A hierarchical alarm processing and task scheduling method for a multi-user environment

Abstract

The application discloses a hierarchical alarm processing and task scheduling method in a multi-user environment, relates to the technical field of information processing and resource scheduling, and comprises the following steps: acquiring a technical personnel capability parameter vector, time constraint data and a work load upper limit, obtaining a candidate allocation scheme, calculating a task priority weighting factor of the technical personnel in different time periods, marking as an unusable state if the time constraint data of the technical personnel conflicts with a task time requirement, obtaining a first allocation scheme after removing the conflict, acquiring a capability parameter vector of other technical personnel and a work load allocation proportion, generating a substitute personnel list closest to the capability parameter vector of the unusable personnel, and obtaining a second allocation scheme; the hierarchical alarm processing and task scheduling method in the multi-user environment generates a real-time task execution log, updates system state data, realizes intelligent allocation, dynamic adjustment and stability prediction of technical personnel tasks, and improves the efficiency and rationality of task allocation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information processing and resource scheduling, and particularly relates to a hierarchical alarm processing and task scheduling method for a multi-user environment. BACKGROUND

[0002] Under the background of high development of informatization and intelligence, the hierarchical alarm processing and task scheduling in a multi-user environment is of great significance. This field is directly related to the efficient operation and resource optimization of complex systems, especially in emergency response, industrial control and intelligent service scenarios. Reasonable human resource scheduling can significantly improve task processing efficiency and system reliability.

[0003] Currently, research on human resource scheduling in hierarchical alarm processing mainly focuses on automated task allocation, but existing methods still have obvious shortcomings in intelligent personnel deployment for multi-user scheduling. Many traditional scheduling schemes rely on static rules or simple priority sorting, which are difficult to adapt to dynamic changes in multi-user demand. These methods often ignore the individualized workload upper limit and ability differences of technical personnel, leading to uneven task allocation or low efficiency. In addition, existing schemes often lack flexible substitute personnel recommendation mechanisms when dealing with time conflicts, which can delay alarm processing due to personnel unavailability. Especially for alarm tasks of different levels, how to accurately match according to the task urgency and personnel ability is still a difficult problem that has not been fully solved. The above limitations reveal the core challenges of human resource scheduling in a multi-user environment. First, intelligent personnel deployment needs to dynamically integrate various constraint conditions such as time, ability and task priority in multi-user scheduling, but existing algorithms are difficult to achieve efficient global optimization. Second, the solution of time conflicts and substitute personnel recommendation requires the system to quickly respond to dynamic changes, but there is currently a lack of mechanisms that take into account real-time and accuracy. Finally, the setting of individualized workload upper limit and ability parameters involves multi-dimensional evaluation of technical personnel, and existing methods are often oversimplified, making it difficult to accurately reflect individual differences. SUMMARY

[0004] The purpose of the present application is to provide a hierarchical alarm processing and task scheduling method for a multi-user environment to solve the problems existing in the prior art.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a hierarchical alarm processing and task scheduling method for a multi-user environment, comprising:

[0006] obtaining a technical personnel ability parameter vector, time constraint data and workload upper limit to obtain a candidate allocation scheme;

[0007] The computing technician assigns a task priority weighting factor to each task, and marks a technician as unavailable if the technician's time constraint data conflicts with the task time requirement, to obtain a first assignment scheme after removing conflicts;

[0008] The ability parameter vector and workload allocation ratio of other technicians are obtained, a substitute technician list closest to the unavailable technician ability parameter vector is generated, and a second assignment scheme is obtained;

[0009] The substitute technicians are sorted according to the task priority weighting factor and the time constraint check, and a third assignment scheme is obtained;

[0010] The task-person mapping table is determined according to the time constraint check and the ability parameter vector matching requirement of each task;

[0011] The load change trend of the technician after the assignment is obtained, and the task is re-assigned to generate an adjusted fifth assignment scheme;

[0012] The stability of the task assignment in the future time period is predicted, and a substitute technician list is regenerated by batch comparison operation to obtain a final assignment scheme;

[0013] The real-time task execution log is generated by combining the task priority weighting factor and the ability parameter vector, and the system state data is obtained.

[0014] Preferably, the candidate assignment scheme is obtained by obtaining the ability parameter vector, time constraint data and workload upper limit of the technician, and includes:

[0015] The ability parameter vector, time constraint data and workload upper limit of the technician are extracted from the schedule database, the structured query language is used to parse multi-user data to obtain an initial personnel data set, the ability parameter vector in the initial personnel data set is processed by vector normalization, the task priority list is combined to generate a personnel-task matching matrix, and an intermediate assignment scheme containing time availability and ability score is obtained. If the workload of a certain technician in the intermediate assignment scheme exceeds the upper limit, the task assignment is adjusted by a linear programming algorithm to obtain an optimized candidate assignment scheme. According to the optimized candidate assignment scheme, the time availability of each technician is obtained, the time window division method is used to determine the task execution time period, and if the task execution time period conflicts with the time constraint data, the task order is adjusted by priority sorting to obtain a conflict resolved assignment scheme. By comparing the conflict resolved assignment scheme with the ability score, the final task assignment result is determined by using the weighted average method. The final task assignment result is obtained, an assignment record containing the technician, the task and the time period is generated, and is stored in the schedule database.

[0016] Preferably, the computing technician weights the task priority in different time periods, marks as unavailable state if the technician's time constraint data conflicts with the task time requirement, and obtains the first allocation scheme after removing the conflict includes:

[0017] Obtain the initial personnel and task matching matrix, extract the technician, task priority and time period information, generate preliminary allocation data containing time constraints, use dynamic integration algorithm to calculate the task priority weighting factor in each time period, obtain the weighting factor set, if the technician's time constraint conflicts with the task time requirement, mark the corresponding technician as unavailable state according to the conflict marking rule, obtain the allocation data after removing the conflict, generate the first allocation scheme by combining the allocation data after removing the conflict with the weighting factor set, obtain the matching degree of each task priority and time period for the first allocation scheme, judge whether it meets the preset allocation balance threshold, obtain the allocation balance evaluation result, according to the allocation balance evaluation result, if the matching degree is lower than the preset threshold, adjust the weighting factor, recalculate the allocation data, obtain the optimized allocation scheme, determine the final personnel and task matching relationship through the optimized allocation scheme, and generate the allocation result containing time period and task priority.

[0018] Preferably, the ability parameter vector of the other technicians and the workload allocation ratio are obtained, the substitute personnel list closest to the unavailable personnel ability parameter vector is generated, and the second allocation scheme includes:

[0019] The ability parameter vector of the unavailable technical personnel and the ability parameter vectors of all technical personnel and the workload allocation proportion are extracted from the multi-user schedule database to obtain an initial personnel dataset, the similarity between the ability parameter vector of the unavailable technical personnel and the ability parameter vectors of the other technical personnel in the initial personnel dataset is calculated by using a cosine distance to obtain a similarity value set, wherein the cosine distance formula is: cos(θ) = (A·B) / (||A||·||B||), A is the ability parameter vector of the unavailable technical personnel, B is the ability parameter vector of the other technical personnel, and ||A|| and ||B|| represent the modulus of the vector A and the vector B respectively, the similarity value set is screened according to a preset threshold value, if the similarity value is greater than the preset threshold value, the corresponding technical personnel is included in a candidate replacement list to obtain a preliminary replacement personnel set, the workload allocation proportion of each technical personnel in the preliminary replacement personnel set is obtained, if the workload allocation proportion is lower than a preset maximum load threshold value, the technical personnel is retained to obtain an optimized replacement personnel list, the similarity values in the optimized replacement personnel list are arranged in descending order to obtain a sorted replacement personnel sequence, a second allocation scheme is generated by using the sorted replacement personnel sequence in combination with the task data corresponding to the unavailable technical personnel, and a final replacement personnel allocation result is determined, and the workload allocation proportion of the technical personnel in the second allocation scheme is updated from the multi-user schedule database to obtain an updated schedule dataset.

[0020] Preferably, the task priority weighting factor and the time constraint verification are combined to sort the replacement personnel to obtain a third allocation scheme, which includes:

[0021] The replacement personnel list, the task priority weighting factor and the time constraint condition are obtained from the second allocation scheme, a weighted sorting algorithm is used to calculate the personnel priority to obtain an initial priority sorting, the workload data of each replacement personnel is obtained according to the initial priority sorting, the load proportion is calculated, if the load proportion exceeds an upper threshold value, the corresponding replacement personnel is removed to obtain an updated priority sorting, the time constraint condition of the personnel is obtained from the updated priority sorting, and it is judged whether the task time requirement is met, if not, the order of the personnel is adjusted to obtain an adjusted priority sorting, the third allocation scheme is generated by using an allocation algorithm according to the adjusted priority sorting, the allocation result is obtained from the third allocation scheme, and it is judged whether there is a load conflict, if there is, the load proportion is recalculated and the overloaded personnel is removed to obtain an optimized allocation scheme, the final personnel allocation list is generated according to the optimized allocation scheme, and the third allocation scheme is determined.

