Task allocation method and device

Abstract

The embodiment of the invention provides a task allocation method and device, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of computers. The method comprises the steps of obtaining a plurality of idle object numbers under the condition that a target task is received; wherein each idle object number corresponds to one idle object, and the unprocessed task load of each idle object is lower than a first preset threshold value; determining a task matching degree corresponding to each idle object according to a target task type corresponding to the target task; and determining a preferred idle object from the plurality of idle objects according to the task matching degree corresponding to each idle object, and allocating the target task to the preferred idle object. According to the technical scheme provided by the embodiment of the invention, the influence of the processed but unfinished task on the judgment of the service pressure can be avoided, and the current service pressure of the object can be reflected more accurately, so that the task allocation accuracy is improved, and the task processing time and efficiency are shortened.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of computer technologies, and in particular, to a task allocation method, apparatus, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] In various Internet platforms, customer service is an important item affecting users' satisfaction with the platform. During the customer service process, allocating service tasks to appropriate customer service nodes can effectively improve service efficiency and customer experience. When performing task allocation, it is necessary to consider the current service pressure of the customer service nodes to ensure that service tasks can be processed quickly.

[0003] However, since it often takes a longer time for a service task to be determined as completed after being processed by a customer service node, only based on the amount of tasks currently being processed by the customer service node, the current actual service pressure of the customer service node cannot be accurately reflected, resulting in inaccurate task allocation for each customer service node and affecting the efficiency of customer service.

[0004] It should be noted that the above content is not necessarily prior art and is not used to limit the patent protection scope of the present application. Summary of the Invention

[0005] Embodiments of the present application provide a task allocation method, apparatus, computer device, computer-readable storage medium, and computer program product to solve or alleviate one or more of the above-mentioned technical problems.

[0006] One aspect of the embodiments of the present application provides a task allocation method, and the method includes: When a target task is received, obtain a plurality of idle object numbers; wherein, each of the idle object numbers corresponds to an idle object, and the amount of unprocessed tasks of each idle object is lower than a first preset threshold; Determine the task matching degree corresponding to each of the idle objects according to the target task type corresponding to the target task; Determine a preferred idle object from the plurality of idle objects according to the task matching degree corresponding to each of the idle objects, and allocate the target task to the preferred idle object.

[0007] Optionally, the method further includes: Obtain a plurality of tasks to be allocated; Determine the task weight corresponding to each of the tasks to be allocated; Determine a target task from the plurality of tasks to be allocated according to the task weight corresponding to each of the tasks to be allocated.

[0008] Optionally, determining the task weight corresponding to each of the tasks to be assigned includes: Determine the task type, creation time, and creation object of the target task to be assigned, where the target task to be assigned is any one of the multiple tasks to be assigned; Determine the identity identifier of the creation object; Determine the task weight of the target task to be assigned according to the task type, the creation time, and the identity identifier.

[0009] Optionally, determining the task weight of the target task to be assigned according to the task type, the creation time, and the identity identifier includes: Determine the relative time of the target task to be assigned according to the creation time and a preset marked time; Determine the task weight of the target task to be assigned according to the task type, the relative time, and the identity identifier.

[0010] Optionally, the idle object is determined according to the following operations: Determine the periodic task volume of the target online object, where the periodic task volume includes the number of tasks assigned to the target online object within a preset period; When the periodic task volume is lower than a second preset threshold, determine whether the unprocessed task volume of the target online object is lower than a first preset threshold; When the unprocessed task volume of the target online object is lower than the first preset threshold, determine that the target online object is the idle object.

[0011] Optionally, according to the target task type corresponding to the target task, determining the task matching degree corresponding to each of the idle objects includes: Determine the feedback information and average processing time corresponding to the target task type of the target idle object, where the target idle object is any one of the multiple idle objects; Determine the timeliness information corresponding to the target task type of the target idle object according to the feedback information and the average processing time; Determine the task preference degree corresponding to the target task type of the target idle object; Determine the task matching degree of the target idle object according to the timeliness information and the task preference degree.

[0012] Another aspect of the embodiments of the present application provides a task allocation device, and the device includes: An acquisition module, when receiving a target task, acquires a plurality of idle object numbers; where each of the idle object numbers corresponds to an idle object, and the unprocessed task volume of each idle object is lower than a first preset threshold; A first determination module, configured to determine the task matching degree corresponding to each of the idle objects according to the target task type corresponding to the target task; A second determination module, configured to determine a preferred idle object from multiple idle objects according to the task matching degree corresponding to each of the idle objects, and assign the target task to the preferred idle object.

