Task allocation method and device, electronic equipment and readable storage medium

Abstract

The invention discloses a task allocation method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining task information of a to-be-allocated task and target data of each employee in a target employee group, and the target data comprises historical task completion data and personal information data; for each employee, based on the target data of the employee, determining a feature image corresponding to the employee, the feature image being used for representing features of historical processing tasks of the employee; and based on the task information and the feature image corresponding to each employee, determining an allocated employee corresponding to the to-be-allocated task.

Description

Technical Field

[0001] This application relates to the field of task allocation, and in particular, to a task allocation method, apparatus, electronic device, and readable storage medium. Background Art

[0002] An office automation system (OA system) widely, comprehensively, and rapidly collects, organizes, processes, stores, and uses information by adopting a series of modern office devices and advanced communication technologies, enabling internal personnel of an enterprise to conveniently and quickly share information and efficiently collaborate on tasks, thereby achieving the purpose of improving administrative efficiency.

[0003] Although the OA system can facilitate the business operation process of employees, when allocating tasks on the OA system, the person responsible for allocation still needs to determine the employees who will handle the tasks by themselves and select the corresponding employees from multiple employee options to manually click on the options for task allocation, resulting in low task allocation efficiency. Summary of the Invention

[0004] This application discloses a task allocation method, apparatus, electronic device, and readable storage medium, which can improve the efficiency of task allocation.

[0005] To solve the above problems, this application adopts the following technical solutions: In a first aspect, an embodiment of this application discloses a task allocation method, including: obtaining task information of a task to be allocated and target data of each employee in a target employee group, where the target data includes historical task completion data and personal information data; for each employee, determining a feature image corresponding to the employee based on the target data of the employee, where the feature image is used to characterize the features of the tasks historically processed by the employee; and determining an allocated employee corresponding to the task to be allocated based on the task information and the feature images corresponding to the respective employees.

[0006] In a second aspect, an embodiment of this application discloses a task allocation apparatus, including: an obtaining module, configured to obtain task information of a task to be allocated and target data of each employee in a target employee group, where the target data includes historical task completion data and personal information data; a determining module, configured to determine, for each employee, a feature image corresponding to the employee based on the target data of the employee, where the feature image is used to characterize the features of the tasks historically processed by the employee; and the determining module is further configured to determine an allocated employee corresponding to the task to be allocated based on the task information and the feature images corresponding to the respective employees.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory. The memory stores a program or instructions that can run on the processor. When the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instructions are stored. When the program or instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute: the steps of the method described in the first aspect.

[0010] The technical solution adopted by the present application can achieve the following beneficial effects: An embodiment of the present application provides a task allocation method. By obtaining the task information of the task to be allocated and the target data of each employee in the target employee group, where the target data includes historical task completion data and personal information data, for each employee, based on the employee's target data, a characteristic image corresponding to the employee is determined, and the characteristic image is used to characterize the characteristics of the employee's historical processing tasks. Then, based on the task information of the task to be allocated and the characteristic images corresponding to each employee, the allocated employee corresponding to the task to be allocated is determined. Since the task allocation method of the present application does not require manual operation by the allocator, the allocated employee of the task to be allocated can be automatically determined, and the efficiency of task allocation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic flowchart of a task allocation method disclosed in an embodiment of the present application; Figure 2 It is a flowchart of generating an employee characteristic image disclosed in an embodiment of the present application; Figure 3 It is a schematic structural diagram of a task allocation device disclosed in an embodiment of the present application; Figure 4 It is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application belong to the scope of protection of the present application.

[0013] The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances, so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same category, and do not limit the number of objects. For example, the first object can be one or multiple. In addition, "and / or" in the description and claims means at least one of the objects connected by electricity, and the character " / ", generally represents an "or" relationship between the associated objects before and after.

[0014] The task allocation method, device, electronic device, and readable storage medium disclosed in the embodiments of this application will be described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0015] This application discloses a task allocation method. Figure 1 It is a schematic flowchart of a task allocation method disclosed in an embodiment of this application. As Figure 1 shown, the method includes the following steps: S120. Obtain the task information of the task to be allocated and the target data of each employee in the target employee group, where the target data includes historical task completion data and personal information data.