[0022] Preferably, the time constraint verification and the ability parameter vector matching requirement for each task are used to determine the task and personnel mapping table, which includes:

[0023] By analyzing the third allocation scheme, the task time constraint and the capability parameter vector are obtained, the allocation basis data is determined, if the task time constraint and the capability parameter vector are not matched, the task priority is adjusted, the checked task sequence is obtained, the greedy algorithm is used, the technical personnel are allocated according to the checked task sequence and the capability parameter vector, the preliminary allocation result is determined, the time arrangement is generated through the preliminary allocation result, the time conflict is judged, the optimized time table is obtained, the fourth allocation scheme is generated according to the optimized time table and the preliminary allocation result, the task allocation details are determined, the corresponding relationship between the task and the technical personnel is extracted from the fourth allocation scheme, the task-personnel mapping table is generated, the matching degree of the allocation scheme and the capability parameter vector is checked for the task-personnel mapping table, and the final allocation result is judged.

[0024] Preferably, the task is redistributed to generate the adjusted fifth allocation scheme after the load change trend of the technical personnel is obtained includes:

[0025] The load change trend of the technical personnel is obtained from the task mapping table, the deviation of the daily task duration of each technical personnel from the historical average value is calculated, the load change trend is obtained, the allocation proportion of the current work load of each technical personnel is calculated according to the load change trend and in combination with the preset upper limit of the work load, the allocation proportion data is determined, if the allocation proportion of a certain technical personnel exceeds the dynamic threshold, the task allocation is adjusted through the linear programming algorithm, the temporary task allocation scheme is generated, the technical personnel load data after task redistribution is extracted from the temporary task allocation scheme, the load balance degree index is calculated, the balance degree evaluation value is obtained, the balance degree evaluation value and the balance degree of the historical allocation scheme are compared, if the current balance degree is higher than the historical average value, the fifth allocation scheme is generated by using the current scheme, the task allocation details of each technical personnel are extracted from the fifth allocation scheme, the task mapping table is updated, and the optimized allocation result is obtained, the load change trend of each technical personnel is calculated through the optimized allocation result, whether the dynamic threshold constraint is satisfied is verified, and the final allocation scheme is confirmed.

[0026] From the above technical solutions, the present application has the following beneficial effects:

[0027] The hierarchical alarm processing and task scheduling method for a multi-user environment generates an initial personnel-task matching matrix by obtaining the ability parameter vector of a technician, time constraints and a workload upper limit, and combining the task priority. For time conflicts, the application uses a dynamic integration algorithm to calculate a priority weighting factor, and generates a substitute personnel list through cosine distance calculation. The task allocation is performed using a greedy algorithm, and is dynamically adjusted according to the load change trend. The application also uses time series analysis to predict the stability of task allocation, and optimizes the substitute scheme through batch comparison operation. Finally, the application generates a real-time task execution log, updates the system state data, realizes intelligent allocation, dynamic adjustment and stability prediction of the technician task, and improves the efficiency and rationality of task allocation. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The method flowchart of the application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0030] As shown in Figure 1 The application provides a technical solution: a hierarchical alarm processing and task scheduling method for a multi-user environment, which comprises obtaining the ability parameter vector of a technician, time constraint data and a workload upper limit, obtaining a candidate allocation scheme; calculating the task priority weighting factor of the technician in different time periods, and marking as unavailable if the time constraint data of the technician conflicts with the task time requirement, obtaining a first allocation scheme after removing the conflict; obtaining the ability parameter vector of other technicians and the workload allocation proportion, generating a substitute personnel list closest to the ability parameter vector of the unavailable personnel, obtaining a second allocation scheme; sorting the substitute personnel by combining the task priority weighting factor and the time constraint check, obtaining a third allocation scheme; determining the task-personnel mapping table according to the time constraint check and the ability parameter vector matching requirement of each task; obtaining the load change trend of the technician after allocation, re-allocating tasks to generate a fifth adjusted allocation scheme; predicting the stability of task allocation in the future time period, re-generating a substitute personnel list through batch comparison operation, obtaining a final allocation scheme; and generating a real-time task execution log by combining the task priority weighting factor and the ability parameter vector, obtaining system state data.

[0031] The application realizes efficient matching between tasks and technical personnel by constructing a multi-dimensional scheduling model. First, the ability parameters of technical personnel are recorded in the form of a vector, and the ability parameters of each technical personnel are composed of several dimensions, such as proficiency in different skill categories, and the numerical range can be set to between zero and one. The system also constructs a task parameter vector according to the task requirements, which represents the requirements of the task in each ability dimension. In order to calculate the matching degree between a certain technical personnel and a certain task, the system uses the cosine similarity algorithm, which specifically does the following: multiply the ability parameter vectors of the technical personnel and the task, and then divide by the product of the lengths of the two vectors. The closer the similarity value is to one, the higher the matching degree between the technical personnel and the task. The priority weighting factor of the task takes into account the importance and time urgency of the task. First, a basic priority coefficient is assigned to each task. Then, a time decay parameter is introduced, which determines the trend of the priority of the task over time. The farther the current time is from the start time of the task, the lower the priority weighting value of the task. The system processes the basic priority through an exponential decay function to obtain the weighted priority value in the current time period. For the judgment of time conflict, the system checks whether the time interval of the task intersects with the available time period of a certain technical personnel. If there is no intersection between the two, it means that the technical personnel cannot complete the task in that time period, so it is marked as an unavailable personnel. When generating the substitute personnel list, the system compares the ability parameter gap between the unavailable personnel and other technical personnel. The gap is quantified by calculating the Euclidean distance between the two sets of parameter vectors, and the smaller the distance value, the closer the ability. The system sorts the closest technical personnel in ascending order of distance and selects the closest technical personnel as a substitute. The prediction of scheduling stability is achieved by setting a time sliding window. The system monitors the change in the number of tasks assigned within the time window and calculates the change rate of the number of assignments for each task within the time period. If the change rate of a certain task exceeds the set threshold, it means that the task scheduling is unstable, and the system will regenerate the substitute personnel list to improve the robustness of the scheduling. Finally, the system records the real-time task execution log, including task number, executor, start time, end time, task status, priority factor and ability matching degree, etc. These data will be the core basis of the system state, which will be used for subsequent visual analysis and scheduling optimization.

[0032] The hierarchical alarm processing and task scheduling method provided by the application has remarkable advantages in a multi-user environment. By introducing the capability parameter vector and the task priority weighting mechanism, more accurate matching between tasks and technical personnel is realized, the response capability of the system to time and skill requirements is effectively improved, and high-priority tasks can be processed in time by high-matching personnel; the automatic substitute personnel screening mechanism is adopted to enhance the fault tolerance and scheduling continuity of the system in the case of unavailable personnel; the matching algorithm is based on vector similarity evaluation, avoiding human intervention and improving the objectivity and scientificity of task allocation; traceable log records are generated throughout the scheduling process, facilitating real-time monitoring of system operation status and task traceability management; by dynamically analyzing the load change trend of technical personnel and the task allocation fluctuation rate, the task scheduling process is adjusted in advance and the stability is predicted, thereby further optimizing the overall operation efficiency and resource allocation effect of the system, which has high practical value and popularization prospect.

[0033] For example: a large manufacturing enterprise has multiple maintenance technical groups, which need to respond to equipment alarm tasks every day. After the application is implemented in the scheduling platform of the enterprise, the most suitable personnel are automatically recommended according to the skill level of the maintenance technical personnel, shift time and historical task records. When a technical personnel is temporarily on leave or a fault task is not completed in time, the system immediately activates the substitute mechanism to ensure uninterrupted task response. The accuracy of platform scheduling is improved from 75% before implementation to 93%, and the task overtime rate is reduced by about 40%, which has obtained high evaluation from users.

[0034] Obtaining the technical personnel capability parameter vector, time constraint data and work load upper limit, obtaining the candidate allocation scheme includes extracting the technical personnel capability parameter vector, time constraint data and work load upper limit from the schedule database, using structured query language to parse multi-user data to obtain an initial personnel data set, normalizing the capability parameter vector in the initial personnel data set, combining the task priority list to generate a personnel and task matching matrix, obtaining an intermediate allocation scheme containing time availability and capability score, if the work load of a certain technical personnel in the intermediate allocation scheme exceeds the upper limit, adjusting the task allocation through a linear programming algorithm to obtain an optimized candidate allocation scheme, obtaining the time availability of each technical personnel according to the optimized candidate allocation scheme, using a time window division method to determine the task execution time period, if there is a conflict between the task execution time period and the time constraint data, adjusting the task order through priority sorting to obtain a conflict-solved allocation scheme, comparing the conflict-solved allocation scheme with the capability score, using a weighted average method to determine the final task allocation result, obtaining the final task allocation result, generating an allocation record containing technical personnel, tasks and time periods, and storing it in the schedule database.

