Online resource management method and system

Abstract

The invention relates to the technical field of online resource management, in particular to an online resource management method and system. According to the invention, the task complexity of the target task of the user, the interaction cooperation degree with the service provider and the business experience value of the corresponding reservation service provider are comprehensively considered, the first predicted processing time is calculated, and then the service time of each service provider on the day and the number of undertaken users are combined. Calculating a task completion rate of each service provider, dividing the service providers into two types of progress advanced servers and progress lagging servers according to the task completion rates, then screening out dispatchable users responsible for the progress lagging servers, and finally calculating a second predicted processing time length according to a dispatchable user set and a progress standard-reaching server set, and a plurality of dispatch service combinations are obtained in combination with the task completion rate, and task dispatch is performed on the dispatchable users by means of the dispatch service combinations, so that the users can obtain more reasonable service arrangement.

Description

Technical Field

[0001] The present invention relates to the technical field of online resource management, and particularly to an online resource management method and system. Background Art

[0002] With the popularization of the Internet, as well as the wide application of high-speed broadband networks and wireless communication technologies, a solid network foundation has been provided for the resource management of the online service industry. The resources of the online service industry cover multiple aspects, mainly including business information systems, online trading platforms, business data, and remote collaboration platforms, etc. The online resource management system can help service enterprises optimize business processes and improve service efficiency and customer satisfaction.

[0003] The business volume is an important factor in service resource allocation, but it cannot be the only criterion for service time period and service team allocation. Currently, there are obvious deficiencies in the resource management of the online service industry, especially the differences in service durations of different users are not fully considered, lacking refined consideration. For example, some users generally have longer service times due to complex business requirements, unfamiliarity with processes, and low communication efficiency; while another part of users have simple business requirements, convenient communication, and short service time consumption. However, the existing online service resource management ignores these differences and still mainly allocates service time periods according to the business volume. When a service provider receives a relatively large proportion of users with long service demand, it is difficult for the service provider to complete service tasks on time. Hasty services will affect service quality and cause congestion in service time periods at the same time. If the proportion of users with short service demand is relatively high, it will lead to idle service resources of the service provider in some time periods. This makes it difficult to reasonably allocate service resources, which can neither meet user needs nor is conducive to improving the service quality of enterprises. Summary of the Invention

[0004] The main object of the present invention is to provide an online resource management method and system, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes an online resource management method, including: Obtaining service interaction information of a service department, where the service interaction information includes service provider information and user task information; Obtaining the target task information and past task information of each user according to the user task information, and obtaining the task complexity of the target task of each user according to the target task information and past task information; Obtaining user service association data according to the user task information, and obtaining the interaction cooperation degree of each user according to the user service association data and past task information; Obtain the business experience value of each service provider according to the service provider information, and obtain the first estimated processing duration of each user's target task according to the task complexity, interaction cooperation degree of each user, and the business experience value of the corresponding initial service provider; Obtain the daily service duration of each service provider according to the service provider information, and obtain the task completion rate according to the daily service duration of each service provider and the first estimated processing duration of the corresponding receiving user; Classify the service providers according to the task completion rate to obtain a set of service providers with advanced progress and a set of service providers with lagging progress, and screen the users received by all service providers with lagging progress in the set of service providers with lagging progress according to the appointment service information to obtain a set of users that can be reassigned; Obtain the second estimated processing duration according to the set of users that can be reassigned and the set of service providers with advanced progress, and perform task reassignment on each user that can be reassigned according to the second estimated processing duration and the task completion rate to generate a resource allocation plan, where the resource allocation plan includes multiple reassignment service combinations.

[0006] Preferably, the step of obtaining the task complexity of each user's target task according to the target task information and past task information includes: Obtain the task requirement expression information and task execution result information according to the target task information; Obtain the industry standard, and obtain the task difficulty value of each user's target task according to the industry standard and the task requirement expression information; Obtain the execution frequency of the target task and the cumulative execution frequency of multiple tasks according to the past task information, and obtain the single-task weighted value corresponding to the target task according to the execution frequency of the target task and the cumulative execution frequency of multiple tasks; Obtain the task association score value according to the past task information and the industry standard, and obtain the task exception score value according to the task execution result information and the industry standard; Calculate the task complexity according to the task difficulty value, single-task weighted value, task association score value, and task exception score value, where the calculation formula is: ; Wherein, represents the task complexity, represents the task difficulty value of the th target task, represents the serial number of the target task, represents the th single-task weighted value of the target task, represents the task association score value, represents the task exception score value.

[0007] Preferably, the step of obtaining the interaction cooperation degree of each user according to the user service association data and past task information includes: Obtaining multiple groups of cooperation degree evaluation features and corresponding historical interaction cooperation degrees according to the user service association data, wherein the cooperation degree evaluation features include consultation frequency feature, consultation content feature, data integrity feature, and appointment fulfillment time feature; Obtaining the correlation coefficients corresponding to the consultation frequency feature, consultation content feature, data integrity feature, and appointment fulfillment time feature and the interaction cooperation degree respectively according to the user service association data, and obtaining the interaction occupancy ratios corresponding to the consultation frequency feature, consultation content feature, data integrity feature, and appointment fulfillment time feature according to the correlation coefficients; Obtaining historical behavior information according to the past task information of each user, wherein the historical behavior information includes consultation frequency information, consultation content information, data integrity information, and appointment fulfillment time information; Obtaining judgment standard information, and judging the consultation frequency information, consultation content information, data integrity information, and appointment fulfillment time information according to the judgment standard information to obtain a key feature judgment set; Obtaining the interaction cooperation degree of each user according to the key feature judgment set and the interaction occupancy ratio.

[0008] Preferably, the step of obtaining the business experience value of each service provider according to the service provider information, and obtaining the first estimated processing duration of the target task of each user according to the task complexity, interaction cooperation degree of each user, and the business experience value of the corresponding initial service provider includes: Obtaining the service evaluation information and service history information of each service provider according to the service provider information, and obtaining a service evaluation value according to the service evaluation information; Obtaining the service success rate and service failure rate according to the service history information, and obtaining a task processing ability value according to the service success rate and service failure rate; Obtaining industry business evaluation data, and obtaining an evaluation weight coefficient according to the industry business evaluation data, wherein the evaluation weight coefficient includes a first weight coefficient corresponding to the service evaluation value and a second weight coefficient corresponding to the task processing ability value, and obtaining a business experience value according to the service evaluation value, the first weight coefficient, the task processing ability value, and the second weight coefficient; Obtaining the basic service duration according to the service history information, and calculating the first estimated processing duration according to the basic service duration, task complexity, interaction cooperation degree, and business experience value, wherein the calculation formula is: ; wherein, refers to the first estimated processing duration, refers to the basic service duration, refers to the business experience value, refers to the task complexity, refers to the interaction cooperation degree.

