Task allocation method and device based on man-machine fusion, equipment, medium and product

Abstract

The embodiment of the invention discloses a task allocation method and device based on man-machine fusion, equipment, a medium and a product, and the method comprises the steps: determining the concurrency condition of task requests in a first task allocation time period; determining a task processing load coefficient according to the concurrency condition and a preset maximum task processing concurrency amount of the first task processing object; and according to the task processing load coefficient and a preset task processing load threshold value, the task corresponding to the first proportion in the first task is determined to be allocated to a second task processing object, the first task processing object comprises a robot, and the second task processing object comprises a worker. According to the technical scheme provided by the embodiment of the invention, the problem that effective task allocation cannot be carried out on robots and workers at present is solved, the tasks with non-highest priorities can be allocated to the workers according to the concurrence condition of the task requests, the working pressure of the robots is relieved, the on-time completion rate of the tasks with high priorities is improved, and the task allocation efficiency is improved. And the overall task processing efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular, to a task allocation method, device, equipment, medium, and product based on human-machine integration. Background Art

[0002] In the scenario of robot cluster working services, an efficient scheduling and management background system is necessary. The system needs to uniformly control task requirements, with the goal of enabling the robot cluster to operate efficiently in the service scenario.

[0003] In this field, the current management system mainly controls robots and ignores the collaborative management of workers. In the case of high task concurrency, it cannot effectively allocate tasks to robots and workers. Summary of the Invention

[0004] The embodiments of the present invention provide a task allocation method, device, equipment, medium, and product based on human-machine integration. It can, according to the concurrency of task requests, allocate non-highest-priority tasks to workers, relieve the working pressure of robots, improve the on-time completion rate of high-priority tasks, and enhance the efficiency of overall task processing.

[0005] In a first aspect, the embodiments of the present invention provide a task allocation method based on human-machine integration. The method includes:

[0006] Determine the concurrency of task requests during a first task allocation period;

[0007] According to the concurrency and the preset maximum task processing concurrency of a first task processing object, determine the task processing load coefficient of the first task processing object;

[0008] According to the task processing load coefficient and a preset task processing load threshold, determine whether to allocate a first task in the task corresponding to the task request to a second task processing object, where the first task is a non-highest-priority task;

[0009] In the case where it is necessary to allocate the first task to the second task processing object, allocate a task corresponding to a first proportion in the first task to the second task processing object, where the first proportion is the proportion of the number of first tasks allocated to the second task processing object in all first tasks, and the first proportion is determined according to the task processing load coefficient;

[0010] Wherein, the first task processing object includes a robot, and the second task processing object includes a worker.

[0011] In a second aspect, the embodiments of the present invention provide a task allocation device based on human-machine integration. The device includes:

[0012] A concurrency situation determination module, configured to determine the concurrency situation of task requests during a first task allocation period;

[0013] A load factor determination module, configured to determine the task processing load factor of a first task processing object according to the concurrency situation and a preset maximum task processing concurrency of the first task processing object;

[0014] A task allocation judgment module, configured to determine whether to allocate a first task in the task corresponding to the task request to a second task processing object according to the task processing load factor and a preset task processing load threshold, where the first task is a non-highest priority task;

[0015] A task allocation module, configured to, when it is necessary to allocate the first task to the second task processing object, allocate a task corresponding to a first ratio in the first task to the second task processing object, where the first ratio is the ratio of the number of first tasks allocated to the second task processing object to all first tasks, and the first ratio is determined according to the task processing load factor;

[0016] Wherein, the first task processing object includes a robot, and the second task processing object includes a worker.

[0017] In a third aspect, an embodiment of the present invention further provides a computer device, which includes:

[0018] One or more processors;

[0019] A memory, configured to store one or more programs;

[0020] When the above one or more programs are executed by the one or more processors, the one or more processors implement the human-machine fusion-based task allocation method provided in any embodiment of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the human-machine fusion-based task allocation method provided in any embodiment of the present invention.

[0022] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the human-machine fusion-based task allocation method provided in any embodiment of the present invention.

[0023] The embodiments in the above invention have the following advantages or beneficial effects:

[0024] In an embodiment of the present invention, the concurrency of task requests in the first task allocation period is determined; according to the concurrency and the preset maximum task processing concurrency of the first task processing object, the task processing load coefficient of the first task processing object is determined; according to the task processing load coefficient and the preset task processing load threshold, it is determined whether the first task in the task corresponding to the task request needs to be allocated to the second task processing object, where the first task is a non-highest-priority task; in the case where the first task needs to be allocated to the second task processing object, the task corresponding to the first proportion in the first task is allocated to the second task processing object, where the first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load coefficient; where the first task processing object includes a robot and the second task processing object includes a worker. The technical solution of the embodiment of the present invention solves the problem that the current task cannot be effectively allocated between the robot and the worker. By allocating non-highest-priority tasks to the worker according to the concurrency of task requests, the working pressure of the robot can be reduced, the on-time completion rate of high-priority tasks can be improved, and the overall task processing efficiency can be increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flowchart of a task allocation method based on human-machine integration provided by an embodiment of the present invention;

