate tasks corresponding to each agent based on the calculated target fitness value, that is, update the executed task. Optionally, the executed task can be updated in combination with the historical executed tasks and the task assignment rule.

[0071] In an alternative embodiment, step 1042 above includes:

[0072] Step a1, determining the first execution tasks of the agents based on the target fitness value and the historical execution tasks;

[0073] Step a2, performing matching based on the task assignment rule and the first execution tasks of the agents to obtain a task rule result;

[0074] Step a3, when the task rule result indicates that the first execution tasks of the agents comply with the task assignment rule, updating the execution tasks corresponding to the agents based on the first execution tasks of the agents.

[0075] In a specific embodiment, the task rule result is used to indicate whether the first execution tasks of the agents comply with the task assignment rule; specifically, first determine the target fitness value of the historical execution task, compare the target fitness value of the historical execution task with the fitness values of other candidate tasks that have not been determined as historical execution tasks. If the target fitness value in the historical execution task is the largest, it indicates that the historical execution task is still the currently most suitable execution task for the corresponding agent, and use the historical execution task as the first execution task of the agents. If the target fitness value in the historical execution task is less than the target fitness value of any other candidate task, then reselect a new candidate task as the first execution task of the agents; further, on the basis of determining the first execution task, combine the task assignment rule to check whether the first execution task complies with the rule information in the task assignment rule. Correspondingly, match the first execution task with the task assignment rule to obtain a task rule matching result. Further, when the task rule result indicates that the first execution tasks of the agents comply with the task assignment rule, update the execution tasks corresponding to the agents based on the first execution tasks of the agents, that is, use the first execution tasks corresponding to the agents as the execution tasks corresponding to the agents. When the task rule result indicates that the first execution tasks of the agents do not comply with the task assignment rule, recalculate the target fitness value, and then update the execution task again.

[0076] In an alternative embodiment, before step 104, the method further includes:

[0077] Step b1, obtaining the updated execution tasks of the agents;

[0078] Step b2, performing matching on the updated execution tasks of the agents to obtain a matching result;

[0079] Step b3, when the matching result indicates that there are no identical tasks among the updated execution tasks of the agents, jump to the step of calculating the fitness value based on the task information and task matching information of the candidate tasks to obtain the target fitness value.

[0080] In a specific embodiment, the matching result is used to indicate whether there are identical tasks among the updated execution tasks of the agents. Specifically, after each update of the execution tasks corresponding to the agents, the updated execution tasks of the agents are obtained, and then the obtained updated execution tasks of the agents are matched, that is, it is compared whether the updated execution tasks corresponding to each agent are the same to obtain the matching result. Further, when the matching result indicates that there are no identical tasks among the updated execution tasks of the agents, jump to the step of calculating the fitness value based on the task information and task matching information of the candidate tasks to obtain the target fitness value.

[0081] In an alternative embodiment, before step 104, the method further includes:

[0082] Step c1, when the matching result indicates that there are identical tasks among the updated execution tasks of the agents, re-execute the step of updating the execution tasks corresponding to the agents based on the target fitness value and the historical execution tasks.

[0083] In a specific embodiment, when the matching result indicates that there are identical tasks among the updated execution tasks of the agents, re-update the execution tasks corresponding to the agents based on the previously obtained target fitness value and the historical execution tasks. Then, after re-updating the execution tasks, the updated execution tasks corresponding to the agents are matched again to determine the matching result.

[0084] In an alternative embodiment, the above step S104 further includes:

[0085] Step d1, obtain the preset number of updates;

[0086] Step d1, update the execution tasks corresponding to the agents based on the preset number of updates.

[0087] In a specific embodiment, the preset number of updates may be the number of times of calculating the target fitness value set in advance; optionally, the preset number of updates is obtained, and then based on the preset number of updates, the number of times of calculating the target fitness value is determined. Correspondingly, the number of times of calculating the target fitness value is iterated, and then based on the last calculation, the target fitness value is determined, that is, the target fitness value corresponding to the preset number of updates is determined. Further, based on the target fitness value calculated each time, the execution tasks corresponding to each agent are updated, and then the execution tasks corresponding to the target fitness value corresponding to the preset number of updates are determined, that is, the target execution tasks.