[0016] Exemplarily, the task information of the task to be allocated can be obtained from the OA system. The task information of the task to be allocated can include the task name, task type, task description text, etc. The target employee group can be the enterprise, department, etc. to which the task to be allocated belongs on the OA system. Each employee in the target employee group is an employee who may be the processor of the task to be allocated.

[0017] In this application, based on the identifier (Identifier, ID) of each employee in the target employee group, the target data of each employee on the OA system can be obtained.

[0018] Exemplarily, the historical task completion data of an employee may include the ID of the historical completed task, the task type of the historical completed task, the completion time of the historical completed task, the complexity of the historical completed task, the on-time rate of each task type, the specified second-hand task rate of each task type, etc. Among them, the task type may include training tasks, document processing tasks, contract negotiation tasks, process follow-up tasks, etc. The complexity of the historical completed task can be determined according to the level manually marked by relevant personnel (such as the assigned personnel) on the OA system, or according to the keywords in the task description (such as important, critical, etc.), or according to the customer level involved in the task. Among them, the customer level can be determined according to the average processing speed and average overtime rate of the customer's corresponding tasks. When the average processing speed is greater and the average overtime rate is smaller, the customer level is higher. A second-hand task is a task transferred from another employee to the current employee, that is, the current employee is not the first assigned handler of this task. A specified second-hand task is a task that has been assigned to another employee for a certain period of time and has not been overly overdue, that is, a task that has been assigned to another employee for a period of time but has not timed out or has timed out shortly. In this application, a certain task can be identified as a second-hand task by setting a minimum time threshold and an overtime threshold.

[0019] Exemplarily, the personal information data of an employee may include personal physical data, ability data, and risk data. Among them, the personal physical data of the employee may include the age of the employee, and the age of the employee can be determined through the registered employee information. When the age of the employee is smaller, it can be considered that the physical condition of the employee is better. The ability data of the employee may include the employee's education level, task experience, and skill certificates. The employee's education level and task experience can be determined through the registered employee information, and the employee's skill certificates can be determined through the certificate information collected when the employee joins the company. When the employee has a higher education level, more task experience, and more skill certificates, it can be considered that the employee's ability level is higher. The risk data of the employee may include the employee's medical history data, leave data, and task duration data. The employee's medical history data can be identified and determined through the disease type or department type in the submitted sick leave note, the employee's leave data can be determined through the number of submitted leaves (including sick leave and personal leave), and the employee's task duration data can be determined through the clock-in time. When the employee has less medical history, fewer leave times, and a smaller current continuous task duration, it can be considered that the employee's risk data is smaller and the risk level is lower.

[0020] S140. For each of the employees, based on the target data of the employee, determine a characteristic image corresponding to the employee, where the characteristic image is used to characterize the characteristics of the historical processing tasks of the employee.

[0021] That is to say, based on the target data of each employee respectively, determine a characteristic image corresponding to the employee to realize the visualization of the characteristics of the historical processing tasks of the employee.

[0022] S160. Determine the assigned employee corresponding to the task to be assigned based on the task information and the feature images corresponding to each of the employees.

[0023] In this application, based on the task information of the task to be assigned and the feature images corresponding to each employee, determine the assigned employee suitable for the task to be assigned, and send the task to the assigned employee for processing.

[0024] An embodiment of this application provides a task assignment method. By obtaining the task information of the task to be assigned and the target data of each employee in the target employee group, the target data includes historical task completion data and personal information data. For each employee, based on the target data of the employee, determine the feature image corresponding to the employee, where the feature image is used to characterize the features of the tasks historically processed by the employee. Then, based on the task information of the task to be assigned and the feature images corresponding to each employee, determine the assigned employee corresponding to the task to be assigned. Since the task assignment method of this application does not require manual operation by the assignment personnel, the assigned employee of the task to be assigned can be automatically determined, which can improve the efficiency of task assignment.