[0009] Preferably, the step of grading service providers according to the task completion rate to obtain a set of service providers with advanced progress and a set of service providers with lagging progress, and screening the users undertaken by all service providers with lagging progress in the set of service providers with lagging progress according to the reserved service information to obtain a set of users to be reassigned includes: Determine whether the task completion rate of each service provider is greater than 1; If the task completion rate of the service provider is greater than 1, it is determined that the service provider is a service provider with advanced progress; If the task completion rate of the service provider is not greater than 1, it is determined that the service provider is a service provider with lagging progress; Obtain the reassignment willingness option according to the user task information of the users undertaken by the service providers with lagging progress; Determine whether the reassignment willingness option of the users undertaken by the service providers with lagging progress is willing to be reassigned; If the reassignment willingness option of the user is willing to be reassigned, it is determined that the user is a user to be reassigned; If the reassignment willingness option of the user is not willing to be reassigned, it is determined that the user is a non-reassigned user.

[0010] Preferably, the step of obtaining the second estimated processing duration according to the set of users to be reassigned and the set of service providers with advanced progress, and reassigning tasks to each user to be reassigned according to the second estimated processing duration and the task completion rate to generate a resource allocation plan includes: Arrange and combine multiple users to be reassigned in the set of users to be reassigned and service providers with advanced progress in the set of service providers with advanced progress to obtain a set of deployable combinations, and obtain the second estimated processing duration of each deployable combination according to the set of deployable combinations; Obtain the task compliance time and the remaining service duration according to the task completion rate and the daily service duration of the service providers with advanced progress, and obtain the total service time of each service provider with advanced progress according to the task compliance time, the second estimated processing duration and the set of deployable combinations; Obtain the average service time according to the total service time of each service provider with advanced progress, and obtain the objective function according to the average service time and the total service time; Obtain the constraint condition information according to the set of users to be reassigned, the set of service providers with advanced progress and the remaining service duration, and obtain the adapted service combination from the set of deployable combinations according to the constraint condition information and the objective function to form a resource allocation plan.

[0011] The present application also provides an online resource management system, including: A first acquisition module, configured to acquire service interaction information of a service department, where the service interaction information includes service provider information and user task information; A second acquisition module, configured to acquire target task information and past task information of each user according to the user task information, and acquire the task complexity of the target task of each user according to the target task information and the past task information; A third acquisition module, configured to acquire user service association data according to the user task information, and acquire the interaction cooperation degree of each user according to the user service association data and the past task information; A fourth acquisition module, configured to acquire the business experience value of each service provider according to the service provider information, and acquire the first estimated processing duration of the target task of each user according to the task complexity, interaction cooperation degree of each user and the business experience value of the corresponding initial service provider; A fifth acquisition module, configured to acquire the daily service duration of each service provider according to the service provider information, and acquire the task completion rate according to the daily service duration of each service provider and the first estimated processing duration of the corresponding received user; A screening module, which grades service providers according to the task completion rate to obtain a set of service providers with advanced progress and a set of service providers with lagging progress, and screens the users received by all service providers with lagging progress in the set of service providers with lagging progress according to the appointment service information to obtain a set of users that can be reassigned; An allocation module, which acquires the second estimated processing duration according to the set of users that can be reassigned and the set of service providers with advanced progress, and reassigns tasks to each user that can be reassigned according to the second estimated processing duration and the task completion rate to generate a resource allocation plan, where the resource allocation plan includes multiple reassignment service combinations.

[0012] Preferably, the allocation module includes: A permutation and combination unit, configured to permute and combine multiple users that can be reassigned in the set of users that can be reassigned and the service providers with advanced progress in the set of service providers with advanced progress to obtain a set of allocable combinations, and acquire the second estimated processing duration of each allocable combination according to the set of allocable combinations; A first acquisition unit, configured to acquire the task compliance duration and the remaining service duration according to the task completion rate and the daily service duration of the service provider with advanced progress, and acquire the total service time of each service provider with advanced progress according to the task compliance duration, the second estimated processing duration and the set of allocable combinations; A second acquisition unit, configured to acquire the average service time according to the total service time of each service provider with advanced progress, and acquire the objective function according to the average service time and the total service time; A third acquisition unit, configured to obtain constraint condition information according to the set of transferable users, the set of service providers with advanced progress, and the remaining service duration, and obtain an adapted service combination from the set of deployable combinations according to the constraint condition information and the objective function, so as to form a resource deployment plan.

[0013] The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned online resource management method are implemented.

[0014] The present invention also provides 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 above-mentioned online resource management method are implemented.

[0015] The beneficial effects of the present invention are as follows: In the present invention, service provider information and user task information are obtained through service interaction information of an online service platform, comprehensively mastering the conditions of users and service providers. Based on the target task information and past task information of each user, the task complexity of the user's current target task is evaluated. At the same time, according to the user service association data and past task information, the interaction cooperation degree between the user and the reserved service provider is determined. Then, according to the service provider information, the business experience value of each service provider is obtained, and the first estimated processing duration required for each user's target task is obtained by comprehensively considering the task complexity, interaction cooperation degree of each user, and the business experience value of the corresponding initial service provider. Then, combined with the daily service duration of each service provider and the number of users undertaken, the task completion rate of each service provider is calculated. The task completion rate can intuitively show the possibility of each service provider completing tasks under the current resource allocation situation, facilitating timely detection of problems such as overloaded tasks or idle resources of service providers. Then, the service providers are classified according to the task completion rate, divided into a set of service providers with advanced progress and a set of service providers with lagging progress. Subsequently, the users responsible for all service providers with lagging progress in the set of service providers with lagging progress are screened to obtain a set of transferable users. After clarifying the service providers and user groups that need to be deployed, the online resource deployment can be more targeted. Finally, the second estimated processing duration is calculated according to the set of transferable users and the set of service providers with qualified progress, and the transferable users are transferred according to the second estimated processing duration and the task completion rate to obtain multiple transfer service combinations. With the help of the transfer service combinations, it can be clearly determined to which service provider with advanced progress each transferable user is transferred to process tasks, so as to enable users to obtain a more reasonable service arrangement, improve the utilization rate of online resources, reduce the user waiting time, and improve the user service experience and the service quality of the online service platform. Description of the Drawings

[0016] Figure 1Schematic flowchart of a method according to an embodiment of the present application.