[0013] Another aspect of the embodiments of the present application provides a computer device, including: At least one processor; and A memory communicatively connected to the at least one processor; Wherein: the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0014] Another aspect of the embodiments of the present application provides a computer-readable storage medium, in which computer instructions are stored, and when the computer instructions are executed by a processor, the method as described above is implemented.

[0015] Another aspect of the embodiments of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method as described above is implemented.

[0016] The embodiments of the present application adopting the above technical solutions may include the following advantages: Assign tasks to objects whose current unprocessed task volume has not reached saturation. Thereby, it is possible to avoid the influence of tasks that have been processed but not completed on the judgment of service pressure, more accurately reflect the current service pressure of the object, thereby improving the accuracy of task allocation and shortening the processing time and efficiency of tasks. BRIEF DESCRIPTION OF THE DRAWINGS The drawings exemplarily show embodiments and constitute a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments. The shown embodiments are only for illustrative purposes and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0017] Figure 1 Schematically shows an operating environment diagram of the task allocation method according to Embodiment 1 of the present application; Figure 2 Schematically shows a flowchart of the task allocation method according to Embodiment 1 of the present application; Figure 3 Schematically shows an example schematic diagram of the task type of the target task; Figure 4Schematically shows an additional flowchart of the task allocation method according to Embodiment 1 of the present application; Figure 5 Schematically shows Figure 4 the sub-step flowchart of step S402 in Figure 6 Schematically shows Figure 5 the sub-step flowchart of step S504 in Figure 7 Schematically shows another additional flowchart of the task allocation method according to Embodiment 1 of the present application; Figure 8 Schematically shows Figure 2 the sub-step flowchart of step S202 in Figure 9 Schematically shows an exemplary application flowchart of the task allocation method according to Embodiment 1 of the present application; Figure 10 Schematically shows the block diagram of the task allocation device according to Embodiment 2 of the present application; Figure 11 Schematically shows the hardware architecture diagram of the computer device according to Embodiment 3 of the present application; Figure 12 Schematically shows the process schematic diagram of determining the task to be allocated according to Embodiment 1 of the present application; and Figure 13 Schematically shows the process schematic diagram of determining the task matching degree of the target idle object according to Embodiment 1 of the present application. Detailed implementation manners

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0019] It should be noted that the descriptions involving "first", "second", etc. in the embodiments of the present application are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions appears to be contradictory or unable to be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0020] In the description of the present application, it should be understood that the numerical labels before the steps do not indicate the order of execution of the steps, but are only used to facilitate the description of the present application and distinguish each step. Therefore, it should not be construed as a limitation to the present application.

[0021] First, the following are the term explanations involved in the present application: Timestamp: It is a data format used to represent a specific point in time, and it records time in seconds counted from January 1, 1970, 00:00:00 UTC (Coordinated Universal Time) or other custom time.

[0022] Data mining: It is a process of extracting valuable information and patterns from a large amount of data. By applying technologies such as statistics, machine learning, and artificial intelligence, it analyzes the hidden rules in the dataset to help discover the potential value behind the data.

[0023] Data cleaning: It refers to the process of checking, correcting, and filtering the original data during data analysis and processing to remove incorrect, duplicate, incomplete, or inconsistent data records. It includes operations such as correcting spelling mistakes, filling in missing values, deleting duplicates, standardizing formats, and removing outliers.

[0024] Machine learning algorithm: It is a method in computer science used to build models to automatically learn from data and make predictions or decisions. By inputting a large amount of data and using mathematical models and statistical principles, it discovers the patterns and rules in the data, so as to be able to perform tasks such as classification, regression, and clustering on new unknown data.

[0025] redis-zset: It is a composite data structure that combines the characteristics of a set and a hash table. Each element in the ZSET is unique, and each element is associated with a score. Redis will automatically sort the elements in the set according to this score.

[0026] redis-hash: It is a data structure in the Redis database that stores data in the form of key-value pairs, where the value is a collection of fields and values. Redis-Hash is similar to a hash table or dictionary in programming languages and can store and access multiple attributes of an object.

