Batch job log checking method and device based on RPA cooperation

Abstract

The invention discloses a batch job log checking method and device based on RPA cooperation. According to the method, after operation information is obtained through an operation checking engine, an RPA robot extracts logs and calls scripts to push checking results to the operation checking engine for keyword analysis, and if abnormity is found, an alarm is generated and pushed to an alarm monitoring platform. An optimized RPA resource scheduling model is provided for dynamically scheduling distribution operation nodes of the RPA in the checking process. The model is a single-cycle job check task allocation optimization model based on cooperation of multiple RPA resources, a balanced scheduling scheme considering multiple targets at the same time can be realized, the efficiency of job check and RPA resource utilization is improved, and a support guarantee is provided for normal operation of batch jobs.

Description

Technical Field

[0001] The present invention relates to the field of automated operation and maintenance technology, and in particular to a batch job log checking method and device based on RPA collaboration. Background Art

[0002] In the scenario of batch job operation in the data center, the job scheduling platform is responsible for the unified scheduling and execution of various types of jobs. According to past experience, although the execution status of the job script is normal, some wrong keywords may appear in the execution log. These errors are not discovered in time. Often, these erroneous jobs are not located until the business occurs or the system resource alarm occurs, which greatly affects the business transaction and system stability. Therefore, the review and confirmation of the log check of the completed jobs is essential. To ensure efficient scheduling, the job scheduling platform only stores the job execution information and does not provide job result storage and automatic analysis functions. This is to reduce the system burden and keep the scheduling platform lightweight and high-performance. Therefore, the log check of batch jobs still relies on manual operations, such as finding log error keywords and comparing the log history length. With the continuous development of business and information systems, the number of batch jobs online has reached tens of thousands. The traditional manual method has exposed many problems: 1) The front-line inspectors are overburdened. The time spent on checking job logs has occupied most of their daily work, and the number of checks that need to be made far exceeds the daily limit; 2) Human and machine resources are large. Although the job inspection process is generally a simple and repetitive operation, human and equipment terminal resources are still required to provide support for inspection; 3) The manual inspection of log error keywords and length is prone to omissions. Inspectors often ignore quality in pursuit of quantity, resulting in the failure to effectively reduce the risk of job operation.

[0003] Robotic Process Automation (RPA) is a technology that uses software programs to execute business processes. It can imitate and replicate the process of manual operation of computer graphical interfaces and automatically execute business processes by driving the mouse and keyboard. For the problem of job log checking, RPA can be used to simulate manual log checking operations to obtain log results. However, in actual situations, facing tens of thousands of running batch jobs, RPA resources are limited. If there is no clear strategy method to dynamically schedule multiple RPA resources for job checking, it is difficult to ensure the completion rate of job checking, the timeliness of abnormality detection, and the realization of high-efficiency resource utilization goals. In related technologies, a mature solution has not yet been found to solve the above problems. Summary of the invention

[0004] In order to solve the needs and problems in the background technology, the present invention proposes a batch job log inspection method and device based on RPA collaboration, which provides a reliable reference basis for subsequent job log inspection.

[0005] The objective of the present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a batch job log checking method based on RPA collaboration, comprising the following steps:

[0006] Step 1: Collect the data information required for the current cycle log inspection task allocation decision; including the set of unassigned inspection jobs, the set of RPAs, and whether a certain inspection task has been completed;

[0007] Step 2: Establish an optimization model for job check task allocation: Considering the job allocation priority and the maximum number of job checks performed by RPA in a cycle, the optimization model for job check task allocation is established with the objective function of minimizing the resource usage cost of RPA, the penalty cost of not allocating jobs according to the continuity rule, and the penalty cost of not allocating jobs for checks.

[0008] Step 3: Find the cost-optimal solution: Use a two-stage hybrid metaheuristic algorithm to solve the task inspection task allocation optimization model;

[0009] Step 4: Perform job inspection: After the scheduling cycle K starts, according to the optimal allocation solution obtained in steps 2 and 3, the RPA robot will perform job inspection work in sequence according to the assigned task priority and inspection plan; when the job inspection is successful, the RPA will feed back the result to the job inspection engine; the job inspection engine analyzes the text content according to the preset alarm keywords and ignore keywords, and if there is an abnormal log, it will generate an alarm information and push it to the alarm monitoring platform.