[0017] Figure 2 Schematic structural diagram of a system according to an embodiment of the present application.

[0018] Figure 3 Schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0019] The realization of the object of the present invention, functional features and advantages will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] As Figures 1-3 shown, the present application provides an online resource management method, including: S1. Obtain service interaction information of a service department, where the service interaction information includes service provider information and user task information; S2. Obtain the target task information and past task information of each user according to the user task information, and obtain the task complexity of the target task of each user according to the target task information and past task information; S3. Obtain user service association data according to the user task information, and obtain the interaction cooperation degree of each user according to the user service association data and past task information; S4. Obtain the business experience value of each service provider according to the service provider information, and obtain the first estimated processing duration of the target task of each user according to the task complexity, interaction cooperation degree of each user and the business experience value of the corresponding initial service provider; S5. Obtain the daily service duration of each service provider according to the service provider information, and obtain the task completion rate according to the daily service duration of each service provider and the first estimated processing duration of the corresponding receiving user; S6. Classify the service providers according to the task completion rate to obtain a set of service providers with advanced progress and a set of service providers with lagging progress, and screen the users received by all service providers with lagging progress in the set of service providers with lagging progress according to the reservation service information to obtain a set of users that can be reassigned; S7. Obtain the second estimated processing duration according to the set of users that can be reassigned and the set of service providers with advanced progress, and perform task reassignment on each user that can be reassigned according to the second estimated processing duration and the task completion rate to generate a resource allocation plan, where the resource allocation plan includes multiple reassignment service combinations.

[0022] As described in the above steps S1 - S7, with the popularization of the Internet, as well as the wide application of high - speed broadband networks and wireless communication technologies, a solid network foundation has been provided for the resource management of the online service industry. The resources of the online service industry cover multiple aspects, mainly including business information systems, online trading platforms, business data, and remote collaboration platforms, etc. The online resource management system can help service enterprises optimize business processes and improve the efficiency of services and customer satisfaction. The business volume is an important factor in service resource allocation, but it cannot be the only criterion for service time period and service team allocation. Currently, there are obvious deficiencies in the resource management of the online service industry, especially the differences in service durations of different users are not fully considered, lacking refined consideration. For example, some users generally have longer service times due to complex business requirements, unfamiliarity with processes, and low communication efficiency; while another part of users have simple business requirements, convenient communication, and short service time consumption. However, the existing online service resource management ignores these differences and still mainly allocates service time periods according to business volume. When a relatively large proportion of users with long - time service requirements are received by a service provider, it is difficult for the service provider to complete service tasks on time. Hasty services will affect service quality and at the same time cause congestion in service time periods. If the proportion of users with short - time service requirements is relatively high, it will lead to idle service resources of service providers in some time periods. This makes it difficult to reasonably allocate service resources, neither meeting user needs nor being conducive to improving the service quality of enterprises. In the present invention, by obtaining the service interaction information of the service department, the relevant situation of the service department can be comprehensively understood. Among them, service interaction information refers to various relevant information generated between the service department and users, including service provider information and user task information. Among them, service provider information refers to the relevant materials of the personnel providing services, including the skill level, work experience, professional background, etc. of the service provider. User task information refers to the relevant information of all tasks that users make appointments and process with the service department, such as the type, requirements, goals, etc. of the tasks. Then, according to the user task information, target task information and past task information are obtained. Among them, target task information refers to the key information of the specific tasks that need to be completed currently clearly defined, which clarifies the core goals and directions of the services required by users. Past task information refers to the task - related records when users received similar services in the past, including the completion situation, service duration, service quality evaluation, etc. It can be used to analyze the behavior patterns and task characteristics of users. Immediately afterwards, by combining the target task information and past task information, the task complexity of each user's target task is obtained. Among them, task complexity refers to the value obtained by quantitatively evaluating the difficulty level of the user's current target task. This value reflects the resources and difficulty level required to complete the target task. Then, according to the user task information, user service - related data is obtained. Among them, user service - related data refers to various data reflecting the interaction situation between users and services. Immediately afterwards, by combining the past task information, the interaction cooperation degree of each user is obtained. AmongThe interaction cooperation degree refers to the value for evaluating the cooperation degree between the user and the service provider during the service process. This value reflects the user's understanding of the service process and collaboration ability. The two indicators of task complexity and interaction cooperation degree take into account the impact of differences in different tasks and the user's communication ability on the task processing time. Then, according to the service provider information, the business experience value of each service provider is obtained. Among them, the business experience value is a numerical value used to measure the richness of the service provider's experience in the relevant business field. Then, by combining the task complexity, interaction cooperation degree of each user and the business experience value of the corresponding initial service provider, the first estimated processing duration is obtained. Among them, the first estimated processing duration is the estimated value of the time consumed by the initial service provider to complete the target task for the service user. Through this estimation method that comprehensively considers the factors of both the user and the service provider, it can more comprehensively reflect the actual service situation, make the acquisition of the first estimated processing duration more scientific and reasonable, and provide an accurate time reference for subsequent resource allocation. Then, the daily service duration of each service provider is obtained. Among them, the daily service duration refers to the total duration that the service provider can provide services on the day of providing services. Then, by combining the first estimated processing duration of the users undertaken by each service provider, the task completion rate is obtained. The calculation formula of the task completion rate is: , where refers to the task completion rate of the th service provider, refers to the daily service duration of the th service provider, refers to the number of users undertaken by the th service provider, refers to the th user undertaken and the The first estimated consultation duration corresponding to each service provider can intuitively reflect the possibility of each service provider completing service tasks under the current business volume distribution through the task completion rate, facilitating the timely discovery of situations such as overloaded tasks or idle resources of service providers. Then, the service providers are graded according to the task completion rate to obtain a set of service providers with advanced progress and a set of service providers with lagging progress. Among them, the set of service providers with advanced progress refers to the set of service providers who are predicted to be able to complete service tasks within the specified time, and the set of service providers with lagging progress refers to the set of service providers who, in contrast to the set of service providers with advanced progress, are predicted to be unable to complete service tasks on time. Immediately afterwards, the users served by all service providers with lagging progress in the set of service providers with lagging progress are screened to obtain a set of users who can be reassigned. Among them, the set of users who can be reassigned refers to the set of users who are willing to be reassigned to other service providers for service. By clarifying the service providers and user groups that need to have tasks reassigned, the allocation of service resources can be made more targeted, avoiding blind allocation, improving the efficiency and accuracy of resource allocation. Finally, the second estimated processing duration is obtained based on the set of users who can be reassigned and the set of service providers with advanced progress. Among them, the second estimated processing duration refers to the pre-estimated value of the time required for the service provider undertaking the reassigned task to complete the reassigned task when the user who can be reassigned is reassigned to other service providers for service. And based on the second estimated processing duration and the task completion rate, the users who can be reassigned are reassigned to generate a resource allocation plan including multiple reassignment service combinations. Among them, the reassignment service combination refers to the combination formed by specifying that the task of a certain user who can be reassigned is reassigned to a specific service provider with advanced progress. The allocation arrangement information refers to the overall plan formed after the tasks of each user who can be reassigned are reassigned, including detailed contents such as the reassignment direction of the users who can be reassigned. Through the reassignment service combination, it can be clarified to which service provider with advanced progress each user who can be reassigned is reassigned for service, so that the users who can be reassigned can obtain more reasonable service arrangements, improving the utilization rate of service resources, optimizing the allocation of service resources, reducing the waiting time of users, and enhancing the service experience of users and the service quality of the service department.