[0035] The embodiment is based on structured scheduling modeling, which completes the automatic matching and dynamic optimization scheduling between tasks and personnel by data modeling and score calculation of technical personnel ability, time resources and task requirements. First, the system extracts the information of all technical personnel to be scheduled from the enterprise internal schedule database, which includes three types of key data: one is the ability parameter, that is, the proficiency of technical personnel in multiple skill categories; two is the time constraint data, which reflects the available time interval of the personnel in the current time period; three is the upper limit of the workload of the personnel, such as the maximum number of tasks or the maximum total working time allowed per day or per week. These data are extracted and organized through structured query language to form an initial personnel data set. After obtaining the original ability data, the system will standardize the ability parameters of each technical personnel, so that the score data between different personnel can be compared on the same scale. The standardization method is: subtract the minimum value of the score of all technical personnel in each skill from the score of the personnel in each skill, and then divide by the difference between the maximum and minimum values of the skill, finally map each ability value to between zero and one. The original value of each ability score can be obtained by personnel evaluation scoring, training system record, historical task completion effect, etc. Then, the system obtains the task list to be scheduled. Each task also defines a task ability requirement list, which represents the skill dimension and corresponding skill strength requirement required to complete the task. The system forms a matching score matrix by calculating the ability matching degree between each technical personnel and each task. The score calculation method is: multiply the standardized ability value of the personnel and the skill requirement of the task item by item, and then sum up, and then divide by the size factor of the overall ability data. The closer the calculation result is to one, the more matched the technical personnel and the task are. Combined with the time constraint information, the system generates an intermediate allocation scheme, in which each task corresponds to several candidate technical personnel with high matching degree and current time availability. However, since there is an upper limit to the load of each technical personnel, the system needs to further judge whether the task quantity or total working time has exceeded the limit. If it exceeds the limit, the allocation scheme will be marked as invalid, and the system needs to enter the next task rescheduling process. The rescheduling process optimizes and adjusts the allocation relationship between personnel and tasks to maximize the overall matching degree of the system. This process constructs a linear optimization model, which selects the combination with the highest matching score from all personnel and task combinations under the condition that each task can only be executed by one technical personnel and the total number of tasks of each technical personnel does not exceed its upper limit. The optimization problem can be calculated by linear programming or integer programming solver. After optimization, the system further checks whether there is a conflict between the time period of the technical personnel and the task plan time. Each task has an execution start and end time, and each technical personnel also has its own available time interval. If there is no intersection between the task time and the available time of the personnel, the task cannot be executed by the personnel.For the case of conflict, the system will rearrange the order of the tasks according to the task priority, and the high priority task will be arranged first, so as to form the allocation result after conflict resolution. In the above process, in order to balance the importance of the task and the matching degree of the ability, the system uses a weighted average strategy to integrate the scores of the two. That is, the task priority value is combined with the personnel ability matching score in a certain proportion to obtain the final score. Among them, the task priority is generally set by the business system, and can be scored according to the business urgency of the task, the degree of influence on the customer and other factors, and the common score range is one to five levels; The weighted proportion is set by the scheduling strategy, which indicates that the system is more biased towards the priority of the task itself or the skill adaptability of the personnel. Finally, the system selects the optimal execution personnel for each task according to the weighted score, and generates detailed task allocation records, including the technical personnel number, task number, task execution time period, task importance level, matching score and the like. All the results will be written into the original schedule database for subsequent calling and execution monitoring by the scheduling system.

[0036] The embodiment can greatly improve the automation and intelligence level of task allocation. By introducing vector normalization and matching score mechanism, more accurate adaptability evaluation between tasks and personnel is realized, and the problem of subjective judgment deviation in manual allocation is solved; the introduction of linear programming model effectively balances the resource utilization rate and load limitation, avoids the overload of individual personnel and leads to scheduling failure; the time window division and priority sorting mechanism improves the flexibility of task time matching, makes the task scheduling more reasonable, and maximizes the utilization of resources; finally, through the database writing mechanism, the whole process of allocation record tracking and auditing is realized, which provides data support for subsequent scheduling optimization, performance evaluation and task backtracking, and the overall scheduling efficiency and stability of the system are significantly improved.

[0037] For example, in an information technology outsourcing service enterprise, the customer project group often needs to assemble multiple technical personnel in a short time to handle alarm tasks of different customer systems. After deploying the scheduling system of the present application, the enterprise extracts the available time, ability level and historical task volume of hundreds of technical personnel through the schedule database, and the system automatically completes task matching and load balancing to avoid repeated dispatching or personnel conflict. Through linear programming and weighted scoring mechanism, the optimal task arrangement is realized, more than two hundred tasks can be automatically scheduled every day, the manual intervention rate is reduced by more than 70%, the scheduling accuracy is stable at more than 95%, and the customer satisfaction and system processing efficiency are greatly improved.

[0038] The computing technician marks a task priority weighting factor in different time periods, marks a technician as an unavailable state if the time constraint data of the technician conflicts with the time requirement of the task, obtains a first allocation scheme after removing conflicts, includes obtaining an initial technician and task matching matrix, extracting technician, task priority and time period information, generating preliminary allocation data containing time constraints, using a dynamic integration algorithm, calculating the task priority weighting factor in each time period based on the preliminary allocation data, obtaining a set of weighting factors, if the technician's time constraints conflict with the task's time requirements, mark the corresponding technician as an unavailable state according to the conflict marking rule, obtain the allocation data after removing the conflict, generate the first allocation scheme by combining the allocation data after removing the conflict with the set of weighting factors, for the first allocation scheme, obtain the matching degree of each task priority and time period, judge whether it meets the preset allocation balance threshold, obtain the allocation balance evaluation result, according to the allocation balance evaluation result, if the matching degree is lower than the preset threshold, adjust the weighting factor, recalculate the allocation data, obtain the optimized allocation scheme, determine the final technician and task matching relationship through the optimized allocation scheme, generate an allocation result containing time periods and task priorities.

[0039] The embodiment aims to solve the problem of task allocation and time conflict in a multi-user environment by establishing a time constraint and priority weighting model to achieve an efficient and executable task scheduling mechanism. The core process includes priority factor calculation, time conflict determination, conflict data elimination, matching degree balance judgment and factor dynamic adjustment. First, the system obtains the "personnel and task matching matrix", which is composed of three types of information: technical personnel identification (i.e. personnel number or ID), task priority value and task execution time period. The task priority value is a parameter representing the importance and urgency of the task, usually taking a value range of 1 to 5, where 1 represents the lowest priority and 5 represents the highest. The priority value can be set by business rules, alarm levels, service level agreement (SLA) clauses or expert manual evaluation, or directly provided by the task registration module in the enterprise information system (such as CRM, ERP). The task execution time period is usually represented by start time and end time, for example "April 11, 2025 9:00 to 11:00". The available time period of technical personnel is also described by start time and end time, from the human resource management system or the schedule management module. The system compares whether there is an intersection between the task time period and the available time period of technical personnel. If there is no intersection, it means that the personnel cannot execute the task. A preliminary allocation dataset is constructed, combining the above three types of information to form the basis of task scheduling. Then, a dynamic integration algorithm is used to summarize the task set in each time period and calculate the task priority weighting factor of that time period. This weighting factor represents the comprehensive value of the degree and urgency of the task set in a certain time period. Its calculation method is: the priority values of all tasks in that time period are added up and multiplied by a value called "task urgency attenuation parameter". The task urgency attenuation parameter is a constant used to regulate the rate of decline of task priority over time. For example, if the parameter is set to 0.05, it means that the farther the current time is from the task start time, the faster the weighting value of the task decreases. This parameter is usually set by the system administrator at the initialization time, or can be determined by modeling historical task response data, such as fitting the task delay rate in different time windows. The system matches the task time period with the personnel available time period. If there is no overlap, the personnel is marked as "unavailable" for that task according to the conflict marking rules. The marked information is eliminated to form the allocation data after conflict elimination. Next, the system combines the "weighted factor set" with the conflict-eliminated data set to generate the first round of allocation scheme. On this basis, the system further calculates the matching degree, i.e. the degree of adaptation of the task to the personnel. The matching degree can be composed of two parts: one is the aforementioned task priority weighting factor, and the other is the matching score of personnel and task in the ability vector, which can be integrated into the final score by weighted average method. The weighted average proportion parameter is used to set the weight relationship between the priority factor and the ability score. For example, set to 0.7:0.3 means that the system pays more attention to task urgency. The proportion can be configured by the administrator, or dynamically adjusted according to the nature of the task.The system statistically analyzes the matching degree of all tasks to determine whether the preset allocation balance threshold is reached. The threshold defines the minimum average matching degree required by the system, such as 0.75, which means that if the average matching degree is less than 75%, the scheduling effect is considered to be poor. The threshold can be set by experience or obtained by training the scheduling strategy model. If the matching degree is lower than the set value, the system will trigger the weighting factor adjustment mechanism. This mechanism adjusts the task priority factor based on historical task completion time, complaint records, and timeout frequency to improve the urgency coefficient of high-risk tasks and reduce the scheduling priority of non-critical tasks. The adjusted weighting factor participates in the scheduling operation again to obtain an optimized scheduling result. Finally, the system generates a complete task scheduling dataset, which clearly identifies the technician number, task number, task execution time period, and task priority level, and stores it in the system database.

