An online resource management method and system

Abstract

The present invention relates to the technical field of online resource management, and particularly relates to an online resource management method and system. In the present invention, by comprehensively considering the task complexity of the user's target task, the interaction and cooperation degree with the service provider, and the business experience value of the corresponding reserved service provider, the first estimated processing duration is calculated. Then, in combination 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. According to the task completion rate, the service providers are divided into two categories: service providers with a leading progress and service providers with a lagging progress. Subsequently, the users that can be reassigned and are in charge of by the service providers with a lagging progress are screened out. Finally, the second estimated processing duration is calculated based on the set of users that can be reassigned and the set of service providers that meet the progress standard, and multiple reassignment service combinations are obtained in combination with the task completion rate. With the help of these reassignment service combinations, the tasks of the users that can be reassigned are reassigned, so that the users can obtain a more reasonable service arrangement.

Description

Technical Field

[0001] The present invention relates to the technical field of online resource management, and particularly relates 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 is 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.

[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 at the same time cause congestion in service time periods. 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, neither meeting user needs nor being 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:

[0006] Obtaining service interaction information of a service department, where the service interaction information includes service provider information and user task information;

[0007] 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;

[0008] 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;

[0009] 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;

[0010] 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;

[0011] 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 the 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;

[0012] 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.

[0013] 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:

[0014] Obtain the task requirement expression information and task execution result information according to the target task information;

[0015] Obtain the industry specification standards, and obtain the task difficulty value of each user's target task according to the industry specification standards and the task requirement expression information;

[0016] 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;

[0017] Obtain the task association score value according to the past task information and the industry specification standards, and obtain the task exception score value according to the task execution result information and the industry specification standards;

[0018] 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:

[0019] ;

[0020] Among them, represents the task complexity, represents the task difficulty value of the th target task, Indicates the serial number of the target task, Indicates the single-task weighted value of the th target task, Indicates the task correlation score value, Indicates the task anomaly score value.

[0021] Preferably, the step of obtaining the interaction cooperation degree of each user according to the user service association data and past task information includes:

[0022] Obtain 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 punctuality feature;

[0023] 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 occupation ratios corresponding to the consultation frequency feature, consultation content feature, data integrity feature and appointment punctuality feature according to the correlation coefficients;

[0024] 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;

[0025] Obtain the 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 the key feature judgment set;

[0026] Obtain the interaction cooperation degree of each user according to the key feature judgment set and the interaction occupation ratio.

[0027] 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:

[0028] 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;

[0029] 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;

[0030] 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;

[0031] Obtain a basic service duration according to the service history 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:

[0032] ;

[0033] 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.

[0034] 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 that can be reassigned includes:

[0035] Judge whether the task completion rate of each service provider is greater than 1;

[0036] 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;

[0037] 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;

[0038] Obtain a reassignment willingness option according to the user task information of the users undertaken by the service providers with lagging progress;

[0039] Judge whether the reassignment willingness option of the users undertaken by the service providers with lagging progress is willing to be reassigned;

[0040] If the reassignment willingness option of the user is willing to be reassigned, it is determined that the user is a user that can be reassigned;

[0041] If the reassignment willingness option of the user is not willing to be reassigned, it is determined that the user is a non-reassignable user.

[0042] Preferably, the step of obtaining the second estimated processing duration according to the set of transferable users and the set of service providers with advanced progress, and performing task transfer for each transferable user according to the second estimated processing duration and the task completion rate to generate a resource allocation plan includes:

[0043] Arrange and combine multiple transferable users in the set of transferable users 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;

[0044] Obtain the task compliance time and 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;

[0045] 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;

[0046] 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 to form a resource allocation plan.

[0047] The present application also provides an online resource management system, including:

[0048] 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;

[0049] A second acquisition module, configured 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 the past task information;

[0050] 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;

[0051] 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, the interaction cooperation degree of each user, and the business experience value of the corresponding initial service provider;

[0052] A fifth acquisition module, configured to obtain the daily service duration of each service provider according to the service provider information, and obtain a task completion rate according to the daily service duration of each service provider and the first estimated processing duration of the corresponding receiving user;

[0053] 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 reserved service information to obtain a set of users that can be reassigned;

[0054] A deployment module, which obtains a 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 deployment plan, where the resource deployment plan includes multiple reassignment service combinations.