[0023] In one embodiment, the step S2 of obtaining the task complexity of each user's target task according to the target task information and the past task information includes: S21. Obtain task requirement expression information and task execution result information according to the target task information; S22. Obtain industry standard specifications, and obtain the task difficulty value of each user's target task according to the industry standard specifications and the task requirement expression information; S23. Obtain the execution frequency of the target task and the cumulative execution frequency of multiple tasks according to the past task information, and obtain the single-task weighted value corresponding to the target task according to the execution frequency of the target task and the cumulative execution frequency of multiple tasks; S24. Obtain a task correlation score value according to the past task information and industry specification standards, and obtain a task anomaly score value according to the task execution result information and industry specification standards; S25. Calculate the task complexity according to the task difficulty value, single-task weighting value, task correlation score value, and task anomaly score value, where the calculation formula is: ; where represents the task complexity, represents the task difficulty value of the th target task, represents the serial number of the target task, represents the single-task weighting value of the th target task, represents the task correlation score value, represents the task anomaly score value.

[0024] As described in the above steps S21-S25, the present invention helps to directly understand the status of the current target task by obtaining the task requirement description information and task execution result information of each user's target task, wherein the task requirement description information refers to the user's specific requirements, expectations and related instructions for the target task, which includes the goals to be achieved by the task, the conditions to be met, the completion standards, etc. The task execution result information refers to the information about the actual results generated during the execution of the target task, including the task progress, completion quality, whether errors occurred, etc. Then, the industry specifications and standards are obtained, wherein the industry specifications and standards refer to a series of guidelines and standards that are generally recognized and followed in a specific industry, covering technical requirements, operating procedures, quality, etc. Standards, safety specifications, etc., which provide a unified reference scale for assessing task difficulty. Then, the differences between the task requirement description information and various aspects of industry standards and their impact on task difficulty are comprehensively considered, and a scoring system is adopted. For example, 1-10 points, 1 point indicates extremely low difficulty, and 10 points indicates extremely high difficulty. The specific score is determined based on factors such as the degree of compliance of the task with industry standards, the size of the difference, and the degree of impact on task execution, to obtain the task difficulty value. The task difficulty value refers to the difficulty combined with factors such as the skills, knowledge, resources required to complete the task, and the challenges that may be encountered. Then, the target task execution frequency and the cumulative execution frequency of multiple tasks are obtained based on past task information. The execution frequency refers to the number of times a specific target task has been executed in the past records. The cumulative execution frequency of multiple tasks refers to the cumulative execution times of multiple different tasks executed by the user within a certain period of time. It reflects the user's activity level and experience accumulation in overall task execution. Then, the single-task weighted value corresponding to the target task is obtained according to the target task execution frequency and the cumulative execution frequency of multiple tasks. Among them, the single-task weighted value = target task execution frequency / cumulative execution frequency of multiple tasks. By assigning a weight value to the target task in the task complexity assessment, the relative importance of the task in the user's overall task system can be measured. At the same time, the impact of task repetitiveness and multi-task parallelism on task complexity is quantified, which can more accurately reflect the actual performance of user tasks. The actual complexity is then determined by the scoring method based on past task information, task execution result information and industry specifications and standards, to obtain the corresponding task association score and task anomaly score. The task association score refers to the score obtained by evaluating the degree of association between the target task and other related tasks, and the task anomaly score refers to the score obtained by quantitatively evaluating the abnormal situations that occur during the execution of the target task. Finally, the task difficulty value, single task weighted value, task association score and task anomaly score are calculated through a specific formula to calculate the task complexity. This method integrates information from multiple dimensions to obtain a quantitative result that comprehensively reflects the complexity of user tasks, and this value can be used to intuitively understand the complexity of user tasks.

[0025] In one embodiment, step S3 of obtaining the interaction cooperation degree of each user according to the user service association data and the past task information includes: S31. Obtain multiple groups of cooperation degree evaluation features and the corresponding historical interaction cooperation degrees according to the user service association data, wherein the cooperation degree evaluation features include consultation frequency feature, consultation content feature, data integrity feature, and appointment punctuality feature; S32. Obtain the correlation coefficients corresponding to the consultation frequency feature, consultation content feature, data integrity feature, and appointment punctuality feature and the interaction cooperation degree respectively according to the user service association data, and obtain the interaction occupancy ratios corresponding to the consultation frequency feature, consultation content feature, data integrity feature, and appointment punctuality feature according to the correlation coefficients; S33. Obtain historical behavior information according to the past task information of each user, wherein the historical behavior information includes consultation frequency information, consultation content information, data integrity information, and appointment punctuality information; S34. Obtain judgment standard information, and judge the consultation frequency information, consultation content information, data integrity information, and appointment punctuality information according to the judgment standard information to obtain a key feature judgment set; S35. Obtain the interaction cooperation degree of each user according to the key feature judgment set and the interaction occupancy ratio.