[0040] The present application realizes precise task scheduling and personnel screening in multiple time periods by introducing a task priority weighting mechanism and a dynamic conflict detection strategy, greatly improving the system's adaptability to task urgency and time resources. The availability detection mechanism of technicians ensures the executability of the scheduling, avoiding the situation where the task cannot be implemented due to resource conflicts after being assigned; the real-time dynamic adjustment strategy of the weighting factor realizes active intervention on task backlog and resource overload, ensuring the continuous balance and service stability of the scheduling from the system level. The matching degree evaluation and allocation balance judgment mechanism further enhances the visualization and intelligence of the allocation process, making the scheduling decision more data-based and adjustable control logic.

[0041] For example, in a nationwide chain of on-site operation and maintenance service companies, more than 1,000 device alarm tasks need to be handled daily, and the task distribution involves multiple time zones and cities. After deploying the method of the present application, the scheduling system can identify the business priority of the task and the allocability of the technician in real time, and still maintain a task success matching rate of more than 95% during the task outbreak period. The system eliminates technicians who do not meet the time conditions through conflict detection and dynamically adjusts the task priority weight, so that the task can be automatically allocated to the most suitable personnel. The system successfully supported continuous 7-day overload operation during peak periods such as the Spring Festival and Double Eleven, and improved the task completion rate by 22%, significantly improving customer satisfaction and enterprise operation efficiency.

[0042] The capability parameter vector of the other technicians and the workload allocation ratio are obtained, a substitute technician list closest to the unavailable technician capability parameter vector is generated, and a second allocation scheme is obtained, including extracting the capability parameter vector of the unavailable technician and the capability parameter vector of all technicians and the workload allocation ratio from the multi-user schedule database to obtain an initial personnel dataset, calculating the similarity between the capability parameter vector of each technician in the initial personnel dataset and the capability parameter vector of the unavailable technician by a cosine distance to obtain a set of similarity values, wherein the cosine distance formula is:

[0043] cos(θ)=(A·B) / (||A||·||B||), A is the capability parameter vector of the unavailable technician, B is the capability parameter vector of the other technician, ||A|| and ||B|| represent the modulus of vector A and vector B respectively, the set of similarity values is screened according to a preset threshold, if the similarity value is greater than the preset threshold, the corresponding technician is included in the candidate substitute list to obtain a preliminary substitute technician set, the workload allocation ratio of each technician in the preliminary substitute technician set is obtained, if the workload allocation ratio is lower than a preset maximum load threshold, the technician is retained to obtain an optimized substitute technician list, the similarity values in the optimized substitute technician list are arranged in descending order to obtain a sorted substitute technician sequence, the second allocation scheme is generated by combining the corresponding task data of the unavailable technician through the sorted substitute technician sequence, and a final substitute technician allocation result is determined, the workload allocation ratio of the technician in the second allocation scheme is updated from the multi-user schedule database to obtain an updated schedule dataset.

[0044] The present embodiment mainly realizes the rapid screening of alternative technical personnel based on the similarity of ability and the current workload level, and completes the replacement task allocation, ensuring that the task does not be interrupted. First, the system extracts two types of data from the multi-user schedule database: one is the ability parameter vector of the technical personnel in the "unavailable state", and the other is the ability parameter vector of all technical personnel in the current system and the corresponding workload allocation proportion. The ability parameter vector reflects the scores of technical personnel in different skill dimensions, and the score standard can be set according to the internal evaluation system or the historical task completion performance, usually a decimal between 0 and 1. Next, the system calculates the similarity of the unavailable technical personnel and other technical personnel in the ability vector. The similarity calculation uses the cosine similarity method, that is: first, multiply the scores of the two technical personnel in all skill dimensions respectively and sum them up to get the vector dot product; then square each item in the two ability vectors respectively and sum them up, and then take the square root to get the modulus of the two vectors; finally, divide the dot product by the product of the two moduli to get a similarity value between 0 and 1. The closer the value is to 1, the closer the ability of the two technical personnel is. The system forms a similarity set of all technical personnel and the unavailable technical personnel. Then, a similarity threshold parameter is set, for example, 0.8, which means only the personnel with a similarity of more than 80% can be a suitable replacement. The threshold is set by the system administrator or the strategy model, and can also be optimized by learning according to the historical replacement success rate. The technical personnel who meet the similarity condition are included in the preliminary replacement personnel set. Then, the system extracts the current workload allocation proportion of each candidate personnel from the set. The proportion is the ratio of the amount of tasks he has currently undertaken to the maximum allowable load, for example, a person has undertaken 5 tasks, and the maximum limit is 10, so the allocation proportion is 50%. If the load proportion of a technical personnel is lower than the preset maximum load threshold, for example, 80%, it means that he still has the ability to undertake new tasks, and he will be retained in the optimized replacement personnel list. The system sorts the optimized replacement personnel in descending order according to their similarity with the unavailable personnel, forming a replacement personnel sorting sequence. Then, combined with the task data of the unavailable personnel (such as task priority, task time period), the tasks are allocated to the most suitable replacement personnel in turn, thereby constructing a second allocation scheme. The scheme ensures that each task is executed by the personnel with the most matched ability and moderate workload. Finally, the system writes the task allocation in the second allocation scheme into the database, updates the workload allocation proportion of each replacement personnel, and forms a new schedule data set to provide data support for subsequent scheduling.

[0045] The application realizes efficient replacement scheduling under the condition that personnel is unavailable by introducing the ability similarity screening mechanism and the load ratio control mechanism, guarantees the continuity and accuracy of task execution, and maximally avoids the overload problem of personnel resources. The cosine similarity is used as the basis for ability evaluation, so that the replacement personnel selection has the basis of quantifiability and interpretability; and the screening logic based on the load threshold effectively avoids the system bottleneck caused by the centralized distribution of tasks to a few people, and improves the load balancing and resource utilization efficiency of the overall scheduling system.

[0046] A certain power operation and maintenance enterprise has technical response sites in multiple places, which need to respond to a large number of alarm tasks every day. When a regional technician is temporarily absent for some reason, the scheduling system immediately selects other regional personnel with similar professional ability and task volume below 80% according to their skill models and current task data, and automatically generates a replacement task allocation scheme. The past emergency replacement that needs to be manually coordinated for more than 30 minutes is now completed by the system within 3 seconds and the database is updated synchronously, which significantly improves the emergency response capability and the intelligent level of operation and maintenance scheduling.

[0047] The task priority weighting factor and the time constraint check are combined to sort the replacement personnel, and a third allocation scheme is obtained. The replacement personnel list, the task priority weighting factor and the time constraint condition are obtained from the second allocation scheme, the weighted sorting algorithm is used to calculate the personnel priority to obtain an initial priority order, the workload data of each replacement personnel is obtained according to the initial priority order, the load ratio is calculated, if the load ratio exceeds the upper threshold, the corresponding replacement personnel is removed, and an updated priority order is obtained. The time constraint condition of the personnel is obtained from the updated priority order, and it is judged whether the task time requirement is met, if not, the personnel order is adjusted to obtain an adjusted priority order, the adjusted priority order is used to generate the third allocation scheme according to the adjusted priority order, the allocation result is obtained from the third allocation scheme, and it is judged whether there is a load conflict, if there is, the load ratio is recalculated and the overloaded personnel is removed, and an optimized allocation scheme is obtained. The final personnel allocation list is generated according to the optimized allocation scheme, and the third allocation scheme is determined.

[0048] The main goal of the embodiment is to further consider task priority, personnel load and time constraints on the basis of the screened substitutes, to realize task allocation after sorting optimization, and to ensure that resource allocation is efficient and executable. First, the system extracts the list of available substitutes, the weighted priority factor of each task and the related time constraint information from the second allocation scheme. The weighted priority factor of the task is obtained by multiplying the original priority of the task by the time urgency. The urgency can be controlled by a time decay factor, which is set by the scheduling strategy. The common range is between one percent and ten percent. The larger the value, the more urgent the task. The system calculates the ranking score of each substitute, which takes into account the task priority factor and the current task load of the technician. Specifically, the task priority weighted factor is multiplied by a set task importance weight, and then the current load proportion of the personnel is multiplied by a load suppression coefficient. The weight proportion can be set to seven to three or six to four, etc., which is configured by the administrator according to the characteristics of the task. The final score is used to preliminarily sort all substitutes and obtain an initial priority sorting list. Then, the system obtains the current working load proportion of each person in the sorting list, which is the current number of tasks accepted by the person divided by the maximum number of tasks allowed. If the load proportion of a person exceeds the system-set load upper threshold (such as 80%), the person is removed from the sorting list to form an updated priority sorting list. Then, the system reads the time constraint information of each substitute from the updated list, i.e. the available time period. The system checks whether the time period covers the task execution period. If not, the position of the person in the list is adjusted backward, and the priority is lowered. This process forms an adjusted priority sorting list, reflecting the overall availability of each candidate. According to the sorting result, the system uses a task allocation algorithm to allocate tasks to the technician with the smallest load and the most matched time, generating a third allocation scheme. The system then performs load conflict detection on the allocation result. If it is found that the load proportion of a person exceeds the limit after task allocation (such as more than 100%), the person is removed from the result, and the load allocation and sorting are performed again according to the remaining personnel to form an optimized allocation scheme. Finally, the system generates a final personnel allocation list based on the optimization result, which contains task number, personnel number, task priority, estimated start and end time, and current load proportion, as the third allocation scheme is written into the system database.