[0026] Figure 2 is a flowchart of a task allocation method based on human-machine integration provided by an embodiment of the present invention;

[0027] Figure 3 is a flowchart of a task allocation method based on human-machine integration provided by an embodiment of the present invention;

[0028] Figure 4 is a schematic diagram of task handover provided by an embodiment of the present invention;

[0029] Figure 5 is a schematic structural diagram of a task allocation device based on human-machine integration provided by an embodiment of the present invention;

[0030] Figure 6 is a schematic structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that, for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0032] Figure 1 The figure is a flowchart of a task allocation method based on human-machine integration provided by an embodiment of the present invention. This embodiment is applicable to the scenario of task allocation. This method can be executed by a task allocation device based on human-machine integration, and this device can be implemented in a software and / or hardware manner and integrated into a computer device with application development functions.

[0033] As Figure 1 shown, the task allocation method based on human-machine integration in this embodiment includes the following steps:

[0034] S110. Determine the concurrency of task requests in the first task allocation period.

[0035] The first task allocation period can be any continuous time period in the task allocation of the operation system. The operation system is a system that receives task requests and allocates tasks to task execution objects according to the task requests. In this embodiment, the task requests can include task requests such as logistics distribution, patient education course explanation, and cleaning tasks. Of course, they can also be task requests for other tasks that can be executed by both robots and workers. This embodiment does not make any limitations in this regard. The concurrency of task requests refers to the situation where at least two task requests arrive at the operation system at the same time in the first task allocation period. Specifically, it can be the moment when task requests are concurrent in the first task allocation period, and the concurrency volume corresponding to each concurrent moment.

[0036] S120. Determine the task processing load coefficient of the first task processing object according to the concurrency situation and the preset maximum task processing concurrency of the first task processing object.

[0037] The first task processing object includes robots, specifically a robot cluster. According to the moment when task requests are concurrent in the first task allocation period, and the concurrency volume corresponding to each concurrent moment, determine the task processing load coefficient of the first task processing object according to the ratio of the concurrency volume to the preset maximum task processing concurrency of the first task processing object. The task processing load coefficient can characterize the load degree of the robot at each moment when task requests are concurrent, so as to determine whether to allocate non-highest-priority tasks to workers according to the load degree of the robot in the subsequent process, thereby ensuring the stable operation of the highest-priority tasks.

[0038] S130. Determine whether it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load coefficient and the preset task processing load threshold.

[0039] The first task is a non - highest - priority task, and the second task processing object includes workers. Usually, the system assigns tasks of all priorities to the first task processing object because of the convenience and efficiency of the first task processing object in executing tasks. However, in the case of high - concurrency task requests, some non - highest - priority tasks need to be assigned to the second task processing object to relieve the task - processing pressure on the first task processing object and ensure the stable operation of the highest - priority tasks.

[0040] Therefore, in this embodiment, by determining the task - processing load situation of the entire first - task allocation period according to the statistical index value of the task - processing load coefficient at the concurrent moment of all task requests, and then determining whether there is an overload situation of the first task processing object under the task - request concurrency in the first - task allocation period according to the preset statistical index value and the preset task - processing load threshold. Thus, when there is an overload, the first task in the tasks corresponding to the task requests is assigned to the second task processing object. The preset task - processing load threshold can be the maximum concurrent amount that the first task processing object can bear.

[0041] The statistical index value is the index value corresponding to the preset statistical index. For example, it can include the average value, median, etc. The average value can include the arithmetic average value and geometric average value, etc. This embodiment does not limit the type of the preset statistical index, and can be selected according to actual needs.

[0042] S140. In the case where the first task needs to be assigned to the second task processing object, assign the tasks corresponding to the first proportion in the first task to the second task processing object.

[0043] The first ratio is the proportion of the number of the first tasks assigned to the second task processing object to all the first tasks, and the first ratio is determined according to the task processing load factor. For example, the larger the statistical index value of the task processing load factor, the greater the degree of overload of the robot, and the more the number of the first tasks that need to be assigned to the worker. On the contrary, the smaller the statistical index value of the task processing load factor, the smaller the degree of overload of the robot, and the fewer the number of the first tasks that need to be assigned to the worker. A straight-line function with a positive slope can be used. Taking the average value of the task processing load factor as the independent variable of the straight-line function, the obtained function value as the numerator of the first ratio, and 100 as the denominator of the first ratio, the first ratio can be obtained. The tasks corresponding to the first ratio in the first tasks are assigned to the second task processing object. After the tasks corresponding to the first ratio in the first tasks are assigned to the second task processing object, the tasks corresponding to the remaining second ratio are assigned to the first task processing object, and the sum of the second ratio and the first ratio is 1. And the second tasks in the tasks corresponding to the task request are assigned to the first task processing object. The second task is the highest-priority task. The task assignment can be to send the task assignment information to the client of the first task processing object or the second task processing object.