[0010] Furthermore, the information to be collected in step 1 also includes: the time window width of each inspection cycle , the unit cost of the RPA resource pool, whether each RPA resource has executed the inspection task in the previous cycle to generate a 0-1 matrix. If RPA r has executed the task in the previous cycle, the matrix element is 1, otherwise the matrix element is 0, and the probability of whether the RPA successfully executes a certain inspection task in the historical data The time when RPA checks a single job log.

[0011] Furthermore, in step 2, the assumptions for establishing the optimization model for job inspection task allocation are as follows:

[0012] 1) The maximum number of jobs that can be checked by each RPA robot in each cycle is fixed;

[0013] 2) The inspection time of each job depends on the complexity of the job, which is obtained from historical data;

[0014] 3) RPA robots can process multiple jobs in parallel, but there are resource limitations;

[0015] 4) Jobs that are not checked in time can be accumulated to the next cycle;

[0016] 5) At the beginning and end of each inspection cycle, the time when the RPA robot logs in and out of the log server is negligible;

[0017] 6) At the beginning of each cycle, the robot has completed the inspection task in the previous inspection cycle.

[0018] Furthermore, in step 2, the constraints for establishing the optimization model for job inspection task allocation are as follows:

[0019] 1) When the RPA robot is not running at full capacity, a robot is assigned to each job for inspection during the cycle:

[0020]

[0021] in, is the number of RPA robots, r is the currently available RPA, Generates the elements of a 0-1 matrix for the inspection task. If RPA Assigned inspection work ,but is 1, otherwise is 0, This is the set of jobs that were not assigned for inspection in time due to RPA resource limitations in the previous cycle. The RPA set currently available for job inspection;

[0022] 2) The number of tasks assigned to each robot during an inspection cycle must not exceed its maximum number of inspections:

[0023]

[0024] in, For RPA Check your work time, The time window width for each inspection cycle;

[0025] 3) In Tasks that cannot be assigned for inspection in time within the cycle should be The priority assigned during the check cycle is higher than the jobs to be assigned during this cycle:

[0026] Furthermore, in step 2, the objective function modeling process of establishing the operation inspection task allocation optimization model is as follows:

[0027] The objective function aims to minimize the total cost Z, including the cost of RPA resource usage , the penalty cost for not allocating tasks according to the rules and the penalty cost for not checking the assignment :

[0028]

[0029] in, For the The penalty cost for not checking the continuity rule for each job, RPA for historical data Check your work The probability of success, Penalty cost for units where the job is not inspected.

[0030] Furthermore, in step 3, the two-stage hybrid metaheuristic algorithm for solving the model is further processed as follows:

[0031] 3.1) Initial solution generation

[0032] In this stage, a feasible solution is quickly generated to provide a starting point for the neighborhood search, and a greedy strategy is used to generate an initial solution. The optimization objectives are as follows:

[0033]

[0034] 3.2) Neighborhood search;

[0035] Based on the initial solution, a better solution is found by locally modifying the solution; the details are as follows:

[0036] From the solution Random selection task allocation is reset; for each selected task , reallocated to the RPA with the lowest cost, and obtained an updated solution ;

[0037] Redistribution formula:

[0038]

[0039] Use the acceptance criterion of simulated annealing to decide whether to accept the new solution; that is, if the total cost of the new solution or , then receive the new solution obtained after the optimization operation ;in, represents the solution before optimization, is the change in cost, is the probability of controlling the reception of a poor solution;

[0040] After each iteration, based on the cost change and simulated annealing criteria to decide whether to accept a new solution. If the cost of the new solution is lower, it is accepted directly; if the cost increases, it is accepted with a certain probability;

[0041] 3.3) Perturbation of the solution

[0042] In order to escape from the local optimum, the current solution is disturbed regularly; random selection Task allocation and redistribution; redistribution after disturbance:

[0043]

[0044] 3.4) Solution

[0045] The termination condition is checked after each iteration: 1) the set maximum number of iterations is reached; 2) the change of the current solution is less than the predetermined error range; if the termination condition is met, the algorithm ends; otherwise, it returns to the iterative loop, and the final solution is the optimal solution.

[0046] In a second aspect, the present invention also provides a batch job log checking device based on RPA collaboration, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, the batch job log checking method based on RPA collaboration is implemented.

[0047] In a third aspect, the present invention further provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the batch job log checking method based on RPA collaboration is implemented.