[0026] As described in the above steps S31 - S35, in the present invention, by obtaining multiple groups of cooperation degree evaluation features and the corresponding historical interaction cooperation degrees, through the accumulation of historical data, the potential rules between user behavior features and interaction cooperation degrees can be discovered. Among them, the cooperation degree evaluation features refer to a series of specific attributes or factors used to measure the interaction cooperation degree between users and service providers, including consultation frequency features, consultation content features, data integrity features, and appointment punctuality features. Among them, the consultation frequency feature refers to the value reflecting the frequency of a user consulting service - related questions before or during receiving the service. The consultation content feature refers to the value focusing on the nature, depth, and pertinence degree of the user's consultation content. The data integrity feature refers to the value measuring whether the service - related data provided by the user, such as task background information, project requirement documents, etc., is complete. The appointment punctuality feature refers to the value evaluating the user's compliance with the appointed time during the appointment service process. The historical interaction cooperation degree refers to the historical record value of the cooperation degree between the user and the service provider comprehensively obtained based on the user's performance in each cooperation degree evaluation feature during the past service interaction process. Then, according to multiple groups of cooperation degree evaluation features and the corresponding historical interaction cooperation degree data, the Pearson correlation coefficient formula is used to calculate the correlation coefficients between the consultation frequency feature, consultation content feature, data integrity feature, and appointment punctuality feature and the interaction cooperation degree respectively. The value range of the correlation coefficient is between [-1, 1]. The closer the correlation coefficient is to 1, the stronger the positive correlation. The closer the correlation coefficient is to -1, the stronger the negative correlation. The closer the correlation coefficient is to 0, the weaker the linear correlation between the two. By quantifying the relationship between each feature and the interaction cooperation degree, the importance of each feature can be clarified, avoiding the arbitrariness of subjective judgment. Immediately afterwards, according to the obtained multiple correlation coefficients, the interaction proportion values corresponding to each feature are obtained respectively. Among them, the interaction proportion value refers to the proportion of each feature in determining the interaction cooperation degree. For example, if the correlation coefficient between the consultation frequency feature and the interaction cooperation degree is , the correlation coefficient between the consultation content feature and the interaction cooperation degree is , the correlation coefficient between the data integrity feature and the interaction cooperation degree is , and the correlation coefficient between the appointment punctuality feature and the interaction cooperation degree is , then the interaction proportion values corresponding to the consultation frequency feature, consultation content feature, data integrity feature, and appointment punctuality feature are respectively , , , , by determining the influence degree of each evaluation feature on the interaction cooperation degree, corresponding weights can be given to different features in the comprehensive evaluation, so as to more accurately reflect their contributions to the overall interaction cooperation degree. Then, historical behavior information of each user is extracted from the past task information. Here, the historical behavior information refers to the user behavior records of each user related to the cooperation degree evaluation features, including consultation frequency information, consultation content information, data integrity information, and appointment punctuality information. These information are specific descriptions of the actual behaviors of the current users and correspond to the cooperation degree evaluation features. By obtaining the current service behavior information of the users, the behavior performance of the users before this service can be understood in a timely manner, and the judgment standard information can be obtained. Here, the judgment standard information refers to a series of pre-set criteria and scales used to evaluate and judge the historical behavior information of the users. If the judgment standard information is "if the consultation frequency of the user is higher than three times per month, then this one-way feature is judged as 1; if the consultation content is mainly of in-depth professional type and service-related type, then this one-way feature is judged as 1; if the data integrity rate exceeds 80%, then this one-way feature is judged as 1; if the appointment punctuality rate exceeds 70%, then this one-way feature is judged as 1", by comparing the actual behaviors of the users with the standards, the performance of the users in each feature dimension can be determined, and a key feature judgment set is formed. Here, the key feature judgment set refers to the result set obtained by judging the service behavior information of the users according to the judgment standard information, such as (1, 1, 1, 1), (1, 1, 1, 0), (1, 1, 0, 0), etc. Finally, the interaction cooperation degree is obtained according to the key feature judgment set and the interaction occupancy ratio. If the key feature judgment set is (1, 0, 1, 1), the calculation formula of the interaction cooperation degree is: Interaction cooperation degree = , by combining the importance of each feature and the actual performance of the users in each feature, a value that can comprehensively reflect the interaction cooperation degree of the users is finally obtained, which can more carefully reflect the communication and cooperation characteristics of the users.

[0027] In one embodiment, the step S4 of obtaining the business experience value of each service provider according to the service provider information and obtaining the first estimated processing duration of the target task of each user according to the task complexity, interaction cooperation degree of each user, and the business experience value of the corresponding initial service provider includes: S41. Obtain the service evaluation information and service history information of each service provider according to the service provider information, and obtain the service evaluation value according to the service evaluation information; S42. Obtain the service success rate and service failure rate according to the service history information, and obtain the task processing ability value according to the service success rate and service failure rate; S43. Obtain industry business evaluation data, and obtain an evaluation weight coefficient according to the industry business evaluation data, where the evaluation weight coefficient includes a first weight coefficient corresponding to a service evaluation value and a second weight coefficient corresponding to a task processing ability value, and obtain a business experience value according to the service evaluation value, the first weight coefficient, the task processing ability value, and the second weight coefficient; S44. Obtain a basic service duration according to the service historical information, and calculate a first estimated processing duration according to the basic service duration, the task complexity, the interaction cooperation degree, and the business experience value, where the calculation formula is: ; Wherein, refers to the first estimated processing duration, refers to the basic service duration, refers to the business experience value, refers to the task complexity, refers to the interaction cooperation degree.

[0028] As described in the above steps S41 - S44, in the present invention, by obtaining the service evaluation information and service historical information of each service provider, where the service evaluation information refers to the feedback content given by users on the performance and service results of the service provider during the service process in the past service records of the service provider, including text descriptions, ratings, etc., and the service historical information is the detailed situation of the service provider providing services to users in the past, covering the service business type, the service method adopted, the final service result, etc. The service evaluation information can reflect the subjective feelings of users on the service process and results of the service provider, and accordingly obtain a service evaluation value. Through the service evaluation value, the degree of recognition of the service provider by users can be intuitively quantified. Then, the service success rate and service failure rate are obtained based on the service history information, where the service success rate refers to the ratio of the number of cases in which the service provider successfully completes the service within a certain period of time to the total number of service cases, and the service failure rate refers to the ratio of the number of cases in which the service provider encounters failures during the service process to the total number of service cases. The task processing capability value is obtained through the difference between the service success rate and the service failure rate, and the actual ability of the service provider to cope with various business tasks is quantified with the help of the task processing capability value. Then, the evaluation weight coefficient is obtained based on the industry business evaluation data, where the industry business evaluation data refers to various relevant materials that contain a comprehensive evaluation of the service provider's business level, covering the weight allocation information of the service evaluation value and the task processing capability value, that is, the first weight coefficient and the second weight coefficient. Based on these coefficients, combined with the service evaluation value and the task processing capability value, the business experience value is calculated through the weighted summation formula. Then, the basic service time is obtained based on the service history information, where the basic service time refers to the pre-set standard time value, which is used as the basis for calculating the estimated service time. Then, the first estimated processing time is calculated in combination with the task complexity, the degree of interaction and the business experience value. It is a benchmark value, which represents the basic time required for users to receive services under normal circumstances, without considering task complexity, interaction cooperation and business experience. It shows that the complexity of the task is positively correlated with the first estimated processing time. The more complex the task is, the more time the service provider needs to spend on service work. The interactive cooperation is negatively correlated with the first estimated processing time. The higher the interactive cooperation is, the clearer and more accurate the user can explain his needs to the service provider, helping the service provider to make quick judgments. It shows that the business experience value is negatively correlated with the first estimated processing time. Experienced service providers have sharper insights and can quickly identify key information and develop reasonable service plans. This calculation method fully considers the individual differences between different users and service providers, and helps to improve the accuracy of user service time prediction.