[0025] In one implementation, the historical task completion data may include the task types of each historical completed task, the completion time of each historical completed task, the complexity of each historical completed task, the on-time rate of each task type, and the designated secondary task rate of each task type. The determining the feature image corresponding to the employee based on the target data of the employee may include: creating a heat map based on the task types of the historical completed tasks of the employee, where the heat map is used to display the type situation of the tasks historically processed by the employee; respectively updating the regional centers corresponding to each task type in the heat map based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task, where the regional center is used to characterize the attribute situation of the task type tasks historically processed by the employee, and the attribute situation includes the complexity level and the time situation; respectively determining the color values of the regional centers corresponding to each task type based on the personal information data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type, to obtain the feature image corresponding to the employee, where the color value of the regional center is used to characterize the processing ability of the employee for the task type tasks in the attribute situation.

[0026] When creating a hotspot map of employees' office tasks, the number and names of task types to be represented in the hotspot map can be determined based on the task types of each historical task completed by the employees. The names of task types can be training tasks, document processing tasks, contract negotiation tasks, process follow-up tasks, etc. Then, in the data visualization tool, select the hotspot map as the data visualization form, and set the rows and columns as needed to display the historical task type data of employees in the hotspot map. In the layout of the hotspot map, different areas can be set to represent different task types. For example, the hotspot map can be divided into four areas, representing training tasks, document processing tasks, contract negotiation tasks, and process follow-up tasks respectively. In addition, the corresponding hotspot range can be determined according to the number of each completed task type. The more the number, the larger the corresponding hotspot range. It should be noted that in this solution, at this time, the same default color will be selected for all task types, that is, only the hotspot distribution of employees in task types is highlighted in the current hotspot map.

[0027] After generating the hotspot map with default colors and identifying different task types by regions, based on the task types of each historical task completed by the employees, the completion time of each historical task, and the complexity of each historical task, the regional centers corresponding to each task type in the hotspot map are updated respectively. Based on the personal information data of the employees, the on-time rate of each task type, and the designated secondary task rate of each task type, the color values of the regional centers corresponding to each task type are determined respectively to obtain a characteristic image that can represent the characteristics of the employees' historical task processing.

[0028] It should be noted that in the hotspot map, one task type corresponds to one regional center.

[0029] In one implementation, the step of updating the regional centers corresponding to each task type in the hotspot map based on the task types of each historical task completed by the employees, the completion time of each historical task, and the complexity of each historical task can include: determining the average completion time of each task type task and the average complexity of each task type task respectively based on the task types of each historical task completed by the employees, the completion time of each historical task, and the complexity of each historical task; updating the regional centers corresponding to each task type in the hotspot map respectively based on the average completion time of each task type task and the average complexity of each task type task.

[0030] In this application, the average complexity and average completion time of each task type are calculated respectively. For the regions corresponding to each task type in the heat map, a horizontal two-dimensional coordinate system is established with the center of the current corresponding region of the task type as the origin. Then, with the average completion time of the tasks of this task type as the abscissa and the average complexity of the tasks of this task type as the ordinate, an offset point on this two-dimensional coordinate system is obtained, and the offset point is determined as the new region center corresponding to this task type in the heat map. Exemplarily, with the center of the current corresponding region of the training task in the heat map as the origin, a horizontal two-dimensional coordinate system is established, and then with the average completion time of the training task as the abscissa and the average complexity of the training task as the ordinate, an offset point on this two-dimensional coordinate system is obtained, and this offset point is determined as the new region center corresponding to the training task in the heat map.

[0031] It should be noted that, in order to maintain the rationality of the new region centers corresponding to each task type, the average completion time and average complexity used to determine the new region centers are the normalized average completion time and average complexity of each task type.

[0032] In one implementation, the personal information data may include personal physical data, ability data, and risk data. Determining the color values of the region centers corresponding to each task type respectively based on the personal information data of the employee, the on-time rate of each task type, and the designated second-hand task rate of each task type to obtain a characteristic image corresponding to the employee may include: determining the red (R) channel values of the region centers corresponding to each task type based on the personal physical data of the employee, where the red channel values are used to characterize the physical condition of the employee; determining the green (G) channel values of the region centers corresponding to each task type respectively based on the ability data of the employee, the on-time rate of each task type, and the designated second-hand task rate of each task type, where the green channel values are used to characterize the ability level of the employee to handle the tasks of this task type; determining the blue (B) channel values of the region centers corresponding to each task type based on the risk data of the employee, where the blue channel values are used to characterize the risk degree of the employee; determining the characteristic image corresponding to the employee based on the red channel values, the green channel values, and the blue channel values of the region centers corresponding to each task type.