[0048] In a fourth aspect, the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the batch job log checking method based on RPA collaboration.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention proposes a batch job log inspection method and device based on RPA collaboration. The process is that after obtaining the job information through the job inspection engine, the RPA robot extracts the log and calls the script to push the job log inspection information to the job inspection engine for keyword analysis. If an abnormality is found, an alarm is generated and pushed to the alarm monitoring platform. For the allocation of job nodes for dynamic scheduling of RPA in the inspection process, an optimized RPA resource scheduling model is proposed. This model is a single-cycle job inspection task allocation optimization model based on the collaboration of multiple RPA resources. It can realize a balanced scheduling plan that considers multiple goals at the same time, improves the efficiency of job inspection and RPA resource utilization, and provides support and guarantee for the normal operation of batch jobs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 This is a flow chart of a batch job log checking method based on RPA collaboration of the present invention.

[0053] Figure 2 This is a technical architecture diagram of batch job log inspection based on RPA collaboration of the present invention.

[0054] Figure 3 This is a flow chart of the two-stage hybrid metaheuristic algorithm of the present invention.

[0055] Figure 4 It is a structural schematic diagram of a batch job log checking device based on RPA collaboration of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] like Figure 1 As shown, the present invention comprises the following steps:

[0058] Step 1: Collect logs to check the data information required for task allocation decisions;

[0059] Specifically, in the scheduling cycle Before the start time, the job inspection engine collects data information required for log inspection task allocation decision in the Kth cycle from the job scheduling platform. The information to be collected includes:

[0060] 1. The set of jobs that were not assigned for inspection in time due to RPA resource limitations in the previous cycle :

[0061]

[0062] Task In period K-1, it was not allocated due to RPA resource constraints;

[0063] 2. The RPA collection currently available for job checking in the RPA resource pool :

[0064] in, It is the currently available RPA;

[0065] 3. The time window width of each inspection cycle , is a preset fixed value, executed every 10 minutes:

[0066] 4. Unit cost of RPA resource pool :

[0067]

[0068] 5. Generate a 0-1 matrix based on whether each RPA resource has a check task in the previous cycle :

[0069]

[0070] If RPA r executed task j in the previous cycle, then The value is 1, otherwise the value is 0.

[0071] 6. The probability of RPA successfully performing a certain inspection task in historical data :

[0072]

[0073] in, is the probability that RPA r successfully executes task j.

[0074] 7. The time when RPA checks a single job log :

[0075]

[0076] in is the number of RPA robots, To check the number of tasks, is the execution time of RPA r checking task j.

[0077] Figure 2 is a detailed operation inspection technology architecture diagram according to an embodiment of the present invention, such as Figure 2 This architecture diagram shows the interaction and workflow of each component in the job inspection process to achieve the automatic allocation and processing of job log inspection tasks. This technical architecture diagram includes RPA robots, job scheduling platforms, job inspection engines, and alarm monitoring platforms, among which:

[0078] RPA robot: Each RPA robot is responsible for determining the job tasks that need to be checked in each cycle from the job check engine, and obtaining the log data of the job to be checked from the job scheduling platform according to the tasks that need to be checked. Finally, it calls the local task script and feeds back the job log check information to the job check engine.

[0079] Job scheduling platform: This platform is responsible for storing the information of completed jobs in each cycle.

[0080] Job Check Engine: The job check engine is the core of traditional RPA (Robotic Process Automation) and automated log check processes. As a program component that can be embedded in a large management information system, it obtains the information of completed jobs in each cycle from the job scheduling platform, and dynamically schedules multiple RPA resources for resource checks based on the optimal allocation plan. It receives the inspection results of the RPA robot, determines whether the inspection is successful, and decides on subsequent actions. If the inspection is successful, it indicates that the job does not need to be scheduled again in the current cycle. If the inspection fails, RPA will report the reason for the failure, and the job check engine will decide whether to retry immediately or arrange for rescheduling in the next cycle based on the specific situation. It is also responsible for parsing the log content and performing anomaly detection based on preset keywords.

[0081] Alarm monitoring platform: When the job inspection engine detects abnormal logs, the alarm monitoring platform will receive abnormal alarms. The platform is used to monitor and track all potential abnormal events in real time to ensure timely response and processing.

[0082] In each scheduling cycle, the job check engine obtains completed job information from the job scheduling platform and dynamically assigns RPA robots to perform task checks. The RPA robot obtains log data and performs checks by calling local task scripts, and feeds back the check results to the job check engine. The job check engine determines whether to reschedule unfinished tasks based on the feedback, and performs anomaly detection on the log content. If there is an anomaly, it is pushed to the alarm monitoring platform to ensure that the problem is handled in a timely manner.