[0044] The technical solution of this embodiment determines the concurrency of task requests in the first task assignment period; according to the concurrency and the preset maximum task processing concurrency of the first task processing object, determines the task processing load factor of the first task processing object; according to the task processing load factor and the preset task processing load threshold, determines whether it is necessary to assign the first tasks in the tasks corresponding to the task request to the second task processing object, where the first task is a non-highest-priority task; in the case where it is necessary to assign the first tasks to the second task processing object, assigns the tasks corresponding to the first ratio in the first tasks to the second task processing object, where the first ratio is the proportion of the number of the first tasks assigned to the second task processing object to all the first tasks, and the first ratio is determined according to the task processing load factor; where the first task processing object includes a robot, and the second task processing object includes a worker. The technical solution of the embodiment of the present invention solves the problem that the current task assignment for robots and workers cannot be effectively carried out. By according to the concurrency of task requests, non-highest-priority tasks can be assigned to workers, reducing the working pressure of the robot, improving the on-time completion rate of high-priority tasks, and enhancing the efficiency of overall task processing.

[0045] Figure 2The flowchart of a task allocation method based on human-machine fusion provided by an embodiment of the present invention. This embodiment and the task allocation method based on human-machine fusion in the above embodiment belong to the same inventive concept, and further describes the process of determining the task processing load coefficient. This method can be executed by a task allocation device based on human-machine fusion, and this device can be implemented in a software and / or hardware manner and integrated into a computer device with application development functions.

[0046] As Figure 2 shown, the task allocation method based on human-machine fusion in this embodiment includes the following steps:

[0047] S210. Determine the concurrency of task requests in the first task allocation period.

[0048] In different task allocation periods, the priority settings of task requests, that is, the priorities of tasks, can be different. According to the first task allocation period and the preset priority and period mapping relationship, determine the priorities of tasks in the first task allocation period.

[0049] Suppose in the first task allocation period T, the highest priority task is HQ, and the non-highest priority task, that is, the low priority task, is LQ. For example, if the priority increases gradually from 1 to 5, the highest priority task is the task corresponding to priority 5, and the non-highest priority tasks are the tasks corresponding to priorities 1, 2, 3, and 4. Of course, it can also be set that the priorities of the top preset number of digits, such as the top n digits, are the highest priorities, and the preset number of digits can be set according to actual needs. When n is 2, the highest priority tasks are the tasks corresponding to priorities 4 and 5.

[0050] In this embodiment, the concurrency of task requests can be the sum value of the concurrency of the highest priority task requests and the non-highest priority task requests at each moment.

[0051] S220. Calculate the ratio of the concurrency at the task request concurrency moment in the concurrency situation to the preset maximum task processing concurrency of the first task processing object.

[0052] When at least two task requests arrive at the operation system at the same moment, this moment is taken as the task request concurrency moment.

[0053] In any task request concurrency moment, the number of the highest priority task HQ corresponding to the highest priority task request is QPS-hq, the number of the non-highest priority task, that is, the first task LQ, corresponding to the non-highest priority task request is QPS-lq, and the concurrency is (QPS-hq + QPS-lq). And the first task processing object, that is, the robot cluster, can bear the maximum concurrency, which is the preset maximum task processing concurrency QPS-robot.

[0054] Calculate the ratio of the concurrency and the preset maximum task processing concurrency as p = (QPS - hq + QPS - lq) / QPS - robot * 100%.

[0055] S230. Determine the ratio as the task processing load coefficient of the first task processing object at the task request concurrency moment.

[0056] Take the ratio p as the task processing load coefficient of the first task processing object at the task request concurrency moment. Perform the calculation described in step S220 for all task request concurrency moments to obtain the task processing load coefficient p corresponding to all task request concurrency moments.

[0057] S240. Calculate the average value of the task processing load coefficients of all task request concurrency moments in the first task allocation period. If the average value is greater than the preset task processing load threshold, determine that it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object.

[0058] Among them, the first task is a non - highest - priority task.

[0059] The first task processing object includes a robot, and the second task processing object includes a worker.

[0060] Exemplarily, calculate the average value of the task processing load coefficients of all task request concurrency moments in the first task allocation period according to the sum of the task processing load coefficients of all task request concurrency moments in the first task allocation period and the number of all task request concurrency moments.