[0055] Preferably, the deployment module includes:

[0056] 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 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;

[0057] A first acquisition unit, configured to obtain the task compliance time and the remaining service duration according to the task completion rate and the daily service duration of 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;

[0058] A second acquisition unit, configured to obtain the average service time according to the total service time of each service provider with advanced progress, and obtain an objective function according to the average service time and the total service time;

[0059] A third acquisition unit, configured to obtain constraint condition information according to the set of users that can be reassigned, 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 to form a resource deployment plan.

[0060] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above online resource management method are implemented.

[0061] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above online resource management method are implemented.

[0062] 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 on the online service platform, comprehensively grasping the situations 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 and the number of users undertaken by each service provider, 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 the 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 the 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. After clarifying the service providers and user groups that need to be coordinated, the online resource allocation can be made more targeted. Finally, the second estimated processing duration is calculated according to the set of users who can be reassigned and the set of service providers with qualified progress, and the users who can be reassigned are reassigned according to the second estimated processing duration and the task completion rate to obtain multiple reassigned service combinations. With the reassigned service combinations, it is possible to clearly determine which service provider with advanced progress each user who can be reassigned should be reassigned to for task processing, thereby enabling users to obtain more reasonable service arrangements, improving the utilization rate of online resources, reducing the user waiting time, and enhancing the user service experience and the service quality of the online service platform. Brief Description of the Drawings

[0063] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present application.

[0064] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present application.

[0065] Figure 3 It is a schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0066] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0067] 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.

[0068] Such asFigures 1-3 As shown, the present application provides an online resource management method, comprising:

[0069] S1. Acquire service interaction information of a service department, wherein the service interaction information includes service provider information and user task information;

[0070] S2. 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 past task information;

[0071] S3. Obtain user service related data according to user task information, and obtain the interaction cooperation degree of each user according to the user service related data and past task information;

[0072] S4. 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;

[0073] S5. Obtain the service time of each service provider on that day according to the service provider information, and obtain 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;

[0074] S6, classifying 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;

[0075] S7. Obtain a second estimated processing time according to the set of assignable users and the set of service providers ahead of schedule, and assign tasks to each assignable user according to the second estimated processing time and the task completion rate, and generate a resource allocation plan, wherein the resource allocation plan includes multiple assignment service combinations.

[0076] 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 service efficiency 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 have not been 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 the proportion of users with long - time service requirements received by a service provider is relatively large, it is difficult for the service provider to complete service tasks on time. Hasty service will affect service quality and 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 in some time periods of the service provider, making it difficult to rationally allocate service resources, unable to meet user needs and not conducive to improving enterprise service quality. 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, and objectives 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 task that needs to be completed currently clearly defined, which clarifies the core goal and direction of the service required by the user. 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, whereThe interaction cooperation degree refers to the value for evaluating the degree of cooperation between the user and the service provider during the service process. This value reflects the user's understanding and collaboration ability of the service process. 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. Subsequently, according to the service provider information, the business experience value of each service provider is obtained. Among them, the business experience value refers to the numerical value used to measure the richness of experience of the service provider 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 refers to 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. Subsequently, 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 th service provider's daily service duration, refers to the th service provider's number of users undertaken, The first estimated consultation duration corresponding to each service provider can intuitively reflect the likelihood of each service provider completing service tasks under the current business volume distribution through the task completion rate, facilitating the timely detection of situations such as overloaded tasks or idle resources for 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 the 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 deployment arrangement information refers to the overall plan formed after the tasks of each user who can be reassigned are reassigned, including detailed content 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.

[0077] 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:

[0078] S21. Obtain task requirement expression information and task execution result information according to the target task information;

[0079] 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;

[0080] S23. Obtain the target task execution frequency and the cumulative multi-task execution frequency according to the past task information, and obtain the single-task weighted value corresponding to the target task according to the target task execution frequency and the cumulative multi-task execution frequency;

[0081] S24. Obtain the task association score value according to the past task information and industry standard specifications, and obtain the task exception score value according to the task execution result information and industry standard specifications;

[0082] S25. Calculate the task complexity according to the task difficulty value, the single-task weighted value, the task association score value, and the task exception score value, where the calculation formula is:

[0083] ;

[0084] 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 th single-task weighted value of the target task, represents the task association score value, represents the task exception score value.