[0033] Exemplarily, the better the physical condition of the employee, the larger the red channel values of the region centers corresponding to each task type; the stronger the ability of the employee, the larger the green channel values of the region centers corresponding to each task type; the smaller the risk data of the employee and the lower the risk degree, the larger the blue channel values of the region centers corresponding to each task type.

[0034] After respectively determining the red channel value, green channel value, and blue channel value of the region center corresponding to each task type, the color value of the region center corresponding to each task type can be determined respectively, and then a feature image corresponding to the employee can be obtained.

[0035] In this solution, considering that employees have different processing capabilities for different task types, therefore, for the green channel value corresponding to the ability data, this solution sets corresponding weight coefficients for different task types. Exemplarily, the weight coefficients of the green channel values of different task types can be determined respectively according to the on-time rate of the employee in a preset time period (such as the recent period) or preset number of times (such as the recent times) or all historical processing of each task type, and the specified second-hand task rate of each task type.

[0036] In one implementation manner, the step of respectively determining the green channel value of the region center corresponding to each task type based on the ability data of the employee, the on-time rate of each task type, and the specified second-hand task rate of each task type may include: determining the initial green channel value of the region center corresponding to each task type based on the ability data of the employee, where the initial green channel value is used to represent the baseline ability level of the employee; determining the green channel weight value of the region center corresponding to each task type respectively based on the on-time rate of each task type of the employee and the specified second-hand task rate of each task type; and determining the green channel value of the region center corresponding to each task type respectively based on the initial green channel value and the green channel weight value of the region center corresponding to each task type.

[0037] Exemplarily, the stronger the ability of the employee, the larger the initial green channel value of the region center corresponding to each task type.

[0038] In this embodiment, the green channel weight value of the region center corresponding to any task type can be determined by the following formula; , where is the green channel weight value of the region center corresponding to any task type, is the on-time rate of this task type, is the specified second-hand task rate of this task type, is the current time, is the average completion time of the tasks completed on time, is the average completion time of the specified second-hand tasks, is the task completion time of the current employee's latest on-time completed task, or the task completion time of the current employee's latest on-time completed task of this task type.

[0039] After determining the initial green channel value and the green channel weight values of the regional centers corresponding to each task type, the green channel values of the regional centers corresponding to each task type can be obtained by multiplying the initial green channel value by the green channel weight values of the regional centers corresponding to each task type respectively.

[0040] It should be noted that the red channel values of the regional centers corresponding to each task type can be the same, the initial green channel values of the regional centers corresponding to each task type can be the same, the blue channel values of the regional centers corresponding to each task type can be the same, and the green channel weight values of the regional centers corresponding to each task type can be different.

[0041] In this application, the process of determining the feature image corresponding to an employee can be as Figure 2 shown. Obtain the target data of the employee, create a heat map based on the task types of each historical task completed by the employee, update the regional centers corresponding to each task type in the heat map respectively, determine the color values of the regional centers corresponding to each task type respectively, and then generate the feature image corresponding to the employee.

[0042] In one implementation, determining the assigned employee corresponding to the task to be assigned based on the task information and the feature images corresponding to each employee may include: for each employee, inputting the task information and the feature image corresponding to the employee into a graph neural network to obtain the recognition result corresponding to the employee output by the graph neural network, where the recognition result is used to indicate whether the employee is suitable for the task to be assigned; based on the recognition results corresponding to each employee, determine the assigned employee corresponding to the task to be assigned.

[0043] It should be noted that the graph neural network in this application is a graph neural network that can be used in a directed graph. By using the graph neural network that can be used in a directed graph, this application can accurately and quickly identify whether an employee is suitable for processing the task to be assigned.