[0049] This embodiment introduces task priority factors and dynamic sorting mechanisms, combines time matching and load constraints, and builds a scheduling sorting logic under multiple factors, effectively avoiding the "only capability matching" scheduling mistake, and improving the overall rationality of task execution and the balance of system resource use. At the same time, through the progressive screening and allocation optimization process, the scheduling process can automatically backtrack and correct errors, making the scheduling scheme more stable and practical.

[0050] For example, a certain intelligent building equipment operation and maintenance platform receives a large number of alarm requests during holidays, and multiple front-line technical personnel are marked as unavailable due to holidays. After the system constructs a substitute candidate pool based on historical performance and ability data, it automatically calculates the priority in combination with the task urgency, the current task amount of personnel, and the scheduling schedule, eliminates overloaded and conflicting personnel, and completes sequencing and distribution. The final distribution list is automatically sent to the personnel terminal device, the overall response efficiency is improved by 32%, the holiday overtime task rate is reduced by 46%, and the stable operation of the facility and the service quality are ensured.

[0051] For each task, the time constraint verification and the ability parameter vector matching requirement are determined, the task and personnel mapping table is determined by analyzing the third distribution scheme, obtaining the task time constraint and the ability parameter vector, determining the distribution basic data, if the task time constraint and the ability parameter vector are not matched, adjusting the task priority, obtaining the verified task sequence, using the greedy algorithm, according to the verified task sequence and the ability parameter vector, distributing the technical personnel, determining the preliminary distribution result, generating the time arrangement through the preliminary distribution result, judging the time conflict, obtaining the optimized time table, according to the optimized time table and the preliminary distribution result, generating the fourth distribution scheme, determining the task distribution details, extracting the corresponding relationship between the task and the technical personnel from the fourth distribution scheme, generating the task personnel mapping table, verifying the matching degree of the distribution scheme and the ability parameter vector for the task personnel mapping table, and judging the final distribution result.

[0052] The embodiment aims to generate a final matching relationship mapping table between tasks and personnel based on the third allocation scheme, combining the execution time limit of the task and the personnel ability requirement, to ensure the executability and rationality of task scheduling. First, the system analyzes the third allocation scheme and extracts two core information of each task: time constraint and ability parameter vector requirement. The time constraint is generally represented by start and end time, which comes from the alarm record or the scheduling plan. The ability parameter vector represents the personnel's ability requirement of the task in several skill dimensions, such as "device diagnosis ability ≥ 0.8, communication protocol familiarity ≥ 0.6". These values are modeled by the system for skill labels of task types, or extracted according to historical successful matching experience. Then, the system performs ability-time adaptability check on all tasks. That is, the ability vector of each technician is compared with the task requirement. If the ability requirement of a task is higher than the corresponding dimension of the technician's ability score in a skill dimension, the system marks the combination of the task and the personnel as "not matched". At this time, the system adjusts the priority of the task to appropriately delay the scheduling, so that more suitable technicians are given priority to handle. The priority adjustment can be set with a decreasing weight according to the "skill gap degree", such as reducing one priority level for every 0.1 point difference. After completing the priority adjustment, the system uses a greedy algorithm to allocate technicians to the checked task sequence. The algorithm sorts the tasks from high to low according to the task priority, and finds the first technician who meets the ability requirement and has an available time period for each task. After completing the preliminary allocation, the system constructs a preliminary time schedule plan, that is, aligns the start and end time of each task with the personnel's schedule. If a personnel is scheduled with multiple overlapping tasks, the system judges that there is a time conflict. The conflict detection uses the time period overlap logic: if the time intervals of two tasks have any intersection, it is considered as a conflict. To solve the conflict, the system prioritizes the tasks with high priority, and tries to delay the conflicting tasks to the next available time period or assign them to the next high personnel, to construct an optimized time table. This time table is used to generate the fourth allocation scheme, to further clarify the task execution time and the executor. The system extracts the correspondence between tasks and personnel in the fourth allocation scheme to generate a task-personnel mapping table. Each task corresponds to a personnel number and an execution time period. To verify the rationality of the allocation, the system performs ability matching degree check on the mapping table, which is: calculating the similarity between the ability vector of the assigned personnel and the task requirement vector to obtain the overall scheduling average matching degree. If the matching degree is lower than the set threshold (such as 75%), it is marked as "low allocation quality", and the system can enter the next round of optimization process.

[0053] In this embodiment, the core parameters include the task's ability parameter vector, the technician's ability parameter vector, the task time constraint, the ability matching threshold, the task priority value, the priority adjustment factor, the task allocation greedy algorithm order, the time conflict judgment parameter, and the task-person mapping matching degree score. The task ability parameter vector represents the task's requirements for each skill, with a value range of 0 to 1, usually determined by task modeling or historical data mining, reflecting the task's demand for the technician's skills. The technician's ability parameter vector is used to describe the technician's ability level in different skill dimensions, and the parameter value is usually dynamically adjusted by performance evaluation, training records, and task completion feedback. The task time constraint defines the start and end time of the task, usually provided by the alarm system or customer SLA, and affects the priority of task scheduling. The ability matching threshold represents the minimum matching standard between task requirements and technician's ability, usually set by the system or obtained through historical data regression analysis, ensuring the rationality of task allocation. The task priority value is used to measure the urgency of the task, usually determined by factors such as alarm level and customer demand, and tasks with higher priority will be scheduled first. The priority adjustment factor controls the decline of task priority when the task cannot be completely matched, and this factor can be dynamically adjusted according to the urgency of the task and the matching gap. The task allocation adopts a greedy algorithm order, and the tasks are allocated to suitable personnel according to the adjusted priority. The time conflict judgment parameter is used to detect the conflict of task time interval, and if there is overlap in task time, it is considered as a conflict. Finally, the task-person mapping matching degree score is used to measure the matching degree between the task and the personnel, and the system evaluates the matching degree by calculating the similarity between the task ability requirement vector and the assigned personnel ability vector, ensuring that the task is efficiently allocated to the most suitable personnel on time.

[0054] This embodiment realizes accurate matching of tasks and personnel under multiple constraint conditions by integrating task ability requirements, time resource constraints, and task priority scheduling elements. The introduction of the ability mismatch optimization mechanism and the greedy arrangement algorithm improves the speed and feasibility of task arrangement. The time conflict detection and dynamic scheduling mechanism significantly reduces repeated scheduling and conflict scheduling, improving the overall scheduling success rate and task completion rate on time.

[0055] For example, in an intelligent manufacturing equipment monitoring platform, hundreds of alarm events need to be responded to daily, with significant differences in task skills and time sensitivity. After deploying the method of the present application, the system can automatically select the most matched and time idle personnel from multiple technical teams to perform according to the required technical type and response time window of each alarm task, and automatically postpone or reassign some tasks according to the scheduling conflict. Through the ability matching and conflict processing mechanism, the task matching success rate is improved from 78% to 91%, while avoiding the original daily task delay execution of more than 10 times, greatly improving the operation and maintenance efficiency and system stability.

[0056] The load change trend of the technical personnel after the allocation is obtained, the adjusted fifth allocation scheme is generated by reallocating the tasks, the load change trend of the technical personnel is obtained from the task mapping table, the deviation of the daily task duration of each technical personnel from the historical average is calculated to obtain the load change trend, the allocation proportion of the current work load of each technical personnel is calculated according to the load change trend and in combination with the preset upper limit of the work load, the allocation proportion data is determined, if the allocation proportion of a certain technical personnel exceeds the dynamic threshold, the task allocation is adjusted through a linear programming algorithm to generate a temporary task allocation scheme, the technical personnel load data after the task reallocation is extracted from the temporary task allocation scheme, the load balance degree index is calculated to obtain the balance degree evaluation value, the balance degree evaluation value is compared with the balance degree of the historical allocation scheme, if the current balance degree is higher than the historical average, the current scheme is adopted to generate the fifth allocation scheme, the task allocation details of each technical personnel are extracted from the fifth allocation scheme, the task mapping table is updated to obtain the optimized allocation result, the load change trend of each technical personnel is calculated through the optimized allocation result, whether the dynamic threshold constraint is satisfied is verified, and the final allocation scheme is confirmed.