[0085] As described in the above steps S21 - S25, in the present invention, by obtaining the task requirement expression information and task execution result information of each user's target task, it helps to directly understand the status of the current target task. Among them, the task requirement expression information refers to information such as the specific requirements, expectations, and related descriptions of the user 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 information about the actual results generated during the execution of the target task, including the task progress, completion quality, whether errors occur, etc. Then, the industry standard norms are obtained. Among them, the industry standard norms refer to a series of guidelines and standards that are generally recognized and followed within a specific industry, covering aspects such as technical requirements, operation procedures, quality standards, safety specifications, etc., which provide a unified reference scale for evaluating the task difficulty. Immediately afterwards, comprehensively considering the differences in various aspects between the task requirement expression information and the industry standard norms and their impacts on the task difficulty, a scoring method is adopted, for example, from 1 - 10 points, where 1 point indicates extremely low difficulty and 10 points indicates extremely high difficulty. The specific score is determined based on factors such as the compliance degree of the task with the industry standard norms, the size of the differences, and the impact degree on the task execution, to obtain the task difficulty value. Among them, the task difficulty value refers to the degree of difficulty that comprehensively reflects factors such as the skills, knowledge, resources required to complete the task, and the possible challenges encountered. Then, based on the past task information, the execution frequency of the target task and the cumulative execution frequency of multiple tasks are obtained. Among them, the execution frequency of the target task refers to the number of times a specific target task has been executed in the past records, and the cumulative execution frequency of multiple tasks refers to the cumulative number of times that multiple different tasks executed by the user within a certain period of time, which reflects the user's activity level and experience accumulation in overall task execution. Then, based on the execution frequency of the target task and the cumulative execution frequency of multiple tasks, the single - task weighted value corresponding to the target task is obtained, where the single - task weighted value = execution frequency of the target task / cumulative execution frequency of multiple tasks. By assigning a weight value to the target task in the task complexity assessment, the relative importance of this task in the user's overall task system can be measured, and at the same time, the influence of task repeatability and multi - task parallelism on the task complexity is quantified, which can more accurately reflect the actual complexity of the user's task. Then, respectively based on the past task information, task execution result information, and industry standard norms, a scoring method is adopted to obtain the corresponding task correlation score value and task anomaly score value. Among them, the task correlation score value refers to the score obtained by evaluating the correlation degree between the target task and other related tasks, and the task anomaly score value refers to the score obtained by quantitatively evaluating the abnormal situations that occur during the execution of the target task. Finally, the task complexity is calculated by a specific formula using the task difficulty value, single - task weighted value, task correlation score value, and task anomaly score value. This method synthesizes information from multiple dimensions to obtain a quantitative result that comprehensively reflects the complexity of the user's task, and through this value, the complexity of the user's task can be intuitively understood.

[0086] In one embodiment, step S3 of obtaining the interaction cooperation degree of each user according to the user service association data and past task information includes:

[0087] S31. Obtain 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 punctuality feature;

[0088] S32. Obtain the correlation coefficients corresponding to the interaction cooperation degree of the consultation frequency feature, consultation content feature, data integrity feature, and appointment punctuality feature 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;

[0089] 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;

[0090] 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;

[0091] S35. Obtain the interaction cooperation degree of each user according to the key feature judgment set and the interaction occupancy ratio.

[0092] 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 characteristics 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 compliance features. Among them, the consultation frequency feature refers to the value reflecting the frequency of a user consulting service - related issues before or during receiving services. 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 compliance 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 compliance 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 compliance 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 compliance feature are , , , , 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 related to the cooperation degree evaluation features of each user, 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 guidelines 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 and service-related types, 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 after 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.

[0093] 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:

[0094] 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;

[0095] 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;

[0096] 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;

[0097] 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:

[0098] ;

[0099] 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.

[0100] 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 the user regarding 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 the user about 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 the user can be intuitively quantified.

[0101] 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.

[0102] 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:

[0103] S61, determining whether the task completion rate of each service provider is greater than 1;

[0104] 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;

[0105] 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 a lagging progress;

[0106] S62. Obtain the reassignment willingness options according to the user task information of the users undertaken by the service provider with a lagging progress;

[0107] S63. Determine whether the reassignment willingness option of the user undertaken by the service provider with a lagging progress is willing to be reassigned;

[0108] If the reassignment willingness option of the user is willing to be reassigned, it is determined that the user is a user who can be reassigned;

[0109] If the reassignment willingness option of the user is not willing to be reassigned, it is determined that the user is a non-reassignable user.