[0027] Secondly, to facilitate the understanding of the technical solutions provided by the embodiments of the present application by those skilled in the art, the related technologies are described below: In the task system of customer service, when deciding which customer service node to assign a task to, generally only the online status and saturation (or load) of the customer service are considered. As long as the customer service is online and not saturated, it may be assigned the task. However, little consideration is given to promoting the quality of task processing. The quality of task processing includes several dimensions such as timeliness, problem-solving degree, user satisfaction, and the feeling of customer service follow-up. At the same time, in the actual task assignment process, after the customer service node replies to the user, the user often does not immediately feedback whether to end the current service task. At this time, the customer service node needs to wait for the determination result of the user to remove the current service task from the number of tasks being processed by the customer service node. The applicant has found that during the waiting process, the customer service node does not need to further process the task. Therefore, based on the number of tasks currently being processed by the customer service node, it is impossible to accurately judge the service pressure of the customer service node, which will affect the task assignment and processing efficiency.

[0028] Therefore, the embodiment of the present application provides a task assignment technical solution. In this technical solution, (1) task assignment is performed based on data from three aspects: users, tasks, and customer service, fully considering user needs, problem urgency, customer service timeliness, and praise rate, etc., which can improve the task assignment and processing efficiency; (2) tasks are assigned to objects whose current unprocessed task volume has not reached saturation, which can avoid the influence of tasks that have been processed but not completed on the judgment of service pressure, more accurately reflect the current service pressure of the object, thereby improving the accuracy of task assignment and shortening the task processing time and efficiency. See the following for details.

[0029] Finally, for the convenience of understanding, an exemplary operating environment is provided below.

[0030] As Figure 1 shown, the operating environment diagram includes: a server 6, a customer service terminal 2, and a customer terminal 4.

[0031] The server 6 can provide live broadcast room services, and it can be a single server, a server cluster, or a cloud computing service center.

[0032] The customer terminal 4 can be configured to receive service information from the customer service terminal 2 in real time. The customer terminal 4 can be any type of computing device, such as a smart phone, a tablet device, a laptop computer, a smart TV, a vehicle-mounted terminal, etc. The customer terminal 4 can be built with a browser or a dedicated program, and receive the service information through the browser or the dedicated program to output content to the user. The content can include text, voice, or picture and video, etc.

[0033] The customer service terminal 2 is used to receive in real time the service information input by customer service personnel and perform the operation of sending the service information. The service information may include text, voice, or picture / video, etc. The customer service terminal 2 may be an electronic device such as a smart phone or a tablet computer. Of course, the customer service terminal 2 may be a virtual computing instance in the server 6.

[0034] The customer service terminal 2, the customer terminal 4, and the server 6 can be connected through a network. The network may include various network devices, such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, and / or proxy devices, etc. The network may include physical links, such as coaxial cable links, twisted pair cable links, fiber optic links, and their combinations and / or analogs. The network may include wireless links, such as cellular links, satellite links, Wi-Fi links, and / or analogs.

[0035] It should be noted that the numbers of the customer service terminal 2 and the customer terminal 4 in the figure are only illustrative and are not used to limit the patent protection scope of the present application. According to the actual situation, there may be any number of customer service terminals 2 and customer terminals 4.

[0036] Next, taking the server 6 as the execution subject, the technical solution of the present application will be introduced through multiple embodiments. It should be noted that these embodiments can be implemented in various different forms and should not be construed as being limited only to the embodiments described herein.

[0037] Embodiment 1 Figure 2 Schematically shows a flowchart of a task allocation method according to Embodiment 1 of the present application.

[0038] As Figure 2 shown, the task allocation method may include steps S200 to S204, where: Step S200, when receiving a target task, obtain multiple free object numbers; where each of the free object numbers corresponds to a free object, and the unprocessed task amount of each free object is lower than a first preset threshold.

[0039] Step S202, according to the target task type corresponding to the target task, determine the task matching degree corresponding to each free object.

[0040] Step S204, according to the task matching degree corresponding to each free object, determine a preferred free object from multiple free objects, and allocate the target task to the preferred free object.

[0041] The task allocation method provided in this embodiment can avoid the impact of processed but unfinished tasks on the judgment of service pressure, more accurately reflect the current service pressure of the object, thereby improving the accuracy of task allocation and shortening the processing time and efficiency of tasks.

[0042] The following will elaborate in detail on each step in steps S200 to S204 and optional other steps in combination with Figure 2 them.

[0043] Step S200 When a target task is received, obtain a plurality of idle object numbers; wherein, each of the idle object numbers corresponds to an idle object, and the unprocessed task volume of each of the idle objects is lower than a first preset threshold.