[0029] In one embodiment, the step S6 of grading the service providers according to the task completion rate to obtain a set of service providers ahead of schedule and a set of service providers behind schedule, and screening the users undertaken by all service providers behind schedule in the set of service providers behind schedule according to the reservation service information to obtain a set of transferable users includes: S61, determining whether the task completion rate of each service provider is greater than 1; If the task completion rate of the service provider is greater than 1, the service provider is determined to be an ahead-of-schedule service provider; If the task completion rate of the service provider is not greater than 1, the service provider is determined to be a service provider with lagging progress; S62. Obtain the redirection willingness options based on the user task information of the users undertaken by the service provider with a lagging progress; S63. Determine whether the redirection willingness options of the users undertaken by the service provider with a lagging progress are willing to be redirected; If the redirection willingness options of the user are willing to be redirected, then determine that the user is a user who can be redirected; If the redirection willingness options of the user are not willing to be redirected, then determine that the user is a non-redirected user.

[0030] As described in the above steps S61 - S63, in the present invention, by determining whether the task completion rate of each service provider is greater than 1, the service providers are classified into two categories: service providers with a leading progress and service providers with a lagging progress. If the task completion rate is greater than 1, it means that the actual service duration of the service provider on the current day exceeds the total expected processing duration, and there is still remaining service time after completing the established tasks. Therefore, it can be determined that this service provider is a service provider with a leading progress. If the task completion rate is not greater than 1, it indicates that the actual service duration of the service provider on the current day does not exceed the total expected processing duration, and the tasks undertaken cannot be completed within the specified time, resulting in a backlog of tasks. Therefore, it can be determined that the service provider is a service provider with a lagging progress. Such classification helps the online service platform clearly understand the expected work progress and expected task completion status of each service provider, providing a strong basis for subsequent human resource allocation and operation management. Immediately afterwards, for the users in charge of the service providers with a lagging progress, obtain their redirection willingness options. Among them, the redirection willingness options refer to the options set by the online service platform to understand whether the users are willing to accept being redirected to other service providers to receive services. This step aims to clarify the attitude and willingness of the users towards changing service providers or adjusting service arrangements, so as to decide whether to redirect them according to the users' willingness subsequently. By collecting the redirection willingness options, it can not only effectively solve the problem of task backlog of service providers, but also take the user experience and needs into full consideration to the greatest extent, practice the service concept centered on users, avoid forced redirection without considering the users' willingness, and thus avoid situations that may cause user dissatisfaction and affect the service experience. Then, make a judgment based on the users' redirection willingness options. For users who are willing to be redirected, determine them as users who can be redirected, which means that these users can be reasonably redirected and arranged to receive services from other service providers, so as to relieve the work pressure of the service providers with a lagging progress and optimize the allocation of online service resources. For users who are not willing to be redirected, determine them as non-redirected users. The online service platform will respect their willingness, not perform forced redirection, and continue to maintain the original service arrangement, effectively protecting the service rights and independent options of users.

[0031] In one embodiment, step S7 of obtaining a second estimated processing duration according to the set of assignable users and the set of service providers with advanced progress, and performing task assignment on each assignable user according to the second estimated processing duration and the task completion rate to generate a resource allocation plan includes: S71. Arrange and combine the multiple assignable users in the set of assignable users and the service providers with advanced progress in the set of service providers with advanced progress to obtain a set of deployable combinations, and obtain the second estimated processing duration of each deployable combination according to the set of deployable combinations; S72. Obtain the task compliance time and the remaining service duration according to the task completion rate and the daily service duration of the service providers with advanced progress, and obtain the total service time of each service provider with advanced progress according to the task compliance time, the second estimated processing duration, and the set of deployable combinations; S73. Obtain the average service time according to the total service time of each service provider with advanced progress, and obtain the objective function according to the average service time and the total service time; S74. Obtain constraint condition information according to the set of assignable users, the set of service providers with advanced progress, and the remaining service duration, and obtain an adaptable service combination from the set of deployable combinations according to the constraint condition information and the objective function to form a resource allocation plan.