[0083] Step 2: Establish an optimization model for job inspection task allocation:

[0084] Specifically, considering the job allocation priority and the maximum number of job checks performed by RPA within a cycle, the job check task allocation optimization model is established with the three dimensions of minimizing the resource usage cost of RPA, the penalty cost of not allocating jobs according to the continuity rule, and the penalty cost of not allocating jobs for checks as the objective function:

[0085] 2.1 The model assumes the following conditions:

[0086] 1. The maximum number of jobs that can be checked by each RPA robot in each cycle is fixed;

[0087] 2. The inspection time for each job depends on the complexity of the job, which can be obtained from historical data;

[0088] 3. RPA robots can process multiple jobs in parallel, but there are certain resource limitations;

[0089] 4. Jobs that are not checked in time can be accumulated to the next cycle;

[0090] 5. The time when the RPA robot logs in and out of the log server at the beginning and end of each inspection cycle is negligible;

[0091] 6. At the beginning of each cycle, the robot’s inspection task in the previous inspection cycle has been completed.

[0092] 2.2 The symbols of known variables and decision variables in the model are explained as follows:

[0093] Known variables:

[0094] : No. The unit time cost of each RPA resource;

[0095] : No. The penalty cost for not checking the continuity rule for each job;

[0096] : The unit penalty cost of not checking the job;

[0097] :RPA Check your work time;

[0098] :RPA in historical data Check your work The probability of success;

[0099] : The time window width of each inspection cycle.

[0100] Decision variables:

[0101] If RPA Assigned inspection work , then it is 1, otherwise it is 0.

[0102] 2.3 The model constraints required in the model are as follows:

[0103] 1. When the RPA robot is not fully loaded, a robot is assigned to check each job in the cycle:

[0104]

[0105] 2. The number of tasks assigned to each robot during an inspection cycle must not exceed its maximum number of inspections:

[0106]

[0107] 3. Tasks that cannot be assigned for inspection in time within the cycle should be The priority assigned during the check cycle is higher than the jobs to be assigned during this cycle:

[0108]

[0109] 2.4 The objective function modeling process is as follows:

[0110] The objective function aims to minimize the total cost Z, including the cost of RPA resource usage , the penalty cost for not allocating tasks according to the rules and the penalty cost for not checking the assignment :

[0111]

[0112] Step 3: Find the most cost-optimal solution:

[0113] Specifically, after the start time of the scheduling cycle K, a two-stage hybrid metaheuristic algorithm is used to solve the problem according to the established job inspection task allocation optimization model. This algorithm combines strategies such as initial solution generation, neighborhood search, solution perturbation, and solution acceptance in order to find the most cost-optimized solution under three dimensions: resource constraints, job continuity rules, and penalty costs for unassigned inspections. The two-stage hybrid metaheuristic algorithm for solving the model is as follows: Figure 3 As shown, the solution process is as follows:

[0114] 3.1 Initial solution generation:

[0115] In this stage, a feasible solution is quickly generated to provide a starting point for the neighborhood search. The present invention uses a greedy strategy to generate this initial solution. The optimization objectives are as follows:

[0116]

[0117] satisfy:

[0118]

[0119]

[0120]

[0121] The generation of the initial solution is based on a greedy strategy, which tries to select RPAs with lower costs to perform tasks and quickly generate a feasible solution, as shown in Table 1;

[0122] Table 1 Initial solution generation stage

[0123]

[0124] 3.2 Neighborhood search;

[0125] Based on the initial solution, a better solution is found by modifying the local solution. This stage includes large-area search, solution acceptance criteria and solution perturbation strategy.

[0126] 3.2.1 Large Neighborhood Search

[0127] In each iteration, a certain percentage of RPA tasks are selected for reset and re-optimization. The steps are as follows:

[0128] 1. From the current solution Random selection The task assignments are reset;

[0129] 2. For each selected task , reallocated to the RPA with the lowest cost.

[0130] Redistribution formula:

[0131]

[0132] Neighborhood search searches for better solutions through local modifications, as shown in Table 2. In each iteration, a certain percentage of tasks are selected and reallocated to the RPA with the lowest cost. In this phase of task resetting and reallocation, some tasks (Task2 and Task4) are reallocated to different RPAs in order to reduce the total cost. At this point, although the cost of some tasks increases, the overall cost is reduced through reallocation.

[0133] Table 2 The first iteration of the neighborhood search phase

[0134]

[0135] 3.2.2 Acceptance Criteria for Updated Solutions

[0136] Use the acceptance criterion of simulated annealing to decide whether to accept the new solution. That is, if the total cost of the new solution or , then receive the new solution obtained after the optimization operation .in, represents the solution before optimization, is the change in cost, refers to the temperature, which is used to control the probability of receiving a poor solution.