[0110] 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 estimated 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 estimated processing duration, and the tasks undertaken cannot be completed within the specified time, and there is 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 estimated work progress and estimated 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 reassignment willingness options. Among them, the reassignment willingness option refers to the option set by the online service platform to understand whether the user is willing to accept reassignment to other service providers to receive services. This step aims to clarify the attitude and willingness of the user towards replacing the service provider or adjusting the service arrangement, so as to decide whether to reassign the user according to the user's willingness subsequently. By collecting the reassignment 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 account to the greatest extent, practice the service concept centered on users, avoid forced reassignment without considering the user's willingness, and thus avoid the situation of causing user dissatisfaction and affecting the service experience. Then, judge according to the user's reassignment willingness option. For users who are willing to be reassigned, they are determined as users who can be reassigned, which means that these users can be reasonably reassigned 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 reassigned, they are determined as non-reassignable users, and the online service platform will respect their wishes, not conduct forced reassignment, and continue to maintain the original service arrangement, effectively protecting the service rights and interests and the right of independent choice of users.

[0111] In one embodiment, step S7 of obtaining a second predicted processing duration according to the set of transferable users and the set of service providers with advanced progress, and performing task transfer for each transferable user according to the second predicted processing duration and the task completion rate to generate a resource allocation plan includes:

[0112] S71. Arrange and combine multiple transferable users in the set of transferable users 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 predicted processing duration of each deployable combination according to the set of deployable combinations;

[0113] S72. Obtain the task compliance time and 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 predicted processing duration, and the set of deployable combinations;

[0114] 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;

[0115] S74. 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 to form a resource allocation plan.

[0116] As described in the above steps S71-S74, the present invention generates an allocable combination set by arranging and combining the transferable users and the ahead-of-schedule service providers, so as to comprehensively consider all potential allocation schemes, wherein the allocable combination set refers to the set obtained by allocating and combining multiple transferable users in the transferable user set with the ahead-of-schedule service providers in the ahead-of-schedule service provider set in all possible ways, and then the second estimated processing time of each allocable combination is calculated according to the calculation method similar to step S44. With the help of the second estimated processing time, it is possible to know the time required for the service provider to complete the target task of the transferable user under different combinations, thereby providing a time reference for subsequent allocation decisions, so as to screen out the most reasonable allocation method and allow the user's task to be processed in a timely and effective manner, and then, according to the task completion rate of the ahead-of-schedule service provider and the service time on that day, the allocation decision is made. The task-compliance time and remaining service time of each service provider ahead of schedule are determined, where the task-compliance time refers to the estimated time required for the service provider ahead of schedule to complete the task it originally undertook, and the remaining service time refers to the time remaining for the service provider ahead of schedule to handle other tasks after completing the original task. The task-compliance time = the service time of the day / the task completion rate, and the remaining service time = the service time of the day - the task-compliance time. The remaining service time can accurately assess the time that each service provider ahead of schedule can use to receive the transferable users after completing the given task. Combined with the second estimated processing time and the deployable combination set, the total service time of each service provider ahead of schedule under different deployment plans is calculated, where the total service time refers to the overall service time of each service provider ahead of schedule after receiving the deployed users. The calculation formula for the total service time is: ,in, Indicates The total service time of the ahead-of-time service providers, Indicates The time it takes for the service provider who is ahead of schedule to reach the target. Indicates that the The number of users that can be transferred to the service provider that is ahead of schedule, Indicates the serial number of the transferable user. Indicates transferable users and The second estimated processing time corresponding to the service provider ahead of schedule, Indicates transferable users and The adaptive service combination of the ahead-of-time service providers is then calculated, and the average service time of each ahead-of-time service provider is then calculated, and the objective function is constructed accordingly. The calculation formula for the average service time is: ,in, represents the average service time, Indicates the number of servers ahead of schedule, Indicates the serial number of the service provider with a progress ahead, Indicates the total service time of the -th service provider with a progress ahead, and then, based on the average service time and the total service time of the service provider with a progress ahead after receiving the transferable users, the objective function is obtained: , and 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 a progress ahead, the number of transferable users and the number of service providers with a progress ahead are respectively obtained, and based on these data, constraint condition information is obtained. Among them, the constraint condition information refers to the relevant content of various limiting conditions that need to be considered when transferring transferable users. The constraint condition information is: 1. ( from 1 to ), can only be assigned to one of the service providers with a progress ahead; 2. , indicating that for each service provider with a progress ahead ( from 1 to ), the sum of the second estimated consultation durations of all transferable users it is responsible for cannot exceed the remaining service duration of this service provider with a progress ahead; 3. , indicating that the value range of can only be 0 or 1. Among them, 1 represents transferring the transferable user to the service provider with a progress ahead for consultation, and 0 represents not transferring the transferable user to the service provider with a progress ahead for consultation. Then, combining the objective function and the constraint conditions, the Python PuLP library is used to solve the adapted service combination, that is, on the basis of meeting various actual constraint conditions, a transfer plan that makes the objective function reach the optimal is found, which can 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.

