Automatic operation station management method based on cloudization
A management method and work station technology, which are applied in the field of cloud-based automated work station management, can solve problems such as single point failure of work station clusters, increase the operating load of dispatching stations, increase operation and maintenance costs and operating costs, and reduce hardware. The burden of resource occupation, the effect of reducing network overhead costs, and simplifying the processing flow
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0037] A cloud-based automated workstation management method, such as figure 2 shown, including the following steps:
[0038] Step S100: After the dispatching station is started, obtain the list of executable tasks, and calculate the list of tasks to be executed in the next cycle according to the cron expression of the executable tasks;
[0039] Step S200: Obtain the available computing resources TNS and the number of virtual machines MPC of the cloud platform, and the cloud platform provides resources to the dispatching center and initializes the basic operation station;
[0040] Step S300: Count the total running time of the task list to be executed by running historical data. If the total running time is less than half an hour, use the basic operating station to run the task; otherwise, go to step S400;
[0041] Step S400: Analyze the RPA package in each task in the task list to be executed, analyze the RPA package and obtain the RPA action list, and calculate the hardwar...
Embodiment 2
[0050] This embodiment is optimized on the basis of Embodiment 1. In the step S600, the operating station parses out the RPA program package after obtaining the task, and executes the RPA program. During the execution process, the hardware resource occupation of each RPA action is recorded And send it to the control center in real time.
[0051] Further, in the step S600, after the task execution is completed in one cycle, the collected RPA action hardware resource occupancy conditions are summarized and averaged according to different actions, and updated to the corresponding RPA action resource occupancy list In the corresponding hardware requirement parameters.
[0052] Other parts of this embodiment are the same as those of Embodiment 1, so details are not repeated here.
Embodiment 3
[0054] This embodiment is optimized on the basis of Embodiment 1 or 2. In the step S400, analyze the RPA program package of each task in the task list to be executed and extract the RPA action set, according to the RPA action in the RPA action set in the RPA Find the resource occupancy of the corresponding action in the action resource occupation list, calculate the sum of the total CPU resources and memory resources occupied by the RPA action set, and then divide it by the total number of RPA action sets to obtain the average RPA program hardware resource occupancy, which is the task hardware Resource usage.
[0055] Further, in the step S500, the calculation process is as follows:
[0056] Assuming task A requires AN virtual machines, the hardware resource occupation calculated according to step S400 is AP, and the number of tasks calculated according to step S100 is ATC; assuming task B requires BN virtual machines, the hardware resource occupation calculated according to s...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 