[0044] The target task can be a service task initiated by a platform user, such as video review reminder, signature report, comment review, or video barrage appeal, etc. Examples of the task type of the target task can be as Figure 3 shown. The idle object numbers can include Chinese characters, numbers, letters, or symbols, etc. The idle object can be a customer service node that can currently receive service tasks, such as a customer service staff.

[0045] In some embodiments, the status information of the idle objects can be dynamically updated through a real-time monitoring system. Machine learning algorithms can also be used to predict the unprocessed task volume of each object in a future period of time, so as to predict which objects will become idle objects in a future period of time and make preparations for task allocation in advance.

[0046] In other embodiments, the first preset threshold can also be set to be dynamically adjustable. For tasks involving high-value users or urgent tasks, the first preset threshold can be lowered to ensure that these tasks can be preferentially processed.

[0047] In this embodiment, setting the objects with a lower current unprocessed task volume as idle objects that can be assigned tasks can more accurately judge the service pressure of the objects, thereby improving the accuracy of task allocation and the efficiency of task processing.

[0048] The target task can be obtained in various ways. The following provides an exemplary way to obtain the target task.

[0049] In an alternative embodiment, as Figure 4 shown, the method further includes: S400, obtain a plurality of tasks to be assigned.

[0050] S402, determine the task weights corresponding to each of the tasks to be assigned.

[0051] S404. Determine a target task from multiple tasks to be assigned according to the task weights corresponding to each task to be assigned.

[0052] The tasks to be assigned can be service tasks initiated by users or tasks automatically obtained from channels such as system monitoring, device detection, and data mining. The task weights can be determined according to the importance, urgency, creation time, etc. of the tasks to be assigned. They can also be determined according to the business value of users, the complexity and priority of tasks, etc. or a combination of multiple factors.

[0053] In some embodiments, the task weights of tasks to be assigned can also be adjusted according to user feedback. For example, for a task to be assigned that is feedback by users as having poor processing effects, its weight can be appropriately increased.

[0054] In other embodiments, a task queue to be assigned can be generated according to the task weights of each task to be assigned by using a redis-zset structure, etc. The task to be assigned with a higher task weight is ranked higher in the queue, and the task to be assigned at the head of the queue is the target task. The process of determining tasks to be assigned can be as Figure 12 shown. Specifically, create a task (work order) on the customer service platform and classify the work order problems to determine the urgency. Based on user information (production / consumption data) with user authorization, create a user profile and determine the importance based on the user profile. According to the importance, urgency, and timestamp, determine the position of the task (work order) in the queue to be assigned.

[0055] Determining the target task that needs to be assigned for processing according to the task weights can ensure that tasks to be assigned with higher task weights can be preferentially assigned and executed, thereby ensuring more accurate and efficient task assignment and improving the overall service quality.

[0056] According to the above examples, there are various methods for determining task weights. Another exemplary method for determining task weights is provided below.

[0057] In an alternative embodiment, as Figure 5 shown, step S402 includes: S500. Determine the task type, creation time, and creation object of the target task to be assigned, where the target task to be assigned is any one of multiple tasks to be assigned.

[0058] S502. Determine the identity identifier of the creation object.

[0059] S504. Determine the task weight of the target task to be assigned according to the task type, the creation time, and the identity identifier.

[0060] The task type can represent the priority of the task to be assigned. For example, video review reminder can be an urgent task, special event reporting can be a high-priority task, signature reporting can be a medium-priority task, comment review and video barrage appeal can be low-priority tasks, etc. In some embodiments, a corresponding value can also be set for each priority. For example, an urgent task can correspond to 0, a high-priority task can correspond to 1, a medium-priority task can correspond to 2, a low-priority task can correspond to 3, etc.

[0061] In specific implementation, the priority of various task types can be manually marked by relevant operation personnel, or the priority can be automatically marked by an intelligent model.

[0062] The identity identifier of the creating object can be the account level or membership level of the created account, etc. With the user's authorization, a user portrait can also be made for the user based on the user's recharge data or consumption data, etc., and used as the user's identity identifier. In this embodiment, the identity of the creating object will affect the importance of the task to be assigned. For example, if the creating object of the target task to be assigned is a premium member, the target task to be assigned should be recognized as an important task. In some embodiments, corresponding values can also be set for the importance of the task to be assigned. For example, an important task corresponds to 0, an ordinary task (non-important task) corresponds to 1, etc.