[0044] After the task information of the task to be assigned and the corresponding feature image of the employee are input into the graph neural network, the graph neural network converts the task information of the task to be assigned and the corresponding feature image of the employee into nodes and edges respectively. After the conversion into nodes and edges, the self-loop and forwarding mechanisms of the graph neural network can be used for information fusion discovery and the recognition result is output. Among them, when converting the task information of the task to be assigned into nodes, it can be converted into nodes according to methods such as word vectors. Among them, when converting, the direction of the edge between nodes is determined according to the direction of the relative position representing the word meaning by the word vector. For example, the direction of the edge is defined as pointing from the relatively higher position to the relatively lower position. When converting the feature image into an edge, it can be represented as an edge through methods such as pixel representation or feature vector. For example, in pixel representation, the image can be regarded as a pixel grid, each pixel corresponds to a node, and the pixel value can be used as the attribute of the node; in feature vector representation, an image feature extraction model can be used to extract image features, and these features are used as the attributes of the nodes to obtain the feature vector.

[0045] The graph neural network in this application may include a feature extraction layer, an encoding layer, and a classification layer. The feature extraction layer receives the feature image and task information and outputs the directed graph feature. The directed graph feature is converted into an undirected graph through the encoding layer, and the undirected graph outputs the recognition result through the classification layer. This solution introduces an approximate directed graph Laplacian method based on node rank to encode the directed features. When the encoding layer encodes, the directed graph Laplacian Lappr in symmetric normalization format can be expressed as: , , where, is an approximation of the pi π, is the transformation matrix, including a sparse Laplacian node . , where, is the adjacency matrix with self-loop addition, is the diagonal matrix, and n is the number of nodes of the current directed graph. Based on the above content, the activation function of the graph convolutional neural network can be determined as: , where, represents the input parameter, represents the weight.

[0046] In this application, the recognition results corresponding to the employees output by the graph neural network include two types: yes and no. When the recognition result is yes, it indicates that it is recommended to assign the task to be assigned to the employee corresponding to the current feature image. When the recognition result is no, it indicates that it is not recommended to assign the task to be assigned to the employee corresponding to the current feature image.

[0047] In one implementation, when the recognition results corresponding to multiple employees are "yes", the assigned employee corresponding to the task to be assigned can be determined according to the target task type of the task to be assigned and the on-time rate corresponding to the target task type of the multiple employees with recognition results of "yes". Exemplarily, when the task type of the task to be assigned is a training task, and it is determined that the recognition results of employees a, b, and c are "yes" according to the recognition results output by the graph neural network, the assigned employee corresponding to the task to be assigned is determined from employees a, b, and c according to the on-time rates of the training tasks of employees a, b, and c. For example, when the on-time rate of the training task of employee a is the highest among employees a, b, and c, employee a is determined as the assigned employee corresponding to the task to be assigned.

[0048] By adopting the solution of this application, it is possible to intelligently determine the most suitable employee from all employees in the target employee group to handle the task to be assigned by combining the multi-dimensional data of the employees themselves and the task information of the task to be assigned, thereby improving the intelligence level of the OA office software.

[0049] For the task assignment method provided in the embodiments of this application, the execution subject can be a task assignment device. In the embodiments of this application, taking the task assignment device executing the task assignment method as an example, the task assignment device provided in the embodiments of this application is described.

[0050] Figure 3 It is a schematic structural diagram of a task assignment device disclosed in the embodiments of this application. As Figure 3 shown, the task assignment device 300 includes: an acquisition module 310 and a determination module 320.

[0051] In this application, the acquisition module 310 is configured to acquire the task information of the task to be assigned and the target data of each employee in the target employee group, where the target data includes historical task completion data and personal information data; the determination module 320 is configured to, for each employee, determine a feature image corresponding to the employee based on the target data of the employee, where the feature image is used to characterize the features of the tasks historically processed by the employee; the determination module 320 is further configured to determine the assigned employee corresponding to the task to be assigned based on the task information and the feature images corresponding to the respective employees.

[0052] In one implementation, the historical task completion data includes the task types of each historical completed task, the completion time of each historical completed task, the complexity of each historical completed task, the on-time rate of each task type, and the designated secondary task rate of each task type. The determining module 320 determines a feature image corresponding to the employee based on the target data of the employee, including: creating a heat map based on the task types of the historical completed tasks of the employee, where the heat map is used to display the type situation of the historical processing tasks of the employee; respectively updating the regional centers corresponding to each task type in the heat map based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task, where the regional center is used to characterize the attribute situation of the task type tasks historically processed by the employee, and the attribute situation includes the complexity level and the time situation; respectively determining the color values of the regional centers corresponding to each task type based on the personal information data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type, to obtain a feature image corresponding to the employee, where the color value of the regional center is used to characterize the processing ability of the employee for the task type tasks in the attribute situation.