[0061] The preset task processing load threshold can be set according to actual needs. When the preset task processing load threshold is 80%, when the average value is greater than 80%, the system determines that it is necessary to allocate a certain proportion of LQ tasks to workers.

[0062] S250. When it is necessary to allocate the first task to the second task processing object, allocate the tasks corresponding to the first proportion in the first task to the second task processing object.

[0063] The first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load coefficient.

[0064] In an alternative implementation manner, the determination process of the first proportion includes:

[0065] Substitute the average value of the task processing load coefficient into the preset linear function to obtain the linear function value;

[0066] Determine the first proportion according to the linear function value.

[0067] The preset linear function can be a linear function of one variable with a positive slope. Moreover, when the average value is equal to the preset task processing load threshold, the function value of the preset linear function, that is, the first ratio, is 0, and no first task is assigned to the worker. When the average value is equal to 100%, the first ratio is 100%, and all first tasks are assigned to the worker. It can be understood that when the first ratio exceeds 100%, all first tasks are still assigned to the worker.

[0068] For example, the linear function can be f(x) = 500x - 400.

[0069] During the first task allocation period △t, if the average value of p is 85%, then 25% of the task requests of LQ are dispatched to the worker. If the average value of p is 100%, then 100% of the task requests of LQ are dispatched to the worker.

[0070] For example, in the second task allocation period after the first task allocation period, if the average value of QPS-hq + QPS-lq is smaller than that in the first task allocation period, the average value of p is correspondingly smaller, and the first ratio calculated according to the preset linear function is smaller, then the first tasks assigned to the worker will decrease, and the first tasks assigned to the robot will increase. In summary, in any task allocation period, ensure that the HQ of the period is given to the maximum robot production capacity output and a smooth transition during the peak period.

[0071] In an alternative embodiment, if all the robots in the robot cluster, that is, 100% * QPS-robot, which are the first task processing objects, are assigned the highest-priority task HQ, and the number of unassigned highest-priority tasks reaches the preset task backlog threshold Threshold-hq, then the number of highest-priority tasks equal to the difference between the number of unassigned highest-priority tasks and Threshold-hq is assigned to the worker to ensure that tasks do not accumulate abnormally.

[0072] Through the collaborative task allocation between the robot and the worker in this embodiment, the average task completion time can be shortened by about 30%, the on-time completion rate of the highest-priority tasks > 99%, and the system exception ratio < 0.1%.

[0073] The technical solution of this embodiment is to determine the concurrency of task requests in the first task allocation period; calculate the ratio of the concurrency at the concurrent moment of task requests in the concurrency situation to the preset maximum task processing concurrency of the first task processing object; determine the task processing load coefficient of the first task processing object at the concurrent moment of task requests; calculate the average value of the task processing load coefficients at the concurrent moments of all task requests in the first task allocation period, and determine that it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object when the average value is greater than the preset task processing load threshold; where the first task is a non-highest priority task; when it is necessary to allocate the first task to the second task processing object, allocate the task corresponding to the first ratio in the first task to the second task processing object; where the first ratio is the ratio of the number of the first tasks allocated to the second task processing object to all the first tasks, and the first ratio is determined according to the task processing load coefficient; where the first task processing object includes a robot, and the second task processing object includes a worker. The technical solution of the embodiment of the present invention solves the problem that the current task allocation between robots and workers cannot be effectively carried out. It can allocate a certain proportion of non-highest priority tasks to workers according to the concurrency of task requests and the task processing load coefficient of the robot. The allocation method is flexible, reduces the working pressure of the robot, improves the on-time completion rate of high-priority tasks, and improves the overall task processing efficiency.

[0074] Figure 3 The flowchart of a task allocation method based on human-machine integration provided by an embodiment of the present invention. This embodiment and the task allocation method based on human-machine integration in the above embodiment belong to the same inventive concept, and further describe the process of task handover. This method can be executed by a task allocation device based on human-machine integration, and this device can be implemented in a software and / or hardware manner and integrated in a computer device with application development functions.

[0075] As Figure 3 shown, the task allocation method based on human-machine integration in this embodiment includes the following steps:

[0076] S310. Determine the concurrency of task requests in the first task allocation period.

[0077] S320. Determine the task processing load coefficient of the first task processing object according to the concurrency situation and the preset maximum task processing concurrency of the first task processing object.

[0078] S330. Determine whether it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load coefficient and the preset task processing load threshold.

[0079] Wherein, the first task is a non-highest priority task.

[0080] S340. When it is necessary to allocate the first task to the second task processing object, allocate the task corresponding to the first ratio in the first task to the second task processing object.

[0081] The first ratio is the ratio of the number of the first tasks allocated to the second task processing object to all the first tasks, and the first ratio is determined according to the task processing load factor.