[0088] The task allocation method provided by the embodiments of the present invention obtains a set of agents, at least one candidate task corresponding to each agent in the set of agents, task information and task matching information of each candidate task; and uses the task matching information to indicate the matching degree between the candidate task and the corresponding agent, and reselects the execution task corresponding to each agent from at least one candidate task of each agent; based on the task information and task matching information of each candidate task, fitness calculation is performed to obtain a target fitness value; the calculation simplicity and competitiveness of determining the target fitness value are improved; based on the target fitness value and historical execution tasks, the execution tasks corresponding to each agent are updated, and then it jumps to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value; the iteration of target fitness value calculation and the iteration of execution task update are performed, realizing the dynamic adjustment of execution tasks, and thus realizing the high efficiency of task allocation; until the target fitness value no longer changes, based on the obtained target fitness value and the historical execution tasks, the target execution tasks of each agent are determined from the task set, improving the accuracy of determining the target execution tasks, and thus improving the task allocation ability, adaptability and efficiency of task allocation.

[0089] In this embodiment, a task allocation device is further provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" may be a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0090] This embodiment provides a task allocation device, as Figure 2 shown, including:

[0091] An acquisition module 201, configured to acquire a set of agents, at least one candidate task corresponding to each agent in the set of agents, task information and task matching information of each candidate task; the task matching information is used to indicate the matching degree between the candidate task and the corresponding agent;

[0092] A selection module 202, configured to select the execution task corresponding to each agent from at least one candidate task of each agent;

[0093] A calculation module 203, configured to perform fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value;

[0094] An update module 204, configured to update the execution task corresponding to each agent based on the target fitness value and the historical execution task, and jump to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value;

[0095] A determination module 205, configured to determine the target execution task of each agent from the task set based on the obtained target fitness value and the historical execution task until the target fitness value no longer changes.

[0096] In an optional embodiment, the task information includes task complexity, task requirement value, and task execution time; the above calculation module 203 includes:

[0097] A sorting unit, configured to perform task sorting on at least one candidate task corresponding to each agent based on the task complexity, task requirement value, and task execution time of each candidate task to obtain a task sorting result;

[0098] A determination unit, configured to determine the target fitness value based on the task sorting result and the task matching information.

[0099] In an optional embodiment, the above device further includes:

[0100] A rule acquisition unit, configured to acquire a task assignment rule;

[0101] A rule update unit, configured to update the execution task corresponding to each agent based on the target fitness value, the historical execution task, and the task assignment rule.

[0102] In an optional embodiment, the above update module 204 includes:

[0103] A first task determination unit, configured to determine the first execution task of each agent based on the target fitness value and the historical execution task;

[0104] A rule matching unit, configured to perform matching based on the task assignment rule and the first execution task of each agent to obtain a task rule result;

[0105] A first update unit, configured to update the execution tasks corresponding to the agents based on the first execution tasks of the agents when the task rule result indicates that the first execution tasks of the agents comply with the task assignment rule.

[0106] In an optional embodiment, before jumping to the step of calculating a target fitness value based on the task information and task matching information of the candidate tasks, the apparatus further includes:

[0107] An update acquisition unit, configured to acquire the updated execution tasks of the agents;

[0108] An update matching unit, configured to match the updated execution tasks of the agents to obtain a matching result;

[0109] An update jump unit, configured to jump to the step of calculating a target fitness value based on the task information and task matching information of the candidate tasks when the matching result indicates that there are no identical tasks among the updated execution tasks of the agents.

[0110] In an optional embodiment, the above apparatus further includes:

[0111] A re - execution unit, configured to re - execute the step of updating the execution tasks corresponding to the agents based on the target fitness value and the historical execution tasks when the matching result indicates that there are identical tasks among the updated execution tasks of the agents.

[0112] In an optional embodiment, the above apparatus further includes:

[0113] A times acquisition unit, configured to acquire a preset update times;

[0114] A times update unit, configured to update the execution tasks corresponding to the agents based on the preset update times.