[0063] In some embodiments, the priority, importance, and creation time of the target task to be assigned can be calculated according to a preset formula to obtain the task weight of the target task to be assigned. In other embodiments, a neural network model or a machine learning model, etc., can also be used to dynamically calculate the task weight of the target task to be assigned in combination with real-time situations or other parameters.

[0064] In this embodiment, the task weight of the target task to be assigned is determined by combining the task type of the target task to be assigned, the identity information of the user who created the task, and the creation time. Thus, the importance of the target task to be assigned can be judged from multiple dimensions, so as to optimize the allocation method of the task to be assigned, perform task allocation more reasonably, and ensure that important tasks are processed first.

[0065] According to the above example, there are multiple ways to calculate the task weight of the target task to be assigned through the obtained data. The following provides another exemplary way.

[0066] In an alternative embodiment, as Figure 6 shown, step S504 includes: S600, determining the relative time of the target task to be assigned according to the creation time and a preset marking time.

[0067] S602. Determine the task weight of the target task to be assigned according to the task type, the relative time, and the identity identifier.

[0068] In some embodiments, the preset marking time may be "2023-02-23 00:00:00", etc. In other embodiments, a preset end time may also be set, such as "2060-06-16 00:00:00", etc. When the creation time of the target task to be assigned exceeds the preset end time, a new preset marking time may be set.

[0069] The relative time may be recorded in the form of the number of seconds from the preset marking time. For example, if the creation time of the target task to be assigned is "2023-12-01 00:00:00" and the preset marking time is "2023-02-23 00:00:00", the relative time may be "24364800".

[0070] The task weight of the target task to be assigned may be calculated according to a preset formula. For example, if the target task to be assigned is a video review reminder task initiated by a certain premium member at 00:00:00 on December 1, 2023, then the task weight weight of the target task to be assigned = 0 * 10 11 + 0 * 10 10 + 24364800 = 24364800.

[0071] In this embodiment, storing and calculating the creation time of the target task to be assigned in the form of the relative time from the preset marking time can store the creation time of the target task to be assigned with fewer data bits, reduce the pressure of data storage and calculation, and improve the efficiency of task assignment.

[0072] In an alternative embodiment, as Figure 7 shown, the idle object is determined according to the following operations: S700. Determine the periodic task volume of the target online object, where the periodic task volume includes the number of tasks assigned to the target online object within a preset period.

[0073] S702. When the periodic task volume is lower than a second preset threshold, determine whether the unprocessed task volume of the target online object is lower than a first preset threshold.

[0074] S704. When the unprocessed task volume of the target online object is lower than the first preset threshold, determine that the target online object is the idle object.

[0075] The preset period can be one day, one week, one month, etc. The second preset threshold can be 100, etc. In some embodiments, different weight values can also be assigned to different types of tasks according to the task type of the task, and the periodic task volume and the unprocessed task volume of the target online object can be calculated according to the weight values.

[0076] In some embodiments, the historical task volume of the target online object can also be considered, that is, the task volume of the target online object in multiple past periods, to evaluate its long-term load condition. For a target online object with a relatively high historical task volume, a lower second preset threshold can be set.

[0077] In other embodiments, the task completion rate of the target online object can also be evaluated according to historical data. If the task completion rate is relatively high, even if the periodic task volume is slightly higher than the second preset threshold, the target online object can be determined as an idle object when its unprocessed task volume is lower than the first preset threshold.

[0078] In this embodiment, the periodic task volume and the unprocessed task volume of the target online object are comprehensively considered. Thus, the service status (pressure) of the target online object can be accurately evaluated to accurately select idle objects that can be assigned tasks, so as to perform more precise task allocation, which helps to improve the task processing efficiency.

[0079] Step S202 , according to the target task type corresponding to the target task, determine the task matching degree corresponding to each of the idle objects.

[0080] In some embodiments, the target task type can be manuscript problems, data and revenue problems, function abnormality problems, etc. The task matching degree can be calculated by technologies such as machine learning algorithms. In other embodiments, a matching strategy update mechanism adapted to the dynamic environment can also be constructed, and the matching algorithm parameters can be adjusted in real time and dynamically according to factors such as the load status of each current idle object and the urgency of the task, so as to ensure that the task can be optimally allocated under different environmental conditions.

[0081] In this embodiment, the matching degree between the idle object and the target task is calculated according to the task type. Thus, the task can be more precisely assigned to the most suitable idle object, thereby improving the efficiency and quality of task processing.

[0082] There are various methods for calculating the task matching degree. The following provides an exemplary calculation method.