[0082] The first task processing object includes robots, and the second task processing object includes workers.

[0083] S350. When a task handover request is obtained, determine the initiating object of the task handover request.

[0084] The task handover request can be automatically sent by the robot client according to the preset handover request sending conditions during the execution of the task by the robot or the worker after the task is allocated, or sent by the worker to the operation system server through the client of the worker's handheld terminal. The task handover request can be automatically sent when the robot has an abnormality, and the worker can send the task handover request according to the actual situation of their own task execution. The task handover request needs to include the task information of the task to be handed over, and can include the identifier of the initiating object, such as the worker number, name or robot number of the worker.

[0085] S360. Determine the task information of the task to be handed over according to the task handover request, and generate handover task allocation information according to the task information, where the task information includes at least the task type and the task location.

[0086] The task handover request contains the task information of the task to be handed over. Analyze and integrate the task information, etc., to generate handover task allocation information, where the task information includes at least the task type and the task location. Analyze the task information, for example, determine the remaining task content according to the completion situation at the time of task handover. The remaining task content is, for example, the remaining distance of the logistics distribution task, the remaining courses of the course explanation task, etc. The task information includes at least the task type and the task location, so as to ensure the realization of the handover. The task type can include logistics distribution, inspection, cleaning, and course explanation, etc.

[0087] S370. When the initiating object is the first task processing object, determine whether there is a first task processing object of the same type in the idle state. If so, determine the target handover object among the first task processing objects of the same type. If not, determine the target handover object among the second task processing objects.

[0088] When the initiating object is a robot, determine whether there is a robot of the same type in an idle state. If there is, determine the target handover object among the robots of the same type. If not, determine the target handover object among the workers.

[0089] S380. When the initiating object is the second task processing object, determine the target handover object among the first task processing objects.

[0090] When the initiating object is a worker, determine the target handover object in the robot cluster.

[0091] S390. Send the handover task assignment information to the client corresponding to the target handover object.

[0092] As Figure 4 shown, the robot client sends a handover request to the server. The server generates the handover task assignment information, that is, the Figure 4 order information in, and sends the handover task assignment information to the client corresponding to the handover worker.

[0093] In an alternative embodiment, after sending the handover task assignment information to the client corresponding to the target handover object, when the target handover object is the second task processing object, obtain the identity authentication information sent by the second task processing object; authenticate the identity of the second task processing object according to the identity authentication information. When the identity authentication is passed, send a work control instruction associated with the task information of the task to be handed over to the initiating object.

[0094] As Figure 4 shown, the worker for the handover task goes to the robot's location after receiving the order and sends identity authentication information to the system to verify the permission. When the identity authentication is passed, send a work control instruction associated with the task information of the task to be handed over to the initiating object. The work control instruction can be to open the cabinet door in a logistics distribution scenario and can be specifically set according to actual needs. After the task is completed, the worker can report the task completion status to the server background. All information in the handover process, including the task completion status, handover task assignment information, handover request sending time node, handover time node, worker arrival time node at the task location, handover information reported at the handover time node (such as on-site photo information), and the identity information of the worker and robot involved in the handover, can be recorded in the server background to ensure the traceability of the handover process.

[0095] In an alternative embodiment, obtain the task node status data sent by the initiating object and the target handover object during the task handover process; store the task node status data and the handover task assignment information in an associated manner.

[0096] The task node status data sent by the initiating object and the target handover object may include task process status data, such as the time nodes of the task process and the current situation of task completion, etc.

[0097] When the task execution object executes any task including the handover task, it can receive the task assignment information generated by the system server and report the task node status data during the task execution process. The back-end management personnel can achieve data traceability based on the task node status data, handover task assignment information, human-machine handover data, etc., such as data on handover time and location, task information (such as task content), handover personnel / robot information, etc.

[0098] Workers can obtain task assignment information and report process status through a handheld terminal. The management background records the whole-process data, the system decision-making process data, the dispatching data of similar human employees, and the handover data between robots and humans can be queried and traced.

[0099] In a specific task handover example, when a certain robot has an abnormal service or is about to time out during the execution of a logistics distribution service, it automatically sends a task handover request. The system assigns a worker to the coordinates of the robot executing the task. After passing the identity verification, the worker takes out the distribution items from the robot and converts the business to manual distribution, thus ensuring the effective completion of the task as required. Another example is the disinfection task. The system assigns a worker to the coordinates of the robot's disinfection function module to perform manual disinfection to ensure the completion of the task as required. The robot that puts down the disinfection function module can also go to perform other tasks.