[0117] This application also provides an online resource management system, including:

[0118] 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;

[0119] A second acquisition module, configured 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;

[0120] 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;

[0121] 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;

[0122] 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;

[0123] 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;

[0124] 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.

[0125] In one embodiment, the deployment module includes:

[0126] 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;

[0127] 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;

[0128] 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;

[0129] A third acquisition unit, configured to 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 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.

[0130] 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.

[0131] 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.

[0132] 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 the memory, storage, database, or other media provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. The volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM can be obtained in various 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.

[0133] 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 expressly listed, or further includes elements inherent to such 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.

[0134] 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 content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. An online resource management method, characterized in that, Including: Obtain service interaction information of the service department, where the service interaction information includes service provider information and user task information; 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; 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; 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; 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.

2. The online resource management method according to claim 1, wherein The step of obtaining the task complexity of the target task of each user according to the target task information and past task information includes: Obtain task requirement expression information and task execution result information according to the target task information; Obtain industry standard specifications, and obtain the task difficulty value of the target task of each user according to the industry standard specifications and 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 industry standard specifications, and obtain the task exception score value according to the task execution result information and industry standard specifications; Obtain the task complexity according to the task difficulty value, single-task weighted value, task association score value and task exception score value.

3. The 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 past task information includes: Obtain multiple groups of cooperation degree evaluation features and the corresponding historical interaction cooperation degrees according to the user service association data, where the cooperation degree evaluation features include consultation frequency feature, consultation content feature, data integrity feature and appointment punctuality feature; Obtain the correlation coefficients corresponding to the interaction cooperation degrees for the consultation frequency feature, consultation content feature, data integrity feature, and appointment fulfillment feature respectively based on 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 fulfillment feature according to the correlation coefficients; Obtain historical behavior information based on the past task information of each user, where the historical behavior information includes consultation frequency information, consultation content information, data integrity information, and appointment fulfillment information; Obtain the determination standard information, and determine the consultation frequency information, consultation content information, data integrity information, and appointment fulfillment information according to the determination standard information to obtain the key feature determination set; Obtain the interaction cooperation degree of each user according to the key feature determination set and the interaction occupancy ratio.

4. An 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 duration of the target task of each user according to the task complexity, interaction cooperation degree, and business experience value of the corresponding initial service provider of each user includes: 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; 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; Obtain the industry business evaluation data, and obtain the evaluation weight coefficients according to the industry business evaluation data, where the evaluation weight coefficients include the first weight coefficient corresponding to the service evaluation value and the second weight coefficient corresponding to the task processing ability value, and obtain the business experience value according to the service evaluation value, first weight coefficient, task processing ability value, and second weight coefficient; Obtain the basic service duration according to the service history information, and obtain the first estimated processing duration according to the basic service duration, task complexity, interaction cooperation degree, and business experience value.

5. A method for online resource management according to claim 1, characterized in that, The step of grading the service providers according to the task completion rate to obtain the set of service providers with advanced progress and the set of service providers with lagging progress, and screening the users served by all the service providers with lagging progress in the set of service providers with lagging progress according to the appointment service information to obtain the set of users who can be reassigned includes: Judge 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, then determine 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, then determine 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 served by the service providers with lagging progress; Judge whether the reassignment willingness option of the users served 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, then determine that the user is a user who can be reassigned; If the reassignment willingness option of the user is not willing to be reassigned, then determine that the user is a user who cannot be reassigned.

6. The online resource management method according to claim 1, wherein 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, wherein The deployment module includes: A permutation and combination unit, configured to perform permutation and combination on multiple dispatchable users in the dispatchable user set and the service providers with advanced progress in the service providers with advanced progress set, obtain a set of deployable combinations, and obtain the second estimated processing duration of each deployable combination according to the set of deployable combinations; A first obtaining unit, configured to 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; A second obtaining unit, configured to obtain the average service time according to the total service time of each service provider with advanced progress, and obtain an objective function according to the average service time and the total service time; A third obtaining unit, configured to obtain constraint condition information according to the dispatchable user set, the service provider set 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 deployment plan.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, 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 the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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