[0032] As described in the above steps S71 - S74, the present invention generates a set of deployable combinations by permuting and combining the reassignable users and the service providers with advanced progress, so as to comprehensively consider all potential deployment schemes. Among them, the set of deployable combinations refers to the set obtained after all possible permutations and combinations of multiple reassignable users in the reassignable user set and the service providers with advanced progress in the service provider set with advanced progress. Then, according to a calculation method similar to step S44, the second estimated processing duration of each deployable combination is calculated. With the second estimated processing duration, it is possible to know the time required for the service provider to complete the target tasks of the reassignable users under different combinations, thereby providing a reference basis in terms of time for subsequent deployment decisions, so as to screen out the most reasonable deployment method and enable the user's tasks to be processed in a timely and effective manner. Subsequently, according to the task completion rate and the daily service duration of the service providers with advanced progress, the task compliance duration and the remaining service duration of each service provider with advanced progress are determined. Among them, the task compliance duration refers to the time expected to be consumed for the service provider with advanced progress to complete the tasks originally undertaken by it, and the remaining service duration refers to the time remaining for the service provider with advanced progress to handle other tasks after completing the original tasks. The task compliance duration = daily service duration / task completion rate, and the remaining service duration = daily service duration - task compliance duration. Through the remaining service duration, it is possible to accurately evaluate the time that each service provider with advanced progress can still use to receive reassignable users after completing the established tasks. Then, in combination with the second estimated processing duration and the set of deployable combinations, the total service time of each service provider with advanced progress under different deployment schemes is calculated. Among them, the total service time refers to the overall service duration of each service provider with advanced progress after receiving the deployed users. The calculation formula for the total service time is: , where represents the total service time of the th service provider with advanced progress, represents the task compliance duration of the th service provider with advanced progress, represents the number of reassignable users assigned to the th service provider with advanced progress for service, represents the serial number of the reassignable user, represents the th reassignable user and the second estimated processing duration corresponding to the th service provider with advanced progress, represents the th reassignable user and the th service provider with advanced progress for the adapted service combination. Then, the average service time of each service provider with advanced progress is calculated, and based on this, an objective function is constructed. Among them, the calculation formula for the average service time is: , where represents the average service time, represents the number of service providers with advanced progress, Indicates the serial number of the service provider with an advanced progress, indicating the total service time of the -th service provider with an advanced progress, and then according to the average service time and the total service time of the service provider with an advanced progress after receiving the transferable users, obtain the objective function: , the objective function is to optimize the allocation plan through a mathematical model to achieve the optimal goal of balancing the workload of service providers. Then, according to the set of transferable users and the set of service providers with an advanced progress, obtain the number of transferable users and the number of service providers with an advanced progress respectively, and obtain the constraint condition information based on these data. Among them, the constraint condition information refers to the relevant content of various restrictive conditions that need to be considered when transferring transferable users. The constraint condition information is: 1. , indicating that for each transferable user ( from 1 to ), it can only be assigned to one of the service providers with an advanced progress; 2. , indicating that for each service provider with an advanced progress ( from 1 to ), the sum of the second estimated consultation durations of all transferable users under its responsibility cannot exceed the remaining service duration of this service provider with an advanced progress; 3. , indicating that can only take values of 0 or 1. Among them, 1 represents transferring the transferable user to the service provider with an advanced progress for consultation, and 0 represents not transferring the transferable user to the service provider with an advanced progress for consultation. Then, combine the objective function and the constraint conditions and use the PuLP library of Python to solve the adapted service combination, that is, on the basis of meeting various actual constraint conditions, find the transfer plan that makes the objective function reach the optimal, so as to ensure that the allocation result not only conforms to the actual situation and various rules of the online service platform, but also can achieve the goal of optimizing the online resource allocation.

[0033] This application also provides an online resource management system, including: The first acquisition module is used to acquire the service interaction information of the service department. Among them, the service interaction information includes service provider information and user task information; The second acquisition module is used to acquire the target task information and past task information of each user according to the user task information, and acquire the task complexity of the target task of each user according to the target task information and past task information; The third acquisition module is used to acquire user service related data according to user task information, and acquire the interaction cooperation degree of each user according to the user service related data and past task information; A fourth acquisition module is used to acquire the business experience value of each service provider according to the service provider information, and acquire the first estimated processing time of each user's target task according to the task complexity, interaction cooperation degree and business experience value of the corresponding initial service provider of each user; A fifth acquisition module, used to acquire the service duration of each service provider on the same day according to the service provider information, and acquire the task completion rate according to the service duration of each service provider on the same day and the first estimated processing duration of the corresponding undertaking user; A screening module, which grades the service providers according to the task completion rate to obtain a set of service providers ahead of schedule and a set of service providers behind schedule, and screens all users undertaken by the service providers behind schedule in the set of service providers behind schedule according to the reservation service information to obtain a set of transferable users; The allocation module obtains a second estimated processing time according to the set of assignable users and the set of service providers ahead of schedule, and assigns tasks to each assignable user according to the second estimated processing time and the task completion rate, and generates a resource allocation plan, wherein the resource allocation plan includes multiple assignment service combinations.

[0034] In one embodiment, the deployment module includes: a permutation and combination unit, configured to permutate and combine a plurality of assignable users in the assignable user set and the ahead-of-schedule service providers in the ahead-of-schedule service provider set to obtain an arbitrably configured combination set, and to obtain a second estimated processing time for each arbitrably configured combination according to the arbitrably configured combination set; A first acquisition unit is used to acquire the task completion time and the remaining service time according to the task completion rate and the service time of the service provider ahead of schedule, and acquire the total service time of each service provider ahead of schedule according to the task completion time, the second estimated processing time and the deployable combination set; A second acquisition unit is used to acquire an average service time according to the total service time of each server ahead of schedule, and acquire an objective function according to the average service time and the total service time; The third acquisition unit is used to obtain constraint information according to the set of transferable users, the set of ahead-of-schedule service providers and the remaining service duration, and to obtain an adapted service combination from the set of deployable combinations according to the constraint information and the objective function to form a resource deployment plan.

[0035] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and the steps of the above-mentioned online resource management method are implemented when the processor executes the computer program.

[0036] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and the steps of the above-mentioned online resource management method are implemented when the computer program is executed by a processor.

[0037] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0038] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article, or method including that element.

[0039] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An online resource management method, characterized in that: include: Acquire service interaction information of the service department, wherein the service interaction information includes service provider information and user task information; Obtaining target task information and past task information of each user according to the user task information, and obtaining the task complexity of the target task of each user according to the target task information and the past task information; Obtain user service related data based on user task information, and obtain each user's interactive cooperation degree based on the user service related data and past task information; Obtaining the business experience value of each service provider according to the service provider information, and obtaining the first estimated processing time of each user's target task according to the task complexity, interaction cooperation degree and business experience value of the corresponding initial service provider of each user; Obtaining the service time of each service provider on that day according to the service provider information, and obtaining the task completion rate according to the service time of each service provider on that day and the first estimated processing time of the corresponding undertaking user; The service providers are graded according to the task completion rate to obtain a set of service providers ahead of schedule and a set of service providers behind schedule, and all users undertaken by the service providers behind schedule in the set of service providers behind schedule are screened according to the reservation service information to obtain a set of transferable users; A second estimated processing time is obtained according to the set of assignable users and the set of ahead-of-progress service providers, and tasks are assigned to each assignable user according to the second estimated processing time and the task completion rate to generate a resource allocation plan, wherein the resource allocation plan includes multiple assignment service combinations.

2. An online resource management method according to claim 1, characterized in that: The step of obtaining the task complexity of each user's target task according to the target task information and the past task information includes: Acquire task requirement description information and task execution result information according to the target task information; Obtain industry standards, and obtain the task difficulty value of each user's target task based on the industry standards and task requirement description information; Acquire the target task execution frequency and the cumulative execution frequency of multiple tasks according to the past task information, and acquire the single task weight value corresponding to the target task according to the target task execution frequency and the cumulative execution frequency of multiple tasks; Obtaining a task association score value based on the past task information and industry specification standards, and obtaining a task anomaly score value based on the task execution result information and industry specification standards; The task complexity is obtained according to the task difficulty value, the single task weight value, the task association score value and the task anomaly score value.

