Search

CN-121979691-A - Elastic capacity expansion method and system for industrial control software private cloud platform

CN121979691ACN 121979691 ACN121979691 ACN 121979691ACN-121979691-A

Abstract

The application provides an elastic capacity expansion method and system of an industrial control software private cloud platform, which relate to the technical field of industrial control and cloud computing fusion, in particular to an elastic capacity expansion method of an industrial control software private cloud platform, and comprise the steps of collecting industrial control alarm messages, production process instructions and business operation data from industrial control software and collecting equipment state data from industrial control equipment; and respectively judging whether the corresponding preset capacity expansion triggering conditions are met according to the industrial control message alarm message, the production process instruction, the service operation data and the equipment state data, if so, calculating capacity expansion resource allocation according to the current basic resource allocation and the preset capacity expansion calculation formula of the industrial control software private cloud platform, and elastically expanding the industrial control software private cloud platform according to the capacity expansion resource allocation. The method and the device can improve the capacity expansion triggering accuracy of the industrial control software private cloud platform and reduce the risk of capacity expansion hysteresis.

Inventors

  • SU HENG
  • Qi Liangsheng
  • LUO YU

Assignees

  • 巨宸科技（深圳）有限公司

Dates

Publication Date
20260505
Application Date
20260408

Claims (13)