[0100] In another specific task handover example, when a worker cannot complete the current task due to fatigue or interference, the worker can send a task handover request to call the system to dispatch a robot to take over and continue to complete the task. The robot will go to the coordinates of the worker. The worker authorizes the handover of the work, and the subsequent work will be completed by the robot, thus ensuring the completion of the task as required, and the handover process data is retained by the system for traceability. For example, in a logistics task, the worker puts the items into the robot, and the robot continues to distribute. Another example is the patient education course task, which is continued to be explained to the patient by the robot.

[0101] In a specific task assignment instance, during each stage of the robot's task execution, if worker cooperation is required, the system can send worker task collaboration information in advance to notify the workers to get ready, reducing the waiting time for robot interaction and handover processes and improving the overall execution efficiency. In scenarios where some tasks require human-robot collaboration, the system simultaneously issues task assignment information corresponding to the task content to both the robot and the workers, enabling the workers to prepare in advance for cooperation with the robot, improving the overall work efficiency and forming an efficient collaborative relationship between humans and robots. For example, when the robot needs worker cooperation to execute a task, the system will notify the workers in advance to get ready. For tasks such as hospital specimen delivery, the system will notify the workers to complete blood and urine collection before the specified time so that the robot can load the goods quickly upon arrival, reducing the waiting time of the process. Records of situations where the robot is waiting due to the workers' failure to complete the task on time are determined, and the workers are performance-managed based on the records. The system uniformly manages the robot and the workers to improve efficiency.

[0102] The technical solution of this embodiment determines the concurrency of task requests during the first task allocation period; determines the task processing load coefficient of the first task processing object according to the concurrency and the preset maximum task processing concurrency of the first task processing object; determines whether to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load coefficient and the preset task processing load threshold; where the first task is a non-highest priority task; in the case where the first task needs to be allocated to the second task processing object, allocate the task corresponding to the first proportion in the first task to the second task processing object; the first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load coefficient; the first task processing object includes a robot, and the second task processing object includes a worker; in the case of obtaining a task handover request, determine the initiating object of the task handover request; determine the task information of the task to be handed over according to the task handover request, and generate handover task allocation information according to the task information, where the task information at least includes the task type and the task location; in the case where the initiating object is the first task processing object, determine whether there is a first task processing object of the same type in the idle state, if so, determine the target handover object among the first task processing objects of the same type, if not, determine the target handover object among the second task processing objects; and, in the case where the initiating object is the second task processing object, determine the target handover object among the first task processing objects; send the handover task allocation information to the client corresponding to the target handover object. The technical solution of the embodiment of the present invention solves the problem that the current task cannot be effectively allocated between the robot and the worker. It can allocate non-highest priority tasks to the worker according to the concurrency of task requests, relieve the working pressure of the robot, improve the on-time completion rate of high-priority tasks, and improve the efficiency of overall task processing. And through a perfect handover process, it can effectively handle the situation where the robot has an abnormality or the worker cannot complete the task during task execution, and improve the stability of task execution.

[0103] Figure 5 FIG. is a schematic structural diagram of a task allocation device based on human-machine integration provided by an embodiment of the present invention. This embodiment is applicable to the scenario of task allocation. The task allocation device based on human-machine integration can be implemented in a software and / or hardware manner and integrated into a computer terminal device with application development functions.

[0104] As Figure 5 shown, the task allocation device based on human-machine integration includes: a concurrency determination module 410, a load coefficient determination module 420, a task allocation judgment module 430, and a task allocation module 440.

[0105] Among them, the concurrency situation determination module 410 is used to determine the concurrency situation of task requests in the first task allocation period; the load factor determination module 420 is used to determine the task processing load factor of the first task processing object according to the concurrency situation and the preset maximum task processing concurrency of the first task processing object; the task allocation judgment module 430 is used to determine whether it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load factor and the preset task processing load threshold, where the first task is a non-highest priority task; the task allocation module 440 is used to allocate the tasks corresponding to the first proportion in the first task to the second task processing object when it is necessary to allocate the first task to the second task processing object, where the first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load factor; among them, the first task processing object includes robots, and the second task processing object includes workers.

[0106] The technical solution of this embodiment determines the concurrency situation of task requests in the first task allocation period; determines the task processing load factor of the first task processing object according to the concurrency situation and the preset maximum task processing concurrency of the first task processing object; determines whether it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load factor and the preset task processing load threshold, where the first task is a non-highest priority task; when it is necessary to allocate the first task to the second task processing object, allocates the tasks corresponding to the first proportion in the first task to the second task processing object, where the first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load factor; among them, the first task processing object includes robots, and the second task processing object includes workers. The technical solution of the embodiment of the present invention solves the problem that currently, effective task allocation cannot be performed on robots and workers. It can allocate non-highest priority tasks to workers according to the concurrency situation of task requests, relieve the work pressure of robots, improve the on-time completion rate of high-priority tasks, and improve the efficiency of overall task processing.