[0115] The further function descriptions of the above - mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0116] The task assignment apparatus in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0117] The embodiment of the present invention further provides a computer device having the above - mentioned Figure 2 task assignment apparatus.

[0118] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As Figure 3 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 3 In

[0119] FIG. 1, a processor 10 is taken as an example.

[0120] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0121] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0122] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] The memory 20 can include volatile memory, such as random access memory; the memory can also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memory.

[0123] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected by a bus or other means. Figure 3 Taking the connection by the bus as an example.

[0124] The input device 30 can receive input digital or character information and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor), etc. The above display device includes, but is not limited to, a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0125] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented by downloading through a network from a remote storage medium or a non-transitory machine-readable storage medium and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0126] A part of the present invention can be applied as a computer program product, such as computer program instructions. When executed by a computer, through the operation of the computer, the methods and / or technical solutions according to the present invention can be called or provided. Those skilled in the art should be able to understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0127] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A task assignment method, characterized in that, The method includes: Obtaining a set of agents, at least one candidate task corresponding to each agent in the set of agents, task information and task matching information of each candidate task; the task matching information is used to indicate the matching degree between the candidate task and the corresponding agent; Selecting the execution task corresponding to each agent from at least one candidate task of each agent; Based on the task information and task matching information of each candidate task, performing fitness calculation to obtain a target fitness value; Based on the target fitness value and the historical execution tasks, updating the execution tasks corresponding to each agent, and jumping to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value; Until the target fitness value no longer changes, determining the target execution tasks of each agent from the task set based on the obtained target fitness value and the historical execution tasks.

2. The method according to claim 1, characterized in that The task information includes task complexity, task requirement value, and task execution time; the performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value includes: Based on the task complexity, task requirement value, and task execution time of each candidate task, performing task sorting on at least one candidate task corresponding to each agent to obtain a task sorting result; Based on the task sorting result and the task matching information, determining the target fitness value.

3. The method according to claim 1, wherein The method further includes: Obtaining a task assignment rule; Based on the target fitness value, the historical execution tasks, and the task assignment rule, updating the execution tasks corresponding to each agent.

4. The method according to claim 3, wherein The updating the execution tasks corresponding to each agent based on the target fitness value, the historical execution tasks, and the task assignment rule includes: Based on the target fitness value and the historical execution tasks, determining the first execution task of each agent; Based on the task assignment rule and the first execution task of each agent, performing matching to obtain a task rule result; In the case where the task rule result indicates that the first execution task of each agent conforms to the task assignment rule, updating the execution tasks corresponding to each agent based on the first execution task of each agent.

5. The method according to claim 1, wherein Before jumping to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value, the method further includes: Obtaining the updated execution tasks of each agent; Performing matching on the updated execution tasks of each agent to obtain a matching result; In the case where the matching result indicates that there are no identical tasks among the updated execution tasks of each agent, jumping to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value.

6. The method according to claim 5, wherein The method further includes: In the case where the matching result indicates that there are identical tasks among the updated execution tasks of each agent, re-executing the step of updating the execution tasks corresponding to each agent based on the target fitness value and the historical execution tasks.

7. The method according to claim 1, characterized in that The method further includes: Obtaining a preset number of updates; Update the execution tasks corresponding to the respective agents based on the preset number of updates.

8. A task allocation device, characterized in that, The device includes: An acquisition module, configured to acquire an agent set, at least one candidate task corresponding to each agent in the agent set, task information of each candidate task, and task matching information; the task matching information is used to indicate the matching degree between the candidate task and the corresponding agent; A selection module, configured to select the execution task corresponding to each agent from at least one candidate task of each agent; A calculation module, configured to perform fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value; An update module, configured to update the execution tasks corresponding to the respective agents based on the target fitness value and historical execution tasks, and jump to the step of performing fitness calculation based on the task information and task matching information of each candidate task to obtain a target fitness value; A determination module, configured to, until the target fitness value no longer changes, determine the target execution tasks of the respective agents from the task set based on the obtained target fitness value and the historical execution tasks.

9. A computer device, characterized in that, Including: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the task allocation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the task allocation method according to any one of claims 1 to 7.

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