[0057] The present embodiment dynamically analyzes the load change trend of technicians, combines task priority, time constraints, and personnel capacity allocation, and optimizes the task allocation scheme. The core goal of the system is to ensure that the load of each technician is reasonable and to avoid overload while maintaining efficient completion of tasks on time. First, the system obtains the load change data of each technician from the task mapping table. The load change of a technician is calculated by comparing the deviation of the current task duration from the historical average task duration. Through this analysis, the system understands whether the load of the technician has changed significantly. If the task duration of a technician deviates significantly from its historical average, it indicates that its load has changed, and the system will track it and take necessary adjustment measures. Next, the system calculates the load ratio of each technician according to their current task duration. The load ratio represents the proportion of the current task load of the technician relative to its maximum carrying capacity. If the load ratio of a technician exceeds the set dynamic threshold (for example, more than 80% of its maximum load), the system will trigger the adjustment mechanism to prevent the person from continuing to undertake more tasks. If the load of a technician exceeds the threshold, the system will use a linear programming algorithm to redistribute tasks. The purpose of the linear programming algorithm is to optimize the task allocation scheme while ensuring that the load of each technician does not exceed the limit, maximizing the efficiency of task completion. This adjustment algorithm ensures that all tasks are reasonably allocated to qualified technicians and maximizes overall scheduling efficiency. After completing task redistribution, the system generates a temporary task allocation scheme and re-evaluates the load balance according to the task quantity of each technician. Load balance is an indicator used to measure whether the task allocation of each technician is balanced. The closer the value is to 1, the more balanced the load allocation. The system will calculate the current load balance and compare it with the historical task allocation scheme balance. If the load balance of the current scheme is higher than the historical average, it indicates that the allocation result is more reasonable, and the system will adopt this scheme as the new task allocation scheme. Finally, the system generates the fifth allocation scheme according to the optimized time table and adjusted task allocation scheme. This scheme includes the execution time and execution personnel of each task, ensuring that tasks can be completed within the specified time and that the load of each technician is within a reasonable range. The system also updates the task mapping table to reflect the latest correspondence between tasks and technicians. Finally, the system will continue to monitor the load change trend of technicians according to the adjusted allocation scheme to ensure that the load of technicians meets the requirements of the dynamic threshold during task execution. If the load exceeds the preset limit, the system will automatically adjust again to ensure the balance and sustainability of task allocation.

[0058] The embodiment adjusts the task allocation dynamically, optimizes the load balancing, and adjusts the task allocation scheme through linear programming, ensures that the task is reasonably and efficiently allocated to the appropriate technical personnel, and avoids problems such as overload and task delay. The method analyzes the load change trend, historical task data and load balancing of the technical personnel in real time, ensures the stability and efficiency of the scheduling system, and optimizes the use efficiency of resources.

[0059] For example, in a global technical support company, multiple technical teams need to respond to customer device fault repair in different time periods. The system intelligently adjusts according to the task duration and load change trend of each technical personnel, avoids individual team overload during peak hours, and successfully improves the timely completion rate of tasks. Through load balancing calculation, the system ensures the balance of task allocation, and the overall task completion rate is improved by 18%, and the task delay is reduced.

[0060] Predicting the stability of task allocation in the future time period, batch comparison operation regenerates the substitute personnel list, and the final allocation scheme includes using time series analysis algorithm to extract features from historical task allocation data to generate task allocation stability prediction results in the future time period, to obtain the allocation stability trend, if the allocation stability trend shows that there is a risk of time constraint verification failure for a task, then through the risk identification mechanism, extract the failure task identifier from the prediction result to obtain the risk task set, for the risk task set, obtain the pre-established personnel resource pool data, through batch comparison operation, filter the candidate personnel meeting the task demand, generate the substitute personnel list, according to the task priority sorting rule, extract the personnel attribute from the substitute personnel list, combine the priority to calculate the dynamic weight, obtain the adjusted weight distribution matrix, through the dynamic weight distribution matrix, recalculate the matching degree of task and personnel, generate the candidate allocation scheme set, obtain the optimized allocation scheme set, if there are multiple schemes in the candidate allocation scheme set, then through the time constraint verification algorithm, verify the time feasibility of the scheme one by one, determine the final allocation scheme, use the final allocation scheme to update the task allocation database, generate the stable task allocation result, and output to the system log.

[0061] First, the system analyzes historical task assignment data using time series analysis algorithms to extract features such as task execution time, task type, task completion duration, and task assignee. Using these features, the system predicts the stability of task assignments in future time periods. This stability prediction reflects the trend of task assignments and helps the system identify potential risky tasks. Specifically, the system performs regression analysis on historical data to identify regularities and trends in tasks over different time periods. Based on this historical data, the system builds a prediction model to predict whether a task will be stable in a future time period. If the prediction shows that a task may not be completed on time or there is a risk of time constraint conflict, the system marks the task as a "risky task." When the prediction shows that a task's time constraints are at risk of failure, the system extracts failure indicators for the task through a risk identification mechanism. These indicators are usually derived from time constraint verification and analysis of the matching degree of task priority. If the task's time requirements overlap with the available time of personnel or the matching degree of task priority with the current assignee's ability is low, the task is marked as risky. The system collects all tasks marked as "risky" into a risk task set. This set contains information such as task ID, task time constraints, task priority, execution personnel, and predicted failure risk level. Next, for each risky task, the system selects candidate personnel from the personnel resource pool that meets the task requirements. The personnel resource pool contains detailed information about each technical personnel, such as skills, available time, historical workload, and task completion. The system uses batch comparison operations to select personnel based on the following matching degrees: skill matching degree: whether the technical personnel's ability meets the task requirements; available time: whether the technical personnel has free time during the task execution period; workload: whether the technical personnel has already taken on other tasks and whether they can complete the new task within the specified time. Through these indicators, the system selects technical personnel that meet the task requirements and generates a candidate personnel list. For the selected candidate personnel, the system extracts personnel attributes from the list according to the task priority sorting rules and calculates the dynamic weight of each candidate personnel based on the task priority. The calculation of dynamic weight considers the matching degree between task priority and personnel ability. Specifically, the higher the matching degree between task priority and technical personnel's ability, the higher the dynamic weight of the technical personnel. For example, if the task priority is high and highly matches the skills of a certain technical personnel, that technical personnel will be given a higher weight; if the task priority is low or the technical personnel's ability does not fully meet the task requirements, the weight of that technical personnel will be lower. Through the calculation of dynamic weight, the system generates a weight distribution matrix and uses it to calculate the matching degree between tasks and personnel. This matrix shows the priority of each candidate personnel in task assignment and reflects the matching between technical personnel's load and task requirements.Each task is assigned to the appropriate personnel based on the matching degree, generating a set of candidate allocation solutions. If there are multiple solutions in the candidate allocation solution set, the system will perform time constraint verification on each solution. The process of time constraint verification is to verify whether the execution time of each task matches the available time of the technical personnel. The system will verify each allocation solution one by one to ensure that each task can be completed on time. If the time constraints of a task cannot be met, the system will adjust the allocation solution of the task and find suitable technical personnel until the time constraints of all tasks are met. After time constraint verification, the system will determine a final allocation solution that contains the final matching relationship between all tasks and technical personnel. The final allocation solution selects the most suitable personnel to perform each task by analyzing factors such as task priority, technical personnel ability matching degree, time constraints, and load conditions. Finally, the system updates the task allocation database using the final allocation solution to ensure that the execution information of the task is accurately recorded and output to the system log for subsequent tracking and querying.

[0062] This embodiment can predict the stability of task allocation and automatically identify potential risk tasks by combining time series analysis and risk identification mechanisms, thereby effectively reducing conflicts and delays in task allocation. The system dynamically adjusts the allocation weight of the substitute personnel to ensure that tasks can be reasonably allocated to the most suitable technical personnel, improving the efficiency and accuracy of task allocation. Through automated time constraint verification and priority sorting, the use of resources is optimized, avoiding overload and task mismatch, and improving the overall system's response capability and stability.

[0063] For example, in a dispatch system of a global technical support company, thousands of customer requests are handled every day, including equipment failure, network problems, system maintenance, etc. One day, an urgent equipment maintenance task is marked as the highest priority and requires specific skills such as network troubleshooting. However, since the technical personnel responsible for such tasks have been assigned to other tasks, they cannot complete the task within the specified 6 hours. The system predicts the potential instability of task allocation through time series analysis and risk identification mechanism, automatically marks the task as a risk task, and initiates the screening of replacement personnel. The system selects several suitable candidates from the personnel resource pool according to the technical personnel's skills, available time, and current workload through batch comparison operations. Finally, the system selects a technical personnel who not only has the necessary skills but also has spare time during the execution period of the task. After priority sorting and dynamic weight adjustment, the personnel is assigned to the task and successfully completes the task within 6 hours. Through this system, the technical support company not only avoids task delays but also maximizes the optimization of technical personnel's workload, improving task response speed. Customer satisfaction is significantly improved, and through this intelligent scheduling, the technical support company can more efficiently manage resources, reduce labor costs and task backlog, and ensure service quality.

