Ruggedized computer dust particle prediction and self-adaptive dust prevention method and system

Abstract

The invention relates to the technical field of ruggedized computer protection, in particular to a ruggedized computer dust particle prediction and self-adaptive dust prevention method and system. The method comprises the following steps: constructing an air duct airflow state boundary model; motion paths of dust particles with different particle sizes are estimated based on the model, collision detection and stagnation trajectory analysis are executed, and a deposition probability distribution structure is generated; extracting a high deposition risk area, constructing an electrostatic diversion field regulation and control structure, and calculating diversion electrode control parameters; generating an electrode instruction to drive the diversion control module to deflect dust particles; configuring a dust collection area according to the dust particle deflection path and carrying out particle adsorption; and a feedback control vector is generated in combination with the residual dust particle monitoring data and the control parameters, so that self-adaptive updating of electrostatic regulation and control is realized. On the premise that a sealing structure is not needed, dust particle behaviors can be accurately predicted based on CFD and a differential model, the deposition trend of the dust particles is dynamically regulated and controlled, and high-reliability active dustproof control in a complex operation environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer reinforcement protection, and in particular to a method and system for dust particle prediction and adaptive dust prevention of reinforced computers. Background Art

[0002] The present invention relates to the field of computer protection reinforcement technology. With the widespread deployment of high-performance computing equipment, its sensitivity to particle deposition in closed air ducts, the heat dissipation efficiency of air cooling systems, and long-term stability has significantly increased. During equipment operation, dust particles are easily deposited on the walls of air ducts, circuit components, and the surface of heat dissipation modules, causing increased thermal resistance, electrical short circuits, and performance degradation. Traditional dust prevention methods mostly rely on physical filters, periodic cleaning, or passive airflow design. They are unable to identify dust particle movement trends in real time, and it is difficult to perform active deflection control in high-risk areas.

[0003] While existing technologies include some particle prediction models based on fluid dynamics simulations, these models often remain at the macroscopic level of estimation or two-dimensional path trajectories. They lack a hierarchical modeling mechanism for the microscopic behavior of dust particles at the particle size level, nor do they offer comprehensive assessments of particle-boundary interactions, stagnation probability, and spatial deposition tendencies. Furthermore, while electrostatic deflection technology can be used for dust deflection, it lacks a real-time feedback mechanism integrated with particle distribution prediction modules. This makes it difficult for control strategies to match actual particle distribution patterns, impacting the response efficiency and coverage accuracy of dust control systems.

[0004] Therefore, there is an urgent need for a comprehensive particle behavior prediction method that integrates stable airflow boundary modeling, particle size stratification path estimation, collision detection and deposition probability assessment mechanism, and cooperates with the electrostatic diversion control unit to build an adaptive dust prevention system that can dynamically identify high-risk deposition areas, accurately drive deflection paths, and achieve closed-loop feedback control through residual particle monitoring, so as to solve the core problems of existing technologies such as inaccurate particle prediction, lagging prevention and control strategies, and lack of adjustment closed loops. Summary of the Invention

[0005] The present invention provides a reinforced computer dust particle prediction and adaptive dust prevention method and system to solve the problem of how to generate high-precision dust particle distribution prediction results based on stable airflow boundary modeling and particle size stratification path estimation, integrate collision detection and path deposition probability function, and drive the electrostatic diversion deflection system to complete adaptive closed-loop dust prevention control.

[0006] In order to solve the above technical problems, the present invention provides a method for strengthening computer dust particle prediction and adaptive dust prevention, comprising:

[0007] The wind speed data, temperature data and pressure data original data are collected by a micro multi-functional environment sensor array, the collected original data is subjected to data field merging processing and time stamp alignment operation, a stable airflow parameter set is constructed, and an airflow state boundary model is generated; the sensor array includes a thermal type wind speed probe, a thermocouple type temperature collection unit and a piezoresistive micro pressure sensor; the wind speed collection data adopts a three-dimensional vector recording format, the temperature data adopts an absolute value storage mode in Celsius, and the pressure data is recorded in Pascal units; the airflow state boundary model includes a wind channel three-dimensional structure and a control body point mapping relationship, a boundary input surface airflow vector assignment structure, a temperature pressure coupling mapping table and an air pressure gradient atlas;

[0008] Based on the airflow state boundary model, the dust particle motion path is estimated, the collision detection and stagnation trajectory calibration are performed, the candidate deposition path is generated, and the deposition probability distribution structure is calculated;

[0009] The expression of the deposition probability distribution is:

[0010]

[0011] Wherein, represents the dust particle deposition probability in the space body element V m ; Z is a normalization factor; N c represents the total number of candidate paths; is the deposition probability prediction value of the jth path; X j represents the jth dust particle motion path; is an indicator function;

[0012] The deposition probability distribution structure is obtained, the deposition high-risk area is extracted and the electrode regulation area is generated, the electrostatic flow field regulation structure is constructed, and the control parameter matrix of the flow guide electrode is calculated;

[0013] The control parameter matrix of the flow guide electrode is obtained, the control parameter matrix is converted into electrode instructions and the flow guide control module is driven, the dust particle deflection action is performed, and dust particle deflection path data is generated;

[0014] According to the dust particle deflection path data, a dust collection area is configured, a space aggregation and particle adsorption operation is performed, and residual dust particle monitoring data is generated;

[0015] The feedback control vector is generated by combining the residual dust particle monitoring data and the historical control parameter, and the control parameter is updated to complete adaptive adjustment.