[0107] In an optional implementation manner, the load factor determination module 420 is specifically used for:

[0108] Calculate the ratio of the concurrency of task request concurrency moments in the concurrency situation to the preset maximum task processing concurrency of the first task processing object; determine the ratio as the task processing load factor of the first task processing object at the task request concurrency moment.

[0109] In an optional implementation manner, the task allocation judgment module 430 is specifically used for:

[0110] Calculate the average value of the task processing load coefficients at the concurrent moments of all task requests in the first task allocation period. When the average value is greater than the preset task processing load threshold, determine that the first task among the tasks corresponding to the task requests needs to be allocated to the second task processing object.

[0111] In an alternative implementation, the task allocation module 440 is further configured to:

[0112] Substitute the average value of the task processing load coefficients into a preset linear function to obtain a linear function value;

[0113] Determine a first ratio according to the linear function value.

[0114] In an alternative implementation, the device further includes:

[0115] A task handover module, configured to, when a task handover request is obtained, determine the initiating object of the task handover request; determine the task information of the task to be handed over according to the task handover request, and generate handover task allocation information according to the task information, where the task information at least includes the task type and the task location; when the initiating object is the first task processing object, determine whether there is a first task processing object of the same type in an idle state. If so, determine a target handover object among the first task processing objects of the same type. If not, determine a target handover object among the second task processing objects; and, when the initiating object is the second task processing object, determine a target handover object among the first task processing objects; send the handover task allocation information to the client corresponding to the target handover object.

[0116] In an alternative implementation, the task handover module is further configured to:

[0117] When the target handover object is the second task processing object, obtain the identity authentication information sent by the second task processing object; authenticate the identity of the second task processing object according to the identity authentication information, and when the identity authentication is passed, send a work control instruction associated with the task information of the task to be handed over to the initiating object.

[0118] In an alternative implementation, the task handover module is further configured to:

[0119] Obtain the task node status data sent by the initiating object and the target handover object during the task handover process; associate and store the task node status data and the handover task allocation information.

[0120] The task allocation device based on human-machine integration provided by the embodiments of the present invention can execute the task allocation method based on human-machine integration provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0121] Figure 6 A structural schematic diagram of a computer device provided by an embodiment of the present invention. Figure 6 A block diagram of an exemplary computer device 12 suitable for implementing the embodiments of the present invention is shown. Figure 6 The computer device 12 shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention. The computer device 12 can be any terminal device with computing capabilities, such as intelligent controllers, servers, mobile phones and other terminal devices.

[0122] Such as Figure 6 As shown, the computer device 12 is presented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0123] The bus 18 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus structures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0124] The computer device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0125] The system memory 28 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 34 can be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, commonly referred to as a "hard disk drive"). Although Figure 6Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical medium) can be provided. In these cases, each drive can be connected to the bus 18 through one or more data medium interfaces. The system memory 28 can include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0126] A program / utility 40 having a set (at least one) of program modules 42 can be stored, for example, in the system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 42 generally perform the functions and / or methods in the embodiments described in the present invention.

[0127] The computer device 12 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the computer device 12, and / or communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Moreover, the computer device 12 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the computer device 12 through the bus 18. It should be understood that although Figure 6 not shown in the figure, other hardware and / or software modules can be used in combination with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0128] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, for example, implementing the human-machine fusion-based task allocation method provided by the embodiments of the present invention. The method includes:

[0129] Determine the concurrency of task requests in the first task allocation period;

[0130] According to the concurrency and the preset maximum task processing concurrency of the first task processing object, determine the task processing load coefficient of the first task processing object;

[0131] Determine whether it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load coefficient and the preset task processing load threshold, where the first task is a non-highest-priority task;

[0132] In the case where it is necessary to allocate the first task to the second task processing object, allocate the task corresponding to the first proportion in the first task to the second task processing object, where the first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load coefficient;

[0133] Wherein, the first task processing object includes a robot, and the second task processing object includes a worker.

[0134] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the task allocation method based on human-machine fusion provided in any embodiment of the present invention, and the method includes:

[0135] Determine the concurrency of task requests in the first task allocation period;

[0136] According to the concurrency and the preset maximum task processing concurrency of the first task processing object, determine the task processing load coefficient of the first task processing object;

[0137] Determine whether it is necessary to allocate the first task in the task corresponding to the task request to the second task processing object according to the task processing load coefficient and the preset task processing load threshold, where the first task is a non-highest-priority task;

[0138] In the case where it is necessary to allocate the first task to the second task processing object, allocate the task corresponding to the first proportion in the first task to the second task processing object, where the first proportion is the proportion of the number of the first tasks allocated to the second task processing object in all the first tasks, and the first proportion is determined according to the task processing load coefficient;

[0139] Wherein, the first task processing object includes a robot, and the second task processing object includes a worker.

