CN-120805779-B - Reinforced computer dust particle prediction and self-adaptive dust prevention method and system

CN120805779BCN 120805779 BCN120805779 BCN 120805779BCN-120805779-B

Abstract

The invention relates to the technical field of reinforcement computer protection, in particular to a reinforcement computer dust particle prediction and self-adaptive dust prevention method and system. The method comprises the steps of constructing an air duct airflow state boundary model, estimating movement paths of dust particles with different particle diameters based on the model, executing collision detection and stagnation track analysis to generate a deposition probability distribution structure, extracting a high deposition risk area, constructing an electrostatic diversion field regulation structure and calculating diversion electrode control parameters, generating an electrode instruction to drive a diversion control module to deflect the dust particles, configuring a dust collection area according to the dust particle deflection paths and carrying out particle adsorption, and generating a feedback control vector by combining residual dust particle monitoring data and the control parameters to realize self-adaptive updating of electrostatic regulation. According to the invention, on the premise of no need of a sealing structure, the behavior of dust particles can be accurately predicted based on the CFD and the differential model, and the deposition trend of the dust particles can be dynamically regulated and controlled, so that high-reliability active dust prevention control under a complex running environment is realized.

Inventors

WU YANG
WANG YUYAN
LU GUOLIANG
YANG JINYU

Assignees

北京研信通科技有限公司

Dates

Publication Date: 20260508
Application Date: 20250802

Claims (10)

1. The method for predicting and adaptively preventing dust particles of a reinforced computer is characterized by comprising the following steps of: Acquiring wind speed data, temperature data and pressure data raw data through a miniature multifunctional environment sensor array, performing data field merging processing and time stamp alignment operation on the acquired raw data, and constructing a stable airflow parameter set to generate an airflow state boundary model, wherein the sensor array comprises a thermosensitive wind speed probe, a thermocouple type temperature acquisition unit and a piezoresistive type miniature pressure sensor; Estimating a dust particle motion path based on the airflow state boundary model, performing collision detection and stagnation track calibration, generating candidate deposition paths, and calculating a deposition probability distribution structure; The deposition probability distribution is expressed as follows: Wherein, the Representing the dust particle deposition probability in the space element V m , wherein Z is a normalization factor, and N c represents the total candidate path number; x j represents the movement path of the jth dust particle; as an indication function; acquiring a deposition probability distribution structure, extracting a deposition high-risk region, generating an electrode regulation region, constructing an electrostatic diversion field regulation structure, and calculating a control parameter matrix of a diversion electrode; Acquiring a control parameter matrix of the diversion electrode, converting the control parameter matrix into an electrode instruction, driving a diversion control module, executing dust particle deflection action, and generating dust particle deflection path data; Configuring a dust collecting area according to the dust particle deflection path data, performing space aggregation and particle adsorption operation, and generating residual dust particle monitoring data; and generating a feedback control vector by combining the residual dust particle monitoring data and the historical control parameters, and updating the control parameters to complete self-adaptive adjustment.
2. The ruggedized computer dust particle prediction and adaptive dust prevention method of claim 1, wherein the constructing a stable airflow parameter set comprises: acquiring wind speed, temperature and pressure data in the air duct, and performing structural analysis and format unified processing to generate air duct airflow characteristic data; normalizing and disturbance removal processing is carried out on the air channel air flow characteristic data to generate a stable air flow parameter set; An airflow state boundary model is constructed based on the set of stable airflow parameters.
3. The ruggedized computer dust particle prediction and adaptive dust prevention method of claim 1, wherein the estimating a dust particle motion path comprises: and acquiring an airflow state boundary model, and executing dust particle motion path estimation by combining the particle size range setting parameters.
4. The ruggedized computer dust particle prediction and adaptive dust prevention method of claim 1, wherein the performing collision detection and dead-reckoning comprises: And performing collision detection and stagnation track calibration on the dust particle motion path to generate a candidate deposition path.
5. The ruggedized computer dust particle prediction and adaptive dust prevention method of claim 1, wherein the calculating a deposition probability distribution structure comprises: and calculating a deposition probability distribution structure in the air channel area based on the candidate deposition paths.
6. The method for predicting and adaptively preventing dust in a reinforced computer according to claim 1, wherein said constructing a static conductance flow field regulation structure comprises: Acquiring a deposition probability distribution structure, extracting a deposition high-risk region and constructing a space control region structure; Performing electric field boundary segmentation and target offset direction planning on the space control region structure to generate an electrode regulation and control region; and generating a static conductance flow field regulating structure based on the electrode regulating region, and calculating a control parameter matrix of the diversion electrode.
7. The ruggedized computer dust particle prediction and adaptive dust prevention method of claim 1, wherein the performing a dust particle deflection action comprises: Acquiring a control parameter matrix of the diversion electrode, and performing driving signal conversion and electrode instruction generation processing; the electrode instruction is sent to a diversion control module, and programmable diversion electrode control operation is executed; And executing dust particle deflection action according to the control operation of the programmable diversion electrode, and generating dust particle deflection path data.
8. The ruggedized computer dust particle prediction and adaptive dust prevention method of claim 1, wherein the generating residual dust particle monitoring data comprises: acquiring dust particle deflection path data, configuring a dust collection target area and dispatching a dust collection module; performing space aggregation and particle adsorption operation of the dust collection module to generate dust collection control result data; and carrying out residual particle identification and counting processing on the dust collection control result data to generate residual dust particle monitoring data.
9. The method of ruggedized computer dust particle prediction and adaptive dust prevention of claim 1, wherein the performing an adaptive adjustment comprises: acquiring residual dust particle monitoring data, and generating a feedback control vector by combining a deposition probability distribution structure with a control parameter matrix; Updating control parameters in the static conductance flow field regulation structure according to the feedback control vector; Reinjecting the control parameter updating result to the electrode instruction generating flow to complete the self-adaptive parameter adjusting process.
10. A ruggedized computer dust particle prediction and adaptive dust prevention system for use in a ruggedized computer dust particle prediction and adaptive dust prevention method according to any one of claims 1 to 9, comprising: The airflow parameter acquisition module is used for acquiring the wind speed, temperature and pressure data and constructing an airflow state boundary model; the dust particle path prediction module is used for estimating a dust particle motion path based on the airflow state boundary model and generating a deposition probability distribution structure; The static electricity conduction field generation module is used for acquiring the deposition probability distribution structure, generating a static electricity conduction field regulation structure and calculating a control parameter matrix; The flow guide electrode execution module is used for converting the control parameter matrix into a flow guide electrode instruction and executing dust particle deflection operation; the dust collection and residual monitoring module is used for configuring a dust collection area according to the dust particle deflection path data and collecting residual dust particle monitoring data; And the self-adaptive updating module is used for generating a feedback control vector according to the residual dust particle monitoring data and the control parameter matrix and finishing the control parameter updating process.