[0083] In an alternative embodiment, as Figure 8 shown, step S202 includes: S800, determining feedback information and average processing time of a target idle object corresponding to the target task type, wherein the target idle object is any one of the plurality of idle objects.

[0084] S802: Determine, according to the feedback information and the average processing time, timeliness information of the target idle object corresponding to the target task type.

[0085] S804: Determine the task preference of the target idle object corresponding to the target task type.

[0086] S806: Determine the task matching degree of the target idle object according to the timeliness information and the task preference.

[0087] The feedback information may be the user's evaluation information of the target idle object, or the customer service staff's self-evaluation, the supervisor's performance evaluation, etc. In some embodiments, the feedback information and the average processing time may be dynamically updated, and the feedback information and the average processing time in the most recent period are preferentially used.

[0088] The collected feedback information and average processing time can also be cleaned and processed to identify and remove abnormal data. The collected feedback information and average processing time can be stored in the form of a redis-hash table, and a redis-hash table is created for each idle object.

[0089] The task preference can be determined by historical task assignment records and subjective feedback from the target idle object, or by combining the target idle object's skills, expertise, training experience, work interests, etc. The process of determining the task matching degree of the target idle object can be as follows: Figure 13 As shown, it is determined based on customer service information, customer service reviews, timeliness table, etc.

[0090] In this embodiment, the task matching degree is determined based on the timeliness information of the target idle object processing the target task type and the preference for the target task type. Thus, the matching degree between the idle object and the task to be assigned can be judged more accurately, thereby improving the accuracy of task assignment and further improving service efficiency and user satisfaction.

[0091] Step S204 , according to the task matching degree corresponding to each of the idle objects, determine a preferred idle object from the multiple idle objects, and assign the target task to the preferred idle object.

[0092] For example, the task matching degrees of idle objects A, B, and C for a task O on video review are 0.6, 0.8, and 0.4 respectively. Then, task O can be assigned to the idle object B with the highest task matching degree (i.e., the preferred idle object).

[0093] In this embodiment, tasks are assigned to preferred idle objects according to the task matching degree. Thus, tasks can be more accurately assigned to suitable idle objects, improving the task processing efficiency and quality and optimizing the overall service process. At the same time, the experience of the target idle object during task processing can also be improved, thereby enhancing the efficiency of the target idle object in processing tasks.

[0094] To make the present application easier to understand, the following provides an exemplary application in combination with Figure 9 One is provided. Among them: S11. Receive multiple tasks O1, O2, O3. Among them, task O1 is a video review reminder task initiated by user U1 at 00:00:00 on December 1, 2023, task O2 is a signature reporting task initiated by user U1 at 00:10:00 on December 1, 2023, and task O3 is a comment review task initiated by user U2 at 00:11:00 on December 1, 2023; S12. Preset the marked time as 00:00:00 on February 23, 2023, and calculate that the relative time of task O1 is 24364800, the relative time of task O2 is 24365400, and the relative time of task O3 is 24279060; S13. User U1 is an important user, and user U2 is an ordinary user. Calculate that the task weight weight1 of task O1 is 24364800, the task weight weight2 of task O2 is 2026083800, and the task weight weight3 of task O3 is 13024279060; S14. Determine that task O1 with the smallest task weight value is the target task; S15. Currently, the online customer service staff (i.e., the target online objects) are S1, S2, S3, S4. Among them, the daily task volume (i.e., the periodic task volume) of S4 exceeds 100 pieces (i.e., the second preset threshold), and the unprocessed task volumes of S1, S2, and S3 are all lower than 5 pieces (i.e., the second preset threshold). Determine that S1, S2, and S3 are idle customer service (i.e., idle objects); S16. The historical average evaluation (i.e., feedback information) of customer service staff S1 for video review reminder type tasks is 4.4, the average processing time is 500 seconds, and the task preference degree is 0.3. Calculate that the task matching degree between customer service staff S1 and task O1 is 0.5; The historical average evaluation (i.e., feedback information) of customer service staff S2 for video review reminder type tasks is 4, the average processing time is 600 seconds, and the task preference degree is 0.2. Calculate that the task matching degree between customer service staff S2 and task O1 is 0.4; The historical average evaluation (i.e., feedback information) of customer service staff S3 for tasks of the video reminder review type is 5, the average processing time is 300 seconds, and the task preference is 0.5. The task matching degree between customer service staff S2 and task O1 is calculated to be 0.7; S17 determines that customer service staff S3 (i.e., the preferred idle object) is to handle task O1, and assigns task O1 to customer service staff S3.