  1. 1. An elastic capacity expansion method of an industrial control software private cloud platform is characterized by comprising the following steps: acquiring an industrial control alarm message, a production process instruction and service operation data from industrial control software, and acquiring equipment state data from industrial control equipment; Respectively judging whether corresponding preset capacity expansion triggering conditions are met according to the industrial control alarm message, the production process instruction, the service operation data and the equipment state data, and if judging that any preset capacity expansion triggering condition is met, then: calculating the configuration of the dilatation resources according to the current basic resource configuration of the industrial control software private cloud platform and a preset dilatation calculation formula; and according to the capacity expansion resource configuration, carrying out elastic capacity expansion on the industrial control software private cloud platform.
  2. 2. The method of claim 1, wherein the predetermined capacity expansion triggering conditions include an alarm storm triggering condition, a process switching production increasing triggering condition, a service overload triggering condition, and an equipment failure precursor triggering condition, and wherein the determining whether the corresponding predetermined capacity expansion triggering condition is satisfied according to the industrial control alarm message, the production process instruction, the service operation data, and the equipment state data respectively comprises: judging whether the alarm storm triggering condition is met or not according to the industrial control alarm message and a first preset rule; Judging whether the process switching production-increasing triggering condition is met or not according to the production process instruction and a second preset rule; judging whether the service overload triggering condition is met or not according to the service operation data and a third preset rule; and judging whether the equipment failure precursor triggering condition is met or not according to the equipment state data and a fourth preset rule.
  3. 3. The method of claim 2, wherein the determining whether the alarm storm-triggering condition is satisfied according to the industrial control alarm message and a first preset rule specifically includes: Acquiring alarm data of a plurality of continuous preset alarm statistical periods according to the industrial control alarm message; judging whether the total alarm quantity in the continuous multiple preset alarm counting periods is more than or equal to a preset threshold A1 or not, and judging whether the alarm quantity in the continuous multiple preset alarm counting periods is in an ascending trend or not, if so, confirming that the first alarm triggering condition is met; Judging whether the number of the core alarms in a plurality of continuous preset alarm counting periods is more than or equal to a preset threshold A2 or whether the core alarm duty ratio is more than or equal to a preset threshold A3, if yes, confirming that a second alarm triggering condition is met; judging whether the total alarm growth rate in a plurality of continuous preset alarm statistics periods is more than or equal to a preset threshold A4, or whether the core alarm growth rate is more than or equal to a preset threshold A5, or whether the number of the same-link interlocking alarms is more than or equal to a preset threshold A6, if yes, confirming that a third alarm triggering condition is met; Judging whether the total alarm number in any preset alarm counting period is more than or equal to a preset threshold A7 or whether the core alarm number is more than or equal to a preset threshold A8, if yes, confirming that a fourth alarm triggering condition is met; Judging whether the first alarm triggering condition, the second alarm triggering condition and the third alarm triggering condition are met at the same time or whether the fourth alarm triggering condition is met, and if yes, confirming that the alarm storm triggering condition is met.
  4. 4. The method of claim 2, wherein the determining whether the process switching production-improving trigger condition is satisfied according to the production process instruction and a second preset rule, specifically includes: judging whether the productivity increasing rate after the production process is switched is more than or equal to a preset threshold B1 or whether the production process is switched from a single process mode to a multi-process mode or whether the continuous production batch is more than or equal to a preset threshold B2 or whether the increasing of the number of the collected points after the production process is adjusted is more than or equal to a preset threshold B3 or whether the increasing rate of the control instruction frequency after the production process is adjusted is more than or equal to a preset threshold B4 or not according to the production process instruction, and if yes, confirming that the process switching and the production increasing triggering condition is met.
  5. 5. The method as claimed in claim 2, wherein the determining whether the service overload triggering condition is met according to the service operation data and a third preset rule specifically includes: judging whether the CPU occupancy rate of the acquisition driving thread is more than or equal to a preset threshold C1 or whether the acquisition delay is more than or equal to a preset threshold C2 or whether the operation period of a control algorithm is prolonged to a preset threshold C3 or whether the interlocking response delay is more than or equal to a preset threshold C4 or whether the loading delay is more than or equal to a preset threshold C5 or whether the historical data query timeout time is more than or equal to a preset threshold C6 when a plurality of operation stations access a picture simultaneously, and if yes, confirming that the service overload triggering condition is met.
  6. 6. The method as claimed in claim 2, wherein the determining whether the equipment failure precursor triggering condition is satisfied according to the equipment state data and a fourth preset rule specifically includes: Judging whether the number of the set core industrial control equipment with slight abnormality in a preset time period is more than or equal to a preset threshold D1 according to the equipment state data, and if so, confirming that the equipment fault precursor triggering condition is met, wherein the slight abnormality is that the equipment operation parameters deviate from a set normal range and do not reach a set fault alarm threshold.
  7. 7. The method of claim 2, wherein the preset capacity expansion calculation formula is specifically: Capacity expansion resource allocation = base resource allocation× (1+ trigger level coefficient x service amplification coefficient); The basic resource configuration comprises CPU core number and/or memory capacity, the trigger level coefficient and the service amplification coefficient are set parameters of >0, and the calculated capacity expansion resource configuration takes a preset minimum allocation unit of the industrial control software private cloud platform and does not exceed a preset maximum industrial control configuration.
  8. 8. The method of claim 7 wherein said trigger level factor is set by setting said trigger level factor as a parameter a if only one of said predetermined capacity-expansion trigger conditions is determined to be satisfied, and setting said trigger level factor as a parameter b if a plurality of said predetermined capacity-expansion trigger conditions are determined to be satisfied, wherein a < b.
  9. 9. The method as set forth in claim 7, wherein the setting manner of the service amplification factor includes: if the condition of triggering the alarm storm is judged to be met, the business amplification coefficient= (the number of alarms in the current set time period-the average number of alarms in the set time period under the normal working condition)/the average number of alarms in the set time period under the normal working condition; If the process switching and production increasing triggering condition is judged to be met, the service increasing coefficient value is a larger value in production energy increasing and acquisition point position increasing, wherein the production energy increasing= (new production energy-original production energy)/original production energy, and the acquisition point position increasing= (new acquisition point position number-original acquisition point position number)/original acquisition point position number; if the service overload triggering condition is judged to be met, the service amplification factor takes a value of resource occupation amplification, wherein the resource occupation amplification= (current resource occupation rate-normal resource occupation rate)/normal resource occupation rate; if the equipment failure precursor triggering condition is judged to be met, setting the service amplification coefficient as a default value; And if the current capacity expansion simultaneously meets a plurality of conditions of the alarm storm triggering condition, the process switching production increasing triggering condition, the service overload triggering condition and the equipment failure precursor triggering condition, the service amplification coefficient takes the maximum value in the corresponding calculation result.
  10. 10. The method of claim 1, wherein after calculating the configuration of the dilatation resource according to the current basic resource configuration of the proprietary cloud platform of the industrial control software and the preset dilatation calculation formula, further comprising: Collecting virtual machine hardware resource data and physical machine hardware resource data; judging whether the allocable resources of the physical machine are more than or equal to (the capacity-expanding resource allocation-the basic resource allocation) according to the hardware resource data of the physical machine, and whether the residual resource reservation is more than or equal to a preset threshold E1, if so, confirming that the first capacity-expanding check condition is met; calculating a comprehensive load rate according to the hardware resource data of the virtual machine, judging whether the comprehensive load rate is less than or equal to a preset threshold E2, and if yes, confirming that a second capacity expansion verification condition is met, wherein the comprehensive load rate is a weighted average value of the CPU load rate, the memory load rate and the disk IO load rate of the virtual machine; Judging whether the industrial control software private cloud platform supports online capacity expansion of a CPU and/or a memory of the virtual machine, if so, confirming that a third capacity expansion check condition is met; Judging whether an industrial control service line corresponding to the preset capacity expansion triggering condition met by the current capacity expansion is a preset high-priority service or not, if yes, confirming that a fourth capacity expansion checking condition is met, if not, continuously judging whether allocable resources of a physical machine are not less than or equal to (capacity expansion resource configuration-basic resource configuration) x c, and if yes, confirming that the fourth capacity expansion checking condition is met, wherein c is a set parameter of > 1; Judging whether the capacity expansion resource allocation is less than or equal to the maximum value of the calculated capacity expansion resource allocation under the same preset capacity expansion triggering condition in a preset history statistics period, if yes, confirming that a fifth capacity expansion verification condition is met; judging whether the first capacity expansion checking condition, the second capacity expansion checking condition, the third capacity expansion checking condition, the fourth capacity expansion checking condition and the fifth capacity expansion checking condition are met at the same time, if yes, confirming that the checking is passed, and then executing the step of carrying out elastic capacity expansion on the industrial control software private cloud platform according to capacity expansion resource allocation.
  11. 11. The method as claimed in claim 10, wherein said elastically expanding said industrial control software private cloud platform according to said capacity-expanding resource configuration comprises: and calling an on-line capacity expansion API of the virtual machine of the industrial control software private cloud platform according to the capacity expansion resource configuration, and performing on-line vertical capacity expansion of the CPU and/or the memory.
  12. 12. The method of claim 11, wherein after elastically expanding the industrial control software private cloud platform according to the capacity-expanding resource configuration, further comprising: According to the preset capacity expansion triggering condition met by the current capacity expansion, index monitoring is carried out, whether a preset capacity expansion releasing condition is met or not is judged, and if yes, the method comprises the following steps: pushing a resource recovery reminder to an administrator; acquiring a recycling confirmation instruction input by an administrator based on the resource recycling prompt; and calling a resource recovery API of the industrial control software private cloud platform according to the recovery confirmation instruction, restarting the virtual machine in a set recovery period, and recovering to the basic resource configuration.
  13. 13. An elastic capacity expansion system of an industrial control software private cloud platform is characterized by comprising: the acquisition unit is used for acquiring industrial control alarm messages, production process instructions and service operation data from industrial control software and acquiring equipment state data from industrial control equipment; The triggering unit is used for respectively judging whether the corresponding preset capacity expansion triggering condition is met or not according to the industrial control alarm message, the production process instruction, the service operation data and the equipment state data; The computing unit is used for computing the capacity expansion resource configuration according to the current basic resource configuration of the industrial control software private cloud platform and a preset capacity expansion computing formula when any preset capacity expansion triggering condition is judged to be met; and the capacity expansion unit is used for elastically expanding the capacity of the industrial control software private cloud platform according to the capacity expansion resource allocation.