Description

Reinforced computer dust particle prediction and self-adaptive dust prevention method and system Technical Field The invention relates to the technical field of reinforcement computer protection, in particular to a reinforcement computer dust particle prediction and self-adaptive dust prevention method and system. Background The invention relates to the technical field of reinforced computer protection, and along with the wide deployment of high-performance computing equipment, the sensitivity of the high-performance computing equipment to particle deposition in a closed air duct, the heat dissipation efficiency of an air cooling system and the long-term stability is obviously improved. Dust particles are easy to deposit on the surfaces of the air duct wall surface, the circuit element and the heat dissipation module in the running process of the equipment, so that the rise of thermal resistance, the electrical short circuit and the performance degradation are caused. The traditional dust prevention mode depends on a physical filter screen, periodic cleaning or passive airflow design, so that the movement trend of dust particles cannot be identified in real time, and active deflection control is difficult to execute aiming at high-risk areas. In the prior art, although particle prediction models based in part on hydrodynamic simulation exist, the particle prediction models stay on macroscopic estimation or two-dimensional path track level, a layered modeling mechanism for microscopic behaviors under the dimension of the particle size of dust particles is lacked, and comprehensive evaluation means for particle-boundary interaction, stagnation probability and spatial deposition tendency are also lacked. In addition, although the electrostatic diversion technology can be used for dust deflection, a real-time feedback linkage mechanism is not formed with the particle distribution prediction module, so that the diversion and control strategy is difficult to match with the actual particle distribution situation, and the response efficiency and the coverage precision of the dustproof system are affected. Therefore, a comprehensive particle behavior prediction method integrating stable airflow boundary modeling, particle size layering path estimation, collision detection and deposition probability estimation mechanisms is needed, and an adaptive dust prevention system capable of dynamically identifying a deposition high risk area, precisely driving a deflection path and realizing closed loop feedback regulation through residual particle monitoring is constructed by combining an electrostatic diversion control unit, so that the core problems of inaccurate particle prediction, lag of a prevention and control strategy, closed loop regulation and the like in the prior art are solved. Disclosure of Invention The invention provides a reinforced computer dust particle prediction and self-adaptive dust prevention method and system, which are used for solving the problems of how to integrate collision detection and path deposition probability functions based on stable airflow boundary modeling and particle size layered path estimation, generating a high-precision dust particle distribution prediction result and driving an electrostatic diversion deflection system to complete self-adaptive closed-loop dust prevention regulation and control. In order to solve the technical problems, the invention provides a reinforced computer dust particle prediction and self-adaptive dust prevention method, which comprises the following steps: Acquiring wind speed data, temperature data and pressure data raw data through a miniature multifunctional environment sensor array, performing data field merging processing and time stamp alignment operation on the acquired raw data, and constructing a stable airflow parameter set to generate an airflow state boundary model, wherein the sensor array comprises a thermosensitive wind speed probe, a thermocouple type temperature acquisition unit and a piezoresistive type miniature pressure sensor; Estimating a dust particle motion path based on the airflow state boundary model, performing collision detection and stagnation track calibration, generating candidate deposition paths, and calculating a deposition probability distribution structure; The deposition probability distribution is expressed as follows: Wherein, the Representing the dust particle deposition probability in the space element V m, wherein Z is a normalization factor, and N c represents the total candidate path number; x j represents the movement path of the jth dust particle; as an indication function; acquiring a deposition probability distribution structure, extracting a deposition high-risk region, generating an electrode regulation region, constructing an electrostatic diversion field regulation structure, and calculating a control parameter matrix of a diversion electrode; Acquiring a control parameter matrix of the diversion el