[0095] Embodiment 2 Figure 10 A block diagram of a task allocation device according to Embodiment 2 of the present application is schematically shown. The device can be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the embodiments of the present application. The program modules referred to in the embodiments of the present application refer to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment. As Figure 10 shown, the device 1000 may include: an acquisition module 1100, a first determination module 1200, and a second determination module 1300, where: The acquisition module 1100, when receiving a target task, acquires a plurality of idle object numbers; wherein, each of the idle object numbers corresponds to an idle object, and the unprocessed task volume of each of the idle objects is lower than a first preset threshold; The first determination module 1200 is configured to determine the task matching degree corresponding to each of the idle objects according to the target task type corresponding to the target task; The second determination module 1300 is configured to determine a preferred idle object from the plurality of idle objects according to the task matching degree corresponding to each of the idle objects, and assign the target task to the preferred idle object.

[0096] As an optional embodiment, the device 1000 further includes a target task determination module, which is configured to: Acquire a plurality of tasks to be assigned; Determine the task weights corresponding to each of the tasks to be assigned; Determine a target task from the plurality of tasks to be assigned according to the task weights corresponding to each of the tasks to be assigned.

[0097] As an optional embodiment, the target task determination module is further configured to: Determine the task type, creation time, and creation object of a target task to be assigned, where the target task to be assigned is any one of the plurality of tasks to be assigned; Determine the identity identifier of the creation object; Determine the task weight of the target task to be assigned according to the task type, the creation time, and the identity identifier.

[0098] As an alternative embodiment, the target task determination module is further configured to: Determine the relative time of the target task to be assigned according to the creation time and the preset marking time; Determine the task weight of the target task to be assigned according to the task type, the relative time, and the identity identifier.

[0099] As an alternative embodiment, the apparatus 1000 further includes an idle object determination module, configured to: Determine the periodic task volume of the target online object, where the periodic task volume includes the number of tasks assigned to the target online object within a preset period; When the periodic task volume is lower than a second preset threshold, determine whether the unprocessed task volume of the target online object is lower than a first preset threshold; When the unprocessed task volume of the target online object is lower than the first preset threshold, determine that the target online object is the idle object.

[0100] As an alternative embodiment, the apparatus 1000 further includes a task matching degree determination module, configured to: Determine the feedback information and the average processing time corresponding to the target task type of the target idle object, where the target idle object is any one of the multiple idle objects; Determine the timeliness information corresponding to the target task type of the target idle object according to the feedback information and the average processing time; Determine the task preference degree of the target idle object corresponding to the target task type; Determine the task matching degree of the target idle object according to the timeliness information and the task preference degree.

[0101] Embodiment III Figure 11 FIG. schematically shows a hardware architecture diagram of a computer device 10000 suitable for implementing the task allocation method according to Embodiment III of the present application. In some embodiments, the computer device 10000 may be a terminal device such as a smart phone, a wearable device, a tablet computer, a personal computer, a vehicle-mounted terminal, a game console, a virtual device, a workbench, a digital assistant, a set-top box, a robot, etc. In other embodiments, the computer device 10000 may be a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster composed of multiple servers). As Figure 11 shown, the computer device 10000 includes, but is not limited to: a memory 10010, a processor 10020, and a network interface 10030 that can communicate with each other through a system bus. Among them: The memory 10010 includes at least one type of computer-readable storage medium. The readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 10010 may be an internal storage module of the computer device 10000, such as the hard disk or memory of the computer device 10000. In other embodiments, the memory 10010 may also be an external storage device of the computer device 10000, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the computer device 10000. Of course, the memory 10010 may also include both the internal storage module and the external storage device of the computer device 10000. In this embodiment, the memory 10010 is generally used to store the operating system and various application software installed on the computer device 10000, such as the program code of the task allocation method. In addition, the memory 10010 may also be used to temporarily store various data that have been output or will be output.

[0102] In some embodiments, the processor 10020 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other chip. The processor 10020 is generally used to control the overall operation of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is used to run the program code stored in the memory 10010 or process data.

[0103] The network interface 10030 may include a wireless network interface or a wired network interface, which is generally used to establish a communication link between the computer device 10000 and other computer devices. For example, the network interface 10030 is used to connect the computer device 10000 to an external terminal via a network, and establish a data transmission channel and a communication link between the computer device 10000 and the external terminal. The network may be a wireless or wired network such as an enterprise intranet (Intranet), the Internet, Global System of Mobile communication (GSM for short), Wideband Code Division Multiple Access (WCDMA for short), 4G network, 5G network, Bluetooth, Wi-Fi, etc.