Description

Elastic capacity expansion method and system for industrial control software private cloud platform Technical Field The application relates to the technical field of industrial control and cloud computing fusion, in particular to an elastic capacity expansion method and system of an industrial control software private cloud platform. Background Along with the acceleration of industrial digital transformation, industrial control software is gradually migrated from an industrial control computer to a private cloud platform, and the purposes of reducing hardware cost, improving operation and maintenance efficiency, improving high availability of an industrial system and the like can be achieved by means of a cloud computing architecture. However, the industrial control scene has extremely high requirements on real-time performance and stability, and when industrial control software runs, the overload of resources can lead to the sudden rise of the CPU and memory loads of the virtual machine within a few seconds. If the cloud platform cannot supplement resources in time, the problems of control instruction delay, operation picture blocking, data acquisition interruption and the like are caused, and production line shutdown and production accidents are caused in severe cases. In the prior art, a universal private cloud platform takes a resource utilization rate threshold value of a virtual machine where industrial control software is located as a capacity expansion unique trigger basis, a trigger system is single, a resource overload scene of the industrial control software private cloud platform cannot be accurately identified, the problem of identification hysteresis exists, and the characteristic of industrial control resource surge cannot be matched. Therefore, how to improve the capacity expansion triggering accuracy of the industrial control software private cloud platform and reduce the risk of capacity expansion hysteresis is a technical problem to be solved urgently by those skilled in the art. Disclosure of Invention In order to solve the technical problems, the application provides the elastic capacity expansion method of the industrial control software private cloud platform, which can improve the capacity expansion triggering accuracy of the industrial control software private cloud platform and reduce the risk of capacity expansion lag. The application also provides an elastic capacity expansion system of the industrial control software private cloud platform, which has the same technical effect. The first aim of the application is to provide an elastic capacity expansion method of an industrial control software private cloud platform. The first object of the present application is achieved by the following technical solutions: an elastic capacity expansion method of an industrial control software private cloud platform comprises the following steps: acquiring an industrial control alarm message, a production process instruction and service operation data from industrial control software, and acquiring equipment state data from industrial control equipment; Respectively judging whether corresponding preset capacity expansion triggering conditions are met according to the industrial control alarm message, the production process instruction, the service operation data and the equipment state data, and if judging that any preset capacity expansion triggering condition is met, then: calculating the configuration of the dilatation resources according to the current basic resource configuration of the industrial control software private cloud platform and a preset dilatation calculation formula; and according to the capacity expansion resource configuration, carrying out elastic capacity expansion on the industrial control software private cloud platform. Preferably, in the elastic capacity expansion method of the industrial control software private cloud platform, the preset capacity expansion triggering conditions include an alarm storm triggering condition, a process switching production increasing triggering condition, a service overload triggering condition and an equipment failure precursor triggering condition, and the judging whether the corresponding preset capacity expansion triggering condition is met or not according to the industrial control alarm message, the production process instruction, the service operation data and the equipment state data respectively includes: judging whether the alarm storm triggering condition is met or not according to the industrial control alarm message and a first preset rule; Judging whether the process switching production-increasing triggering condition is met or not according to the production process instruction and a second preset rule; judging whether the service overload triggering condition is met or not according to the service operation data and a third preset rule; and judging whether the equipment failure precursor triggering condition is met or n