[0016] Further, the construction of the stable airflow parameter set includes:

[0017] The wind speed, temperature and pressure data in the air duct are obtained, subjected to structured analysis and format uniform processing, and air duct airflow characteristic data is generated;

[0018] The wind channel airflow characteristic data is normalized and disturbance removed to generate a stable airflow parameter set;

[0019] A airflow state boundary model is constructed based on the stable airflow parameter set.

[0020] Further, the estimated dust particle motion path includes:

[0021] The airflow state boundary model is obtained, combined with the particle size range setting parameters, and the dust particle motion path estimation is performed.

[0022] Further, the collision detection and stagnation trajectory calibration includes:

[0023] The dust particle motion path is subjected to collision detection and stagnation trajectory calibration to generate a candidate deposition path.

[0024] Further, the calculation of the deposition probability distribution structure includes:

[0025] The deposition probability distribution structure in the wind channel region is calculated based on the candidate deposition path.

[0026] Further, the construction of the electrostatic flow field regulation structure includes:

[0027] The deposition probability distribution structure is obtained, the deposition high-risk area is extracted, and the space control area structure is constructed;

[0028] The space control area structure is subjected to electric field boundary segmentation and target offset direction planning to generate an electrode regulation area;

[0029] The electrostatic flow field regulation structure is generated based on the electrode regulation area, and the control parameter matrix of the flow guiding electrode is calculated.

[0030] Further, the dust particle deflection action includes:

[0031] The control parameter matrix of the flow guiding electrode is obtained, and the driving signal conversion and electrode instruction generation processing are performed;

[0032] The electrode instruction is sent to the flow guiding control module, and the programmable flow guiding electrode control operation is performed;

[0033] The dust particle deflection action is performed according to the programmable flow guiding electrode control operation to generate dust particle deflection path data.

[0034] Further, the generation of residual dust particle monitoring data includes:

[0035] The dust particle deflection path data is obtained, the dust collection target area is configured, and the dust collection module is dispatched;

[0036] The space aggregation and particle adsorption operation of the dust collection module is performed to generate dust collection control result data;

[0037] Residual particle identification and counting processing is performed on the dust collection control result data to generate residual dust particle monitoring data.

[0038] Further, the complete adaptive adjustment comprises:

[0039] Residual dust particle monitoring data is obtained, and a feedback control vector is generated by combining the deposition probability distribution structure and the control parameter matrix.

[0040] The control parameters in the electrostatic flow field regulation structure are updated according to the feedback control vector.

[0041] The control parameter update result is re-injected into the electrode instruction generation process to complete the adaptive parameter adjustment processing.

[0042] A rugged computer dust particle prediction and adaptive dust prevention system is applied to the rugged computer dust particle prediction and adaptive dust prevention method described in any of the above, comprising:

[0043] An air flow parameter acquisition module is configured to acquire wind speed, temperature and pressure data and construct an air flow state boundary model.

[0044] A dust particle path prediction module is configured to estimate a dust particle motion path based on the air flow state boundary model and generate a deposition probability distribution structure.

[0045] An electrostatic flow field generation module is configured to obtain the deposition probability distribution structure, generate an electrostatic flow field regulation structure and calculate a control parameter matrix.

[0046] A flow guide electrode execution module is configured to convert the control parameter matrix into flow guide electrode instructions and perform dust particle deflection operations.

[0047] A dust collection and residual monitoring module is configured to configure a dust collection area according to dust particle deflection path data and collect residual dust particle monitoring data.

[0048] An adaptive update module is configured to generate a feedback control vector according to the residual dust particle monitoring data and the control parameter matrix, and complete control parameter update processing.

[0049] The key innovations of the present application include:

[0050] (1) CFD-based particle size layered motion path modeling and Reynolds number coupled trajectory derivation mechanism. Realize the dynamic correlation between particle velocity, position, resistance and air flow velocity, introduce "path level differential estimation and dynamic scoring" closed loop modeling in the dust prevention of air cooling system, with high precision and real-time performance.

[0051] (2) Construct the deposition probability function of the space candidate path and the three-dimensional mapping structure. Introduce an explicit regional statistical mechanism to form a voxel-level probability heat map, breaking through the defect of no prediction ability in the traditional dust prevention system, and providing accurate positioning basis for the deflection strategy.

[0052] (3) Electrode regulation region automatic generation and real-time calculation method of electric field deflection direction. The system generates a space electrode control region and an electrostatic conduction parameter matrix through S310-S330 to realize accurate guidance of the electric field before the air inlet and effectively avoid the problem of "deflection misplacement".

[0053] (4) Construction and execution mechanism of multi-level feedback adaptive control path. The system introduces a residual dust particle monitoring result and control parameter comparison mechanism, which can adjust the deflection strength and space control area coverage range in the reverse direction according to the residual dust particle distribution, realizing periodic self-adjustment.

[0054] The following are its main beneficial effects:

[0055] (1) The present application constructs a stable airflow state boundary model in S100 step, and introduces a Lagrangian path differential model based on Reynolds number and Stokes resistance in S210-S230 to realize fine estimation of the space path of dust particles of different particle sizes; and combines collision detection and stagnation evaluation mechanism with space deposition probability mapping function to accurately identify dust particle deposition hot spot area in the air duct, which has lower prediction error compared with traditional particle distribution method based on empirical rules or macroscopic estimation.

[0056] (2) In S300-S330, the electrode regulation region is constructed and the electrostatic conduction control parameter matrix is generated, combined with the instruction conversion and programmable conduction execution mechanism in S400, the deflection direction and voltage intensity can be automatically adjusted according to the change of dust particle space distribution. Further through S600 feedback path, the residual dust particle