[0140] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, device, or component.

[0141] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable medium other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0142] The program codes contained on the computer-readable media may be transmitted by any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0143] The computer program codes for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, Python, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0144] An embodiment of the present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the task allocation method based on human-machine integration provided in any embodiment of the present application.

[0145] In the process of implementing the computer program product, computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, Python, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, alternatively, can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0146] Those of ordinary skill in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Optionally, they can be implemented with program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. Thus, the present invention is not limited to any specific combination of hardware and software.

[0147] Note that the above is only the preferred embodiment of the present invention and the applied technical principle. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A task allocation method based on human-machine fusion, characterized in that: include: Determine the concurrency of task requests in the first task allocation period; Determine a task processing load factor of the first task processing object according to the concurrency situation and a preset maximum task processing concurrency of the first task processing object; Determining whether it is necessary to allocate a first task among the tasks corresponding to the task request to a second task processing object according to the task processing load coefficient and a preset task processing load threshold, wherein the first task is a non-highest priority task; In the case where the first task needs to be allocated to the second task processing object, tasks corresponding to a first proportion of the first tasks are allocated to the second task processing object, wherein the first proportion is a proportion of the number of the first tasks allocated to the second task processing object to all the first tasks, and the first proportion is determined according to the task processing load coefficient; Among them, the first task processing object includes a robot, and the second task processing object includes a worker.

2. The method according to claim 1, characterized in that: The determining, according to the concurrency situation and the preset maximum task processing concurrency of the first task processing object, the task processing load coefficient of the first task processing object includes: Calculate the ratio of the concurrency of the task request concurrency moment in the concurrency situation to the preset maximum task processing concurrency of the first task processing object; The ratio is determined as the task processing load coefficient of the first task processing object at the time of concurrent task requests.

3. The method according to claim 2, characterized in that The determining, according to the task processing load coefficient and a preset task processing load threshold, whether it is necessary to allocate the first task in the tasks corresponding to the task request to the second task processing object includes: Calculate the average value of the task processing load coefficients of all the concurrent moments of the task requests in the first task allocation period, and when the average value is greater than the preset task processing load threshold, determine that the first task among the tasks corresponding to the task request needs to be allocated to the second task processing object.

4. The method according to claim 3, characterized in that The process of determining the first ratio includes: Substituting the average value of the task processing load coefficient into a preset straight line function to obtain a straight line function value; The first ratio is determined according to the straight line function value.

5. The method according to claim 1, characterized in that The method also includes: In the case of obtaining a task handover request, determining an initiator of the task handover request; Determine task information of the task to be handed over according to the task handover request, and generate handover task allocation information according to the task information, wherein the task information at least includes task type and task location; In the case where the initiating object is a first task processing object, determining whether there is an idle first task processing object of the same type as the initiating object, and if so, determining a target handover object in the first task processing object of the same type, and if not, determining the target handover object in the second task processing object; and, In a case where the initiating object is a second task processing object, determining the target handover object in the first task processing object; The handover task allocation information is sent to the client corresponding to the target handover object.

6. The method according to claim 5, characterized in that After sending the handover task allocation information to the client corresponding to the target handover object, the method further includes: When the target handover object is the second task processing object, obtaining identity authentication information sent by the second task processing object; The second task processing object is authenticated according to the identity authentication information, and when the identity authentication passes, a work control instruction associated with the task information of the task to be handed over is sent to the initiating object.

7. The method according to claim 6, characterized in that The method also includes: Acquire task node status data sent by the initiating object and the target handover object during the task handover process; The task node status data and the handover task allocation information are associated and stored.

8. A task allocation device based on human-machine fusion, characterized in that: include: A concurrency determination module, used to determine the concurrency of task requests in the first task allocation period; A load coefficient determination module, used to determine the task processing load coefficient of the first task processing object according to the concurrency situation and the preset maximum task processing concurrency of the first task processing object; A task allocation judgment module, used for determining whether it is necessary to allocate a first task among the tasks corresponding to the task request to a second task processing object according to the task processing load coefficient and a preset task processing load threshold, wherein the first task is a non-highest priority task; a task allocation module, configured to allocate tasks corresponding to a first proportion of the first tasks to the second task processing object when the first tasks need to be allocated to the second task processing object, wherein the first proportion is a proportion of the number of the first tasks allocated to the second task processing object to all the first tasks, and the first proportion is determined according to the task processing load coefficient; Among them, the first task processing object includes a robot, and the second task processing object includes a worker.

9. A computer device, characterized in that: The computer device comprises: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the task allocation method based on human-machine fusion as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the task allocation method based on human-machine fusion as described in any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the task allocation method based on human-machine integration as described in any one of claims 1 to 7.

Images