[0064] The real-time task execution log is generated by combining the task priority weighting factor and the capability parameter vector, and the system state data is obtained, including obtaining the task ranking result by task priority and weighting factor calculation, determining the task allocation scheme, and the calculation formula is: S=P x W+C, wherein S is the task score, P is the task priority, W is the priority weight, and C is the capability parameter. The task allocation scheme is obtained after ranking. According to the task allocation scheme, the real-time task execution log is generated, the task identifier, execution time and technical personnel identifier are extracted from the allocation scheme, the structured execution log is output by using the log generation algorithm, and is stored in the multi-user schedule database. If the execution log is generated, the time constraint data in the multi-user schedule database is updated. The task start time and end time are obtained from the execution log, the time occupation of the technical personnel is calculated, the time constraint field of the corresponding technical personnel in the database is updated, the updated time constraint data is obtained, the technical personnel capability parameter vector is adjusted through the time constraint data, the occupation time proportion is extracted from the time constraint data, the capability parameter vector is updated by combining the task complexity and using vector operation, and the specific formula is: C'=C x (1-T), wherein C' is the adjusted capability parameter, C is the capability parameter, and T is the time occupation proportion. The adjusted capability parameter vector is obtained. If the capability parameter vector is adjusted, the task allocation scheme is recalculated, the technical personnel skill data is extracted from the adjusted capability parameter vector, the task score formula is executed again by combining the task priority and the weighting factor, the new task allocation scheme is obtained, the system state data is updated according to the new task allocation scheme, the task state, the technical personnel state and the time constraint data are obtained from the task allocation scheme, the system state data is generated by using the state aggregation algorithm, and is stored in the multi-user schedule database. The latest system state data is obtained. The real-time task monitoring record is generated through the system state data, the task execution progress and the technical personnel occupation are extracted from the system state data, the real-time task monitoring record is output by using the log generation algorithm, and is stored in the multi-user schedule database. The task monitoring result is obtained.

[0065] The embodiment combines task priority, weighting factor and technician's ability parameter vector to generate real-time task execution log and system state data to optimize task scheduling and resource management. The whole process ensures the accuracy and real-time of task allocation through task scoring, task allocation, execution log generation, ability parameter adjustment and other steps, and improves the response ability and task monitoring ability of the system. The system first calculates the score of each task according to the task priority, weighting factor and ability parameter, and generates a task allocation scheme according to the task score ranking. The task score formula is: S=P×W+C, where S is the task score, P is the task priority, W is the priority weight, and C is the ability parameter. Task priority P: indicates the urgency of the task, usually determined by customer demand or alarm level, priority weight W: used to adjust the influence of task priority in scoring. Usually set by the system, the value range can be dynamically adjusted according to the type of task, ability parameter C: indicates the ability level of the technician, reflecting his execution ability in a certain task. Through the calculation of the task score, the system sorts the tasks and determines the task allocation scheme according to the sorting result. After sorting, the system can ensure that the most urgent and suitable tasks are first allocated to the appropriate technicians.

[0066] According to the task allocation scheme, the system generates a real-time task execution log. This log records the execution of the task, including task identification, execution time and technician identification and other information. The algorithm for generating the log extracts the task identification, technician identification and time information, and stores them in a structured manner in the multi-user schedule database. Task identification: a unique identifier for each task, used to track the progress and execution of the task, execution time: records the start and end time of the task, used for subsequent calculation of task time occupancy and progress, technician identification: indicates the technician who executes the task, used for subsequent calculation of technician's load and time occupancy. After the log is generated, the system will update the time constraint data in the database according to the execution, record the task completion time and the time occupancy of the technician.

[0067] The system obtains the start time and end time of the task from the real-time task execution log, and calculates the time occupancy of the technician. By updating the time constraint field of the technician in the database, the system can accurately reflect the time resources and load of each technician. These time occupancy will affect the subsequent task allocation and adjustment of the ability parameter of the technician.

[0068] According to the time occupation ratio of the technician, the system adjusts the ability parameter vector of the technician. The adjustment formula is: C'=C x (1-T), where C' is the adjusted ability parameter, C is the ability parameter, T is the time occupation ratio, the ability parameter C: represents the initial ability value of the technician, which is usually set based on its historical performance and the complexity of the current task, the time occupation ratio T: is calculated as the proportion of the technician's current load to its total available time. This ratio is usually calculated according to the proportion of the time the technician has spent on the task to its available time. Through this adjustment, the system can accurately reflect the change in the load of the technician and dynamically update its ability parameter. The adjustment of the ability parameter directly affects the allocation scheme of the subsequent tasks, ensuring that the tasks are always allocated to the most suitable technicians.

[0069] After adjusting the ability parameter vector, the system recalculates the task allocation scheme. Based on the new ability parameter vector, the system calculates the score of each task again and generates a new task allocation scheme according to the updated task score ranking. Task status: the execution progress and status of the task, determining whether the task has been completed or needs to be adjusted, technician status: the load and time occupation of each technician, determining whether they can take on new tasks, time constraint data: recording the time requirements of each task to ensure that the task is completed on time.

[0070] By recalculating the task allocation scheme, the system updates the system state data. The system state data contains information such as task status, technician status and time constraint data, and through the state aggregation algorithm, all information is summarized to form the overall state of the system. State aggregation algorithm: integrates the state data of multiple tasks and technicians into a unified system state report, providing a global view. This report helps administrators quickly understand the overall situation of task allocation and the running state of the system.

[0071] The system generates real-time task monitoring records based on the updated system state data and stores them in the multi-user schedule database. This monitoring record provides information such as task execution progress and technician occupation, which is used to track the execution of tasks in real time. Through these monitoring records, the system can timely feedback the progress of the task, help the management personnel adjust the task allocation and resource scheduling, and ensure that all tasks can be completed on time and efficiently.

[0072] For example, in a large enterprise's operation and maintenance management system, hundreds of tasks need to be handled every day, with varying priorities and technical personnel workloads. The system calculates a score for each task by combining task priority and technical personnel capability parameters, and higher priority tasks are assigned to higher matching technical personnel first. During task execution, the system generates real-time execution logs to record task progress and technical personnel time occupation, and updates technical personnel capability parameters based on the logs to ensure continuous optimization of resource allocation. Through this real-time feedback mechanism, the system can adjust task allocation at any time to avoid task delays and technical personnel overloads, improving task execution efficiency and quality.

[0073] While embodiments of the present application have been shown and described, it is to be understood that the embodiments described are merely divergences, modifications, replacements and variations of the embodiments, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for hierarchical alarm handling and task scheduling in a multi-user environment, characterized by, The method comprises the following steps: obtain the candidate allocation scheme by obtaining the skill parameter vector of the technician, the time constraint data and the workload upper limit; calculate the task priority weighting factor of the technician in different time periods, and mark the technician as unavailable if the time constraint data of the technician conflicts with the time requirement of the task, to obtain the first allocation scheme after removing the conflict; obtain the skill parameter vector of other technicians and the workload allocation ratio, generate a substitute technician list closest to the skill parameter vector of the unavailable technician, and obtain the second allocation scheme; sort the substitute technicians according to the task priority weighting factor and the time constraint check to obtain the third allocation scheme; determine the task-person mapping table according to the time constraint check and the skill parameter vector matching requirement of each task; obtain the load change trend of the technician after allocation, re-allocate the task to generate the fifth allocation scheme after adjustment; predict the task allocation stability in the future time period, perform batch comparison operation to regenerate the substitute technician list, and obtain the final allocation scheme; generate the real-time task execution log in combination with the task priority weighting factor and the skill parameter vector, and obtain the system state data; wherein the task-person mapping table is determined by checking the time constraint and the skill parameter vector of each task according to the third allocation scheme, determining the allocation basis data, adjusting the task priority if the time constraint and the skill parameter vector do not match, obtaining the checked task sequence, assigning the technician according to the checked task sequence and the skill parameter vector by using the greedy algorithm, determining the preliminary allocation result, generating the time arrangement through the preliminary allocation result, judging the time conflict, obtaining the optimized time table, generating the fourth allocation scheme according to the optimized time table and the preliminary allocation result, determining the task allocation details, extracting the corresponding relationship between the task and the technician from the fourth allocation scheme, generating the task-person mapping table, checking the matching degree of the allocation scheme and the skill parameter vector according to the task-person mapping table, and determining the final allocation result; the prediction of the task allocation stability in the future time period, the batch comparison operation to regenerate the substitute technician list, and the final allocation scheme obtained by the prediction include: ​ The time series analysis algorithm is used to extract features from historical task allocation data to generate future time period task allocation stability prediction results, obtain allocation stability trend, if the allocation stability trend shows that there is a time constraint check failure risk for a task, the risk identification mechanism is used to extract the failure task identifier from the prediction results to obtain the risk task set, for the risk task set, the pre-established personnel resource pool data is obtained, the batch comparison operation is used to filter the candidate personnel meeting the task demand, the replacement personnel list is generated, the personnel attribute is extracted from the replacement personnel list according to the task priority sorting rule, the dynamic weight is calculated combined with the priority, and the adjusted weight distribution matrix is obtained; the matching degree of the task and the personnel is recalculated through the dynamic weight distribution matrix, the candidate allocation scheme set is generated, the optimized allocation scheme set is obtained, if there are multiple schemes in the candidate allocation scheme set, the time constraint check algorithm is used to verify the time feasibility of each scheme one by one to determine the final allocation scheme, the final allocation scheme is used to update the task allocation database, the stable task allocation result is generated, and the system log is output.