[0137] After each iteration, based on the cost change and simulated annealing criteria to decide whether to accept a new solution. If the cost of the new solution is lower, it is accepted directly; if the cost increases, there is a certain probability that accept.

[0138] Probability of receiving a bad solution It is controlled by the simulated annealing criterion, and the formula is as follows:

[0139]

[0140] Table 3 Solution acceptance criteria stage

[0141]

[0142] The decision of whether to accept a worse solution is made by calculating the cost change and the random number. From the results in Table 3, we can see that the new solution of Task 4 is not accepted because it increases the total cost.

[0143] 3.3 Perturbations of the solution

[0144] In order to escape from the local optimum, the current solution is regularly Perturbation, denoted as . Random Selection The results of redistribution after disturbance are shown in Table 4:

[0145]

[0146] Table 4 Perturbation phase of the solution

[0147]

[0148] 3.4 Solution

[0149] After each iteration, the termination condition is checked: 1. The maximum number of iterations set is reached 2. The change of the current solution is less than the predetermined error range. If the termination condition is reached, the algorithm ends; otherwise, it returns to the iteration loop. The final solution is the optimal solution.

[0150] Table 5 Final solution and convergence

[0151]

[0152] Step 4: Conduct work inspection:

[0153] Specifically, after the scheduling cycle K starts, according to the optimal allocation solution obtained in steps 2 and 3, the RPA robot will perform the job inspection work in sequence according to the assigned task priority and inspection plan. Each RPA robot will call the local task acquisition script to obtain the inspection job information to be executed by the local RPA from the job scheduling platform. After obtaining the information, the job inspection work begins. After the RPA robot logs in to obtain the log results, it actively calls the script on the RPA inspection machine to push the job inspection results to the job inspection engine. When the job inspection is successful, the RPA will feedback the results to the job inspection engine. If the inspection is successful, it indicates that the job does not need to be scheduled again in the current cycle. If the inspection fails, the RPA will report the reason for the failure, and the job inspection engine will decide to retry immediately or arrange for rescheduling in the next cycle according to the specific situation. In addition, if there is a problem in obtaining job information or logs, the system will retain the failure record for subsequent scheduling adjustments and manual intervention. The job inspection engine analyzes the text content based on the preset alarm keywords and ignore keywords. If there is an abnormal log, it generates an alarm information and pushes it to the alarm monitoring platform.

[0154] Corresponding to the aforementioned embodiment of a batch job log checking method based on RPA collaboration, the present invention also provides an embodiment of a batch job log checking device based on RPA collaboration.

[0155] See also Figure 4 An embodiment of the present invention provides a batch job log checking device based on RPA collaboration, including a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it is used to implement a batch job log checking method based on RPA collaboration in the above embodiment.

[0156] An embodiment of a batch job log checking device based on RPA collaboration provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, if Figure 4 As shown in FIG. 1 , a hardware structure diagram of a batch job log inspection device based on RPA collaboration provided by the present invention is provided in any device with data processing capability, except Figure 4 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0157] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0158] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0159] An embodiment of the present invention further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, a batch job log checking method based on RPA collaboration in the above embodiment is implemented.

[0160] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0161] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the batch job log checking method based on RPA collaboration.

[0162] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A batch job log checking method based on RPA collaboration, characterized in that: The steps include: Step 1: Collect the data information required for the current cycle log inspection task allocation decision; including the set of unassigned inspection jobs, the set of RPAs, and whether a certain inspection task has been completed; Step 2: Establish an optimization model for job check task allocation: Considering the job allocation priority and the maximum number of job checks performed by RPA in a cycle, the optimization model for job check task allocation is established with the objective function of minimizing the resource usage cost of RPA, the penalty cost of not allocating jobs according to the continuity rule, and the penalty cost of not allocating jobs for checks. Step 3: Find the cost-optimal solution: Use a two-stage hybrid metaheuristic algorithm to solve the task inspection task allocation optimization model; Step 4: Perform job inspection: After the scheduling cycle K starts, according to the optimal allocation solution obtained in steps 2 and 3, the RPA robot will perform job inspections in sequence according to the assigned task priority and inspection plan. When the job inspection is successful, the RPA will feed back the result to the job inspection engine. The job inspection engine analyzes the text content based on the preset alarm keywords and ignored keywords. If there are abnormal logs, it generates alarm information and pushes it to the alarm monitoring platform.