[0053] In one implementation, the determining module 320 respectively updates the regional centers corresponding to each task type in the heat map based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task, including: respectively determining the average completion time of the tasks of each task type and the average complexity of the tasks of each task type based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task; respectively updating the regional centers corresponding to each task type in the heat map based on the average completion time of the tasks of each task type and the average complexity of the tasks of each task type.

[0054] In one implementation, the personal information data includes personal physical data, ability data, and risk data. The determination module 320 determines the color values of the regional centers corresponding to each task type based on the personal information data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type, and obtains a characteristic image corresponding to the employee, including: determining the red channel value of the regional center corresponding to each task type based on the personal physical data of the employee, where the red channel value is used to characterize the physical condition of the employee; determining the green channel value of the regional center corresponding to each task type based on the ability data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type, where the green channel value is used to characterize the ability level of the employee to handle tasks of the task type; determining the blue channel value of the regional center corresponding to each task type based on the risk data of the employee, where the blue channel value is used to characterize the risk level of the employee; and determining a characteristic image corresponding to the employee based on the red channel value, the green channel value, and the blue channel value of the regional center corresponding to each task type.

[0055] In one implementation, the determination module 320 determines the green channel value of the regional center corresponding to each task type based on the ability data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type, including: determining the initial green channel value of the regional center corresponding to each task type based on the ability data of the employee, where the initial green channel value is used to characterize the baseline ability level of the employee; determining the green channel weight value of the regional center corresponding to each task type based on the on-time rate of each task type of the employee and the designated secondary task rate of each task type; and determining the green channel value of the regional center corresponding to each task type based on the initial green channel value and the green channel weight value of the regional center corresponding to each task type.

[0056] In one implementation, the determination module 320 determines the assigned employee corresponding to the task to be assigned based on the task information and the characteristic images corresponding to each employee, including: for each employee, inputting the task information and the characteristic image corresponding to the employee into a graph neural network to obtain an identification result corresponding to the employee output by the graph neural network, where the identification result is used to indicate whether the employee is suitable for the task to be assigned; and determining the assigned employee corresponding to the task to be assigned based on the identification results corresponding to each employee.

[0057] The task allocation device provided by the embodiments of the present application can implement all the processes implemented by the embodiments of the task allocation method. To avoid repetition, details are not described herein again.

[0058] Optionally, as Figure 4 shown, the embodiments of the present application further provide an electronic device 400, including a processor 401 and a memory 402. A program or instruction that can run on the processor 401 is stored on the memory 402. When the program or instruction is executed by the processor 401, it implements each step of the above-mentioned task allocation method embodiment, and can achieve the same technical effect. To avoid repetition, details are not described herein again.

[0059] It should be noted that the electronic devices in the embodiments of the present application include mobile electronic devices and non-mobile electronic devices.

[0060] The embodiments of the present application further provide a readable storage medium. A program or instruction is stored on the readable storage medium. When the program or instruction is executed by a processor, it implements all the processes of the above-mentioned task allocation method embodiment, and can achieve the same technical effect. To avoid repetition, details are not described herein again.

[0061] Wherein, the processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes computer-readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disk or optical disc, etc.

[0062] The embodiments of the present application further provide a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is made to execute the steps of the above-mentioned task allocation method.

[0063] In the above embodiments of the present application, the differences between the various embodiments are mainly described. As long as the different optimization features between the various embodiments are not contradictory, they can be combined to form a better embodiment. Considering the simplicity of the writing, details are not described herein again.

[0064] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A task allocation method, characterized in that, Including: Obtaining task information of a task to be assigned and target data of each employee in a target employee group, where the target data includes historical task completion data and personal information data; For each employee, based on the target data of the employee, determining a feature image corresponding to the employee, where the feature image is used to characterize the features of the tasks historically processed by the employee; Based on the task information and the feature images corresponding to each employee, determining an assigned employee corresponding to the task to be assigned.