2. The hierarchical alarm processing and task scheduling method for multi-user environment according to claim 1, characterized in that: The candidate allocation scheme includes: The ability parameter vector of the technical personnel, the time constraint data and the workload upper limit are extracted from the schedule database, the structured query language is used to parse multi-user data to obtain an initial personnel data set, the ability parameter vector in the initial personnel data set is processed through vector normalization, a personnel and task matching matrix is generated combined with the task priority list, an intermediate allocation scheme containing time availability and ability score is obtained, if the workload of a certain technical personnel in the intermediate allocation scheme exceeds the upper limit, the task allocation is adjusted through a linear programming algorithm to obtain an optimized candidate allocation scheme, the time availability of each technical personnel is obtained according to the optimized candidate allocation scheme, the task execution time period is determined by using a time window division method, if the task execution time period conflicts with the time constraint data, the task order is adjusted through priority sorting to obtain a conflict-solved allocation scheme, the final task allocation result is determined by comparing the conflict-solved allocation scheme with the ability score through a weighted average method, the final task allocation result is obtained, an allocation record containing technical personnel, tasks and time periods is generated, and is stored in the schedule database.

3. The hierarchical alarm processing and task scheduling method for multi-user environment according to claim 1, characterized in that: The task priority weighting factor of the technical personnel in different time periods is calculated, if the time constraint data of the technical personnel conflicts with the task time requirement, the technical personnel is marked as unavailable, and a first allocation scheme after conflict elimination is obtained. The initial personnel and task matching matrix is acquired, technical personnel, task priority and time period information is extracted, preliminary allocation data containing time constraints is generated, a dynamic integration algorithm is used, task priority weighting factors in each time period are calculated for the preliminary allocation data, a weighting factor set is obtained, if the technical personnel time constraints conflict with the task time requirements, the corresponding technical personnel is marked as unavailable according to the conflict marking rule, and the allocation data after removing the conflict is obtained, the first allocation scheme is generated by combining the allocation data after removing the conflict and the weighting factor set, the matching degree of each task priority and time period is acquired for the first allocation scheme, whether the preset allocation balance threshold is met is judged, and an allocation balance evaluation result is obtained, according to the allocation balance evaluation result, if the matching degree is lower than the preset threshold, the weighting factor is adjusted, the allocation data is recalculated, and an optimized allocation scheme is obtained, the final personnel and task matching relationship is determined through the optimized allocation scheme, and an allocation result containing time periods and task priorities is generated.

4. The hierarchical alarm processing and task scheduling method for multi-user environment according to claim 1, characterized in that: The ability parameter vector and the workload allocation ratio of the other technical personnel are obtained, a backup personnel list closest to the unavailable personnel ability parameter vector is generated, and the second allocation scheme is obtained, which includes: The ability parameter vector and the workload allocation ratio of all technical personnel of the unavailable technical personnel are extracted from the multi-user schedule database, an initial personnel data set is obtained, the similarity between the ability parameter vector of each technical personnel in the initial personnel data set and the ability parameter vector of the unavailable technical personnel is calculated by using a cosine distance, a similarity value set is obtained, wherein the cosine distance formula is: cos (θ) = (A·B) / (||A||·||B||), A is the ability parameter vector of the unavailable technical personnel, B is the ability parameter vector of the other technical personnel, ||A|| and ||B|| represent the modulus of vector A and vector B respectively, the similarity value set is filtered according to a preset threshold, if the similarity value is greater than the preset threshold, the corresponding technical personnel is included in the candidate backup list, an initial backup personnel set is obtained, the workload allocation ratio of each technical personnel is obtained from the initial backup personnel set, if the workload allocation ratio is lower than a preset maximum load threshold, the technical personnel is retained, an optimized backup personnel list is obtained, the similarity values in the optimized backup personnel list are arranged in descending order, an ordered backup personnel sequence is obtained, the second allocation scheme is generated by combining the ordered backup personnel sequence and the task data corresponding to the unavailable technical personnel, and the final backup personnel allocation result is determined, the technical personnel workload allocation ratio in the second allocation scheme is updated from the multi-user schedule database, and an updated schedule data set is obtained.

5. The hierarchical alarm processing and task scheduling method for multi-user environment according to claim 1, characterized in that: The task priority weighting factor and the time constraint are combined for backup personnel sorting, and the third allocation scheme is obtained, which includes: The second allocation scheme is obtained, and a backup personnel list, a task priority weighting factor, and a time constraint condition are obtained. A weighted sorting algorithm is used to calculate the priority of the personnel, and an initial priority order is obtained. According to the initial priority order, the workload data of each backup personnel is obtained, and the load ratio is calculated. If the load ratio exceeds the upper threshold, the corresponding backup personnel is removed, and an updated priority order is obtained. From the updated priority order, the time constraint condition of the personnel is obtained, and it is judged whether the time requirement of the task is met. If not, the order of the personnel is adjusted, and an adjusted priority order is obtained. According to the adjusted priority order, an allocation algorithm is used to generate a third allocation scheme. The allocation result is obtained from the third allocation scheme, and it is judged whether there is a load conflict. If there is, the load ratio is recalculated and the overloaded personnel is removed, and an optimized allocation scheme is obtained. According to the optimized allocation scheme, a final personnel allocation list is generated, and the third allocation scheme is determined.

6. The hierarchical alarm processing and task scheduling method for multi-user environment according to claim 1, characterized in that: The technical personnel load change trend is obtained from the task mapping table, the deviation of the daily task duration of each technical personnel from the historical average is calculated, and the load change trend is obtained. According to the load change trend, the allocation proportion of the current workload of each technical personnel is calculated in combination with the preset upper limit of the workload, and the allocation proportion data is determined. If the allocation proportion of a certain technical personnel exceeds the dynamic threshold, the task allocation is adjusted through a linear programming algorithm to generate a temporary task allocation scheme. The technical personnel load data after task reassignment is extracted from the temporary task allocation scheme, the load balance degree index is calculated, and the balance degree evaluation value is obtained. The balance degree evaluation value is compared with the balance degree of the historical allocation scheme. If the current balance degree is higher than the historical average, the fifth allocation scheme is generated using the current scheme. The task allocation details of each technical personnel are extracted from the fifth allocation scheme, the task mapping table is updated, and the optimized allocation result is obtained. Through the optimized allocation result, the load change trend of each technical personnel is calculated, and it is verified whether the dynamic threshold constraint is met. The final allocation scheme is confirmed. The task priority weighting factor and the capability parameter vector are combined to generate a real-time task execution log, and system state data is obtained.

7. The hierarchical alarm processing and task scheduling method for multi-user environment according to claim 1, wherein: ​ The task priority and the weighting factor are calculated to obtain a task ranking result, determine a task allocation scheme, and the calculation formula is: S=P*W+C, wherein S is a task score, P is a task priority, W is a priority weight, and C is a capability parameter; the task allocation scheme is obtained after ranking; according to the task allocation scheme, a real-time task execution log is generated; task identifiers, execution times, and technical personnel identifiers are extracted from the allocation scheme; a log generation algorithm is used to output a structured execution log and store it in a multi-user schedule database; if the execution log is generated, the time constraint data in the multi-user schedule database is updated; the task start time and the end time are obtained from the execution log; the time occupation of the technical personnel is calculated; the time constraint field of the corresponding technical personnel in the database is updated to obtain updated time constraint data; the technical personnel capability parameter vector is adjusted through the time constraint data; the occupation time proportion is extracted from the time constraint data; the capability parameter vector is updated by combining the task complexity and using vector operation; the specific formula is: C''=C*(1-T), wherein C'' is the adjusted capability parameter, C is the capability parameter, and T is the time occupation proportion; the adjusted capability parameter vector is obtained; if the capability parameter vector is adjusted, the task allocation scheme is recalculated; the technical personnel skill data is extracted from the adjusted capability parameter vector; the task score formula is recalculated by combining the task priority and the weighting factor to obtain a new task allocation scheme; according to the new task allocation scheme, the system state data is updated; the task state, the technical personnel state, and the time constraint data are obtained from the task allocation scheme; a state aggregation algorithm is used to generate system state data and store it in a multi-user schedule database to obtain the latest system state data; the real-time task monitoring record is generated through the system state data; the task execution progress and the technical personnel occupation are extracted from the system state data; a log generation algorithm is used to output the real-time task monitoring record and store it in the multi-user schedule database to obtain the task monitoring result.

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