2. According to the RPA collaboration-based batch job log checking method of claim 1, it is characterized in that: The information that needs to be collected in step 1 also includes: the time window width of each inspection cycle , the unit cost of the RPA resource pool, whether each RPA resource has executed the inspection task in the previous cycle to generate a 0-1 matrix. If RPA r has executed the task in the previous cycle, the matrix element is 1, otherwise the matrix element is 0, as well as the probability A of whether the RPA successfully executes a certain inspection task in the historical data and the time when the RPA checks a single job log.

3. According to the RPA collaboration-based batch job log checking method of claim 1, it is characterized in that: In step 2, the assumptions for establishing the optimization model for job inspection task allocation are as follows: 1) The maximum number of jobs that can be checked by each RPA robot in each cycle is fixed; 2) The inspection time of each job depends on the complexity of the job, which is obtained from historical data; 3) RPA robots can process multiple jobs in parallel, but there are resource limitations; 4) Jobs that are not checked in time can be accumulated to the next cycle; 5) At the beginning and end of each inspection cycle, the time when the RPA robot logs in and out of the log server is negligible; 6) At the beginning of each cycle, the robot has completed the inspection task in the previous inspection cycle.

4. According to the RPA collaboration-based batch job log checking method of claim 1, it is characterized in that: In step 2, the constraints for establishing the optimization model for job inspection task allocation are as follows: 1) When the RPA robot is not running at full capacity, a robot is assigned to each job for inspection during the cycle: ； Where m is the number of RPA robots, r is the currently available RPA, Generates the elements of a 0-1 matrix for inspection tasks. If RPAr is assigned to inspection job j, then is 1, otherwise is 0, This is the set of jobs that were not assigned for inspection in time due to RPA resource limitations in the previous cycle. The RPA set currently available for job inspection; 2) The number of tasks assigned to each robot during an inspection cycle must not exceed its maximum number of inspections: ； in, Check the time of job j for RPAr, The time window width for each inspection cycle; 3) Tasks that cannot be assigned for inspection in time during the K-1 cycle should be assigned with a higher priority during the K inspection cycle than the tasks to be assigned in this cycle: 。 5. According to the RPA collaboration-based batch job log checking method of claim 1, it is characterized in that: In step 2, the objective function modeling process of establishing the operation inspection task allocation optimization model is as follows: The objective function aims to minimize the total cost Z, including the cost of RPA resource usage , the penalty cost for not allocating tasks according to the rules and the penalty cost for not checking the assignment : ； in, is the penalty cost for not checking the jth job according to the continuity rule, is the probability that RPAi checks whether job j is successful in the historical data, and q is the unit penalty cost of a job not being checked.

6. According to the RPA collaboration-based batch job log checking method of claim 1, it is characterized in that: In step 3, the two-stage hybrid metaheuristic algorithm for solving the model is further processed as follows: (3.1) Initial solution generation: In this stage, a feasible solution is quickly generated to provide a starting point for the neighborhood search, and a greedy strategy is used to generate an initial solution. The optimization objectives are as follows: ； (3.2) Neighborhood search; Based on the initial solution, a better solution is found by locally modifying the solution; the details are as follows: From the solution Randomly select k tasks to reset; for each selected task j, reallocate it to the RPA with the lowest cost, and get the updated solution ; Redistribution formula: ； Use the acceptance criterion of simulated annealing to decide whether to accept the new solution; that is, if the total cost of the new solution or , then receive the new solution obtained after the optimization operation ;in, represents the solution before optimization, is the change in cost, is the probability of controlling the reception of a poor solution; After each iteration, based on the cost change and simulated annealing criteria to decide whether to accept a new solution; if the cost of the new solution is lower, it is accepted directly; if the cost increases, it is accepted with a certain probability; (3.3) Perturbation of the solution: In order to escape from the local optimum, the current solution is regularly Perform a perturbation; randomly select k tasks and redistribute them; redistribute after perturbation: ； (3.4) Solution: The termination condition is checked after each iteration: 1) the set maximum number of iterations is reached; 2) the change of the current solution is less than the predetermined error range; if the termination condition is met, the algorithm ends; otherwise, it returns to the iterative loop, and the final solution is the optimal solution.

7. A batch job log checking device based on RPA collaboration, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a batch job log checking method based on RPA collaboration is implemented as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, a batch job log checking method based on RPA collaboration according to any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, a batch job log checking method based on RPA collaboration is implemented.

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