2. The method according to claim 1, characterized in that The historical task completion data includes the task types of each historical completed task, the completion time of each historical completed task, the complexity of each historical completed task, the on-time rate of each task type, and the specified second-hand task rate of each task type. The determining, based on the target data of the employee, a feature image corresponding to the employee includes: Based on the task types of the historical completed tasks of the employee, creating a heat map, where the heat map is used to display the type situation of the tasks historically processed by the employee; Based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task, respectively updating the regional centers corresponding to each task type in the heat map, where the regional centers are used to characterize the attribute situation of the task type tasks historically processed by the employee, and the attribute situation includes the complexity level and the time situation; Based on the personal information data of the employee, the on-time rate of each task type, and the specified second-hand task rate of each task type, respectively determining the color values of the regional centers corresponding to each task type, and obtaining a feature image corresponding to the employee, where the color values of the regional centers are used to characterize the processing ability of the employee for the task type tasks of the attribute situation.

3. The method according to claim 2, wherein The updating, based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task, respectively updating the regional centers corresponding to each task type in the heat map includes: Based on the task types of the historical completed tasks of the employee, the completion time of each historical completed task, and the complexity of each historical completed task, respectively determining the average completion time of the task type tasks and the average complexity of the task type tasks; Based on the average completion time of the task type tasks and the average complexity of the task type tasks, respectively updating the regional centers corresponding to each task type in the heat map.

4. The method according to claim 2, wherein The personal information data includes personal physical data, ability data, and risk data. The determining, based on the personal information data of the employee, the on-time rate of each task type, and the specified second-hand task rate of each task type, respectively determining the color values of the regional centers corresponding to each task type, and obtaining a feature image corresponding to the employee includes: Based on the personal physical data of the employee, determine the red channel values of the regional centers corresponding to the respective task types, where the red channel values are used to characterize the physical condition of the employee; Based on the ability data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type, respectively determine the green channel values of the regional centers corresponding to the respective task types, where the green channel values are used to characterize the ability level of the employee to handle tasks of the task type; Based on the risk data of the employee, determine the blue channel values of the regional centers corresponding to the respective task types, where the blue channel values are used to characterize the risk level of the employee; Based on the red channel values, green channel values, and blue channel values of the regional centers corresponding to the respective task types, determine the feature image corresponding to the employee.

5. The method according to claim 4, wherein The step of respectively determining the green channel values of the regional centers corresponding to the respective task types based on the ability data of the employee, the on-time rate of each task type, and the designated secondary task rate of each task type includes: Based on the ability data of the employee, determine the initial green channel values of the regional centers corresponding to the respective task types, where the initial green channel values are used to characterize the baseline ability level of the employee; Based on the on-time rate of each task type of the employee and the designated secondary task rate of each task type, respectively determine the green channel weight values of the regional centers corresponding to the respective task types; Based on the initial green channel values and the green channel weight values of the regional centers corresponding to the respective task types, respectively determine the green channel values of the regional centers corresponding to the respective task types.

6. The method according to claim 1, wherein The step of determining the assigned employee corresponding to the task to be assigned based on the task information and the feature images corresponding to each employee includes: For each employee, by inputting the task information and the feature image corresponding to the employee into a graph neural network, obtain the recognition result corresponding to the employee output by the graph neural network, where the recognition result is used to indicate whether the employee is suitable for the task to be assigned; Based on the recognition results corresponding to each employee, determine the assigned employee corresponding to the task to be assigned.

7. A task allocation device, characterized in that, It includes: An acquisition module for acquiring the task information of the task to be assigned and the target data of each employee in the target employee group, where the target data includes historical task completion data and personal information data; A determination module for, for each employee, based on the target data of the employee, determine the feature image corresponding to the employee, where the feature image is used to characterize the features of the employee's historical task processing; The determination module is further configured to determine the assigned employee corresponding to the task to be assigned based on the task information and the feature images corresponding to each employee.

8. An electronic device, characterized in that, It includes a processor and a memory. The memory stores programs or instructions that can run on the processor. When the programs or instructions are executed by the processor, the steps of the task allocation method according to any one of claims 1-6 are implemented.

9. A readable storage medium, characterized in that, Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the task allocation method according to any one of claims 1-6 are implemented.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute: the steps of the task allocation method according to any one of claims 1-6.

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