3. An online resource management method according to claim 1, characterized in that: The step of obtaining the interaction cooperation degree of each user according to the user service association data and the past task information includes: Acquire multiple groups of cooperation evaluation features and corresponding historical interaction cooperation degrees according to the user service association data, wherein the cooperation evaluation features include consultation frequency features, consultation content features, data integrity features and appointment time features; Obtain, according to the user service-related data, the correlation coefficients corresponding to the consultation frequency characteristics, consultation content characteristics, information integrity characteristics and appointment time characteristics and the interactive cooperation degree, respectively, and obtain the interactive proportion values ​​corresponding to the consultation frequency characteristics, consultation content characteristics, information integrity characteristics and appointment time characteristics according to the correlation coefficients; Acquire historical behavior information according to the past task information of each user, wherein the historical behavior information includes consultation frequency information, consultation content information, data integrity information and appointment time information; Acquire judgment standard information, and judge the consultation frequency information, consultation content information, data integrity information and appointment time information according to the judgment standard information to obtain a key feature judgment set; The interaction cooperation degree of each user is obtained according to the key feature determination set and the interaction ratio value.

4. The online resource management method according to claim 1, characterized in that: The step of obtaining the business experience value of each service provider according to the service provider information, and obtaining the first estimated processing time of each user's target task according to the task complexity, interaction cooperation degree and business experience value of the corresponding initial service provider of each user, includes: Acquire service evaluation information and service history information of each service provider according to the service provider information, and acquire a service evaluation value according to the service evaluation information; Acquire a service success rate and a service failure rate according to the service history information, and acquire a task processing capability value according to the service success rate and the service failure rate; Acquire industry business evaluation data, and acquire an evaluation weight coefficient according to the industry business evaluation data, wherein the evaluation weight coefficient includes a first weight coefficient corresponding to the service evaluation value and a second weight coefficient corresponding to the task processing capability value, and acquire a business experience value according to the service evaluation value, the first weight coefficient, the task processing capability value, and the second weight coefficient; The basic service duration is obtained according to the service history information, and the first estimated processing duration is obtained according to the basic service duration, task complexity, interaction cooperation degree and business experience value.

5. The online resource management method according to claim 1, characterized in that: The step of grading the service providers according to the task completion rate to obtain a set of service providers ahead of schedule and a set of service providers behind schedule, and screening all users undertaken by the service providers behind schedule in the set of service providers behind schedule according to the reservation service information to obtain a set of transferable users includes: Determining whether the task completion rate of each service provider is greater than 1; If the task completion rate of the service provider is greater than 1, the service provider is determined to be an ahead-of-schedule service provider; If the task completion rate of the service provider is not greater than 1, the service provider is determined to be a service provider with lagging progress; Obtaining a transfer intention option according to the user task information of the user undertaken by the service provider with lagging progress; Determine whether the transfer intention option of the user undertaken by the service provider with lagging progress is willing to transfer; If the transfer intention option of the user is willing to transfer, the user is determined to be a transferable user; If the transfer intention option of the user is not willing to transfer, the user is determined to be a non-transfer user.

6. An online resource management method according to claim 1, characterized in that: The step of obtaining a second estimated processing time according to the set of assignable users and the set of servers ahead of schedule, and assigning tasks to each assignable user according to the second estimated processing time and the task completion rate to generate a resource allocation plan includes: Arrange and combine a plurality of assignable users in the assignable user set and the ahead-of-schedule service providers in the ahead-of-schedule service provider set to obtain an arbitrably configured combination set, and obtain a second estimated processing time of each arbitrably configured combination according to the arbitrably configured combination set; Obtain the task completion time and remaining service time according to the task completion rate and the service time of the service provider ahead of schedule, and obtain the total service time of each service provider ahead of schedule according to the task completion time, the second estimated processing time and the deployable combination set; Obtain an average service time according to the total service time of each service provider ahead of schedule, and obtain an objective function according to the average service time and the total service time; Constraint information is obtained according to the set of assignable users, the set of ahead-of-schedule service providers, and the remaining service duration, and an adapted service combination is obtained from the set of deployable combinations according to the constraint information and the objective function to form a resource deployment plan.

7. An online resource management system, characterized in that: include: A first acquisition module, used to acquire service interaction information of a service department, wherein the service interaction information includes service provider information and user task information; A second acquisition module is used to acquire target task information and past task information of each user according to the user task information, and acquire the task complexity of the target task of each user according to the target task information and the past task information; The third acquisition module is used to acquire user service related data according to the user task information, and acquire the interaction cooperation degree of each user according to the user service related data and the past task information; A fourth acquisition module is used to acquire the business experience value of each service provider according to the service provider information, and acquire the first estimated processing time of each user's target task according to the task complexity, interaction cooperation degree and business experience value of the corresponding initial service provider of each user; A fifth acquisition module, used to acquire the service duration of each service provider on the same day according to the service provider information, and acquire the task completion rate according to the service duration of each service provider on the same day and the first estimated processing duration of the corresponding undertaking user; A screening module, which grades the service providers according to the task completion rate to obtain a set of service providers ahead of schedule and a set of service providers behind schedule, and screens all users undertaken by the service providers behind schedule in the set of service providers behind schedule according to the reservation service information to obtain a set of transferable users; The allocation module obtains a second estimated processing time according to the set of assignable users and the set of service providers ahead of schedule, and assigns tasks to each assignable user according to the second estimated processing time and the task completion rate, and generates a resource allocation plan, wherein the resource allocation plan includes multiple assignment service combinations.

8. An online resource management system according to claim 7, characterized in that: The deployment module includes: a permutation and combination unit, configured to permutate and combine a plurality of assignable users in the assignable user set and the ahead-of-schedule service providers in the ahead-of-schedule service provider set to obtain an arbitrably configured combination set, and to obtain a second estimated processing time for each arbitrably configured combination according to the arbitrably configured combination set; A first acquisition unit is used to acquire the task completion time and the remaining service time according to the task completion rate and the service time of the service provider ahead of schedule, and acquire the total service time of each service provider ahead of schedule according to the task completion time, the second estimated processing time and the deployable combination set; A second acquisition unit is used to acquire an average service time according to the total service time of each server ahead of schedule, and acquire an objective function according to the average service time and the total service time; The third acquisition unit is used to obtain constraint information according to the set of transferable users, the set of ahead-of-schedule service providers and the remaining service duration, and to obtain an adapted service combination from the set of deployable combinations according to the constraint information and the objective function to form a resource deployment plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, 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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