[0104] It should be noted that Figure 11 only the computer device with components 10010 - 10030 is shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.

[0105] In this embodiment, the task allocation method stored in the memory 10010 can also be divided into one or more program modules and executed by one or more processors (such as the processor 10020) to complete the embodiments of this application.

[0106] Embodiment 4 The embodiments of this application also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the task allocation method in the embodiments are implemented.

[0107] In this embodiment, the computer-readable storage medium includes flash memory, hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), random access memories (RAM), static random access memories (SRAM), read-only memories (ROM), electrically erasable programmable read-only memories (EEPROM), programmable read-only memories (PROM), magnetic memories, magnetic disks, optical disks, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the computer device. Of course, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is generally used to store the operating system installed on the computer device and various application software, such as the program code of the task allocation method in the embodiment. In addition, the computer-readable storage medium can also be used to temporarily store various data that have been output or will be output.

[0108] Embodiment 5 The embodiment of the present application also provides a computer program product, including a computer program, which when executed by a processor implements the method in the above embodiment.

[0109] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the embodiments of the present application can be implemented by a general-purpose computer device. They can be concentrated on a single computer device or distributed on a network composed of multiple computer devices. Optionally, they can be implemented by program codes executable by the computer device. Thus, they can be stored in a storage device and executed by the computer device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0110] It should be noted that the above are only the preferred embodiments of the present application, and do not limit the patent protection scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A task allocation method, characterized in that: The method comprises: When receiving the target task, obtaining multiple idle object numbers; wherein each idle object number corresponds to an idle object, and the amount of unprocessed tasks of each idle object is lower than a first preset threshold; Determining the task matching degree corresponding to each of the idle objects according to the target task type corresponding to the target task; According to the task matching degree corresponding to each of the idle objects, a preferred idle object is determined from the plurality of idle objects, and the target task is allocated to the preferred idle object.

2. The method according to claim 1, characterized in that The method further comprises: Get multiple tasks to be assigned; Determine the task weight corresponding to each of the tasks to be assigned; According to the task weights corresponding to the tasks to be assigned, a target task is determined from the multiple tasks to be assigned.

3. The method according to claim 2, characterized in that Determining the task weights corresponding to the tasks to be assigned includes: Determine the task type, creation time and creation object of the target task to be assigned, wherein the target task to be assigned is any one of the multiple tasks to be assigned; Determining the identity of the created object; The task weight of the target task to be assigned is determined according to the task type, the creation time and the identity identifier.

4. The method according to claim 3, characterized in that Determining the task weight of the target task to be assigned according to the task type, the creation time and the identity identifier includes: Determine the relative time of the target task to be assigned according to the creation time and the preset marking time; The task weight of the target task to be assigned is determined according to the task type, the relative time and the identity identifier.

5. The method according to claim 1, characterized in that The idle object is determined according to the following operations: Determine a periodic task amount of a target online object, wherein the periodic task amount includes the number of tasks assigned to the target online object within a preset period; In the case where the periodic task amount is lower than the second preset threshold, determining whether the unprocessed task amount of the target online object is lower than the first preset threshold; When the amount of unprocessed tasks of the target online object is lower than a first preset threshold, the target online object is determined to be the idle object.

6. The method according to claim 1, characterized in that Determining the task matching degree corresponding to each of the idle objects according to the target task type corresponding to the target task includes: Determine feedback information and average processing time of a target idle object corresponding to the target task type, wherein the target idle object is any one of the plurality of idle objects; Determining, according to the feedback information and the average processing time, time efficiency information of the target idle object corresponding to the target task type; Determine the task preference of the target idle object corresponding to the target task type; The task matching degree of the target idle object is determined according to the timeliness information and the task preference degree.

7. A task allocation device, characterized in that: The device comprises: An acquisition module, when receiving a target task, acquires a plurality of idle object numbers; wherein each of the idle object numbers corresponds to an idle object, and the amount of unprocessed tasks of each of the idle objects is lower than a first preset threshold; A first determination module, used to determine the task matching degree corresponding to each of the idle objects according to the target task type corresponding to the target task; The second determining module is used to determine a preferred idle object from the plurality of idle objects according to the task matching degree corresponding to each of the idle objects, and allocate the target task to the preferred idle object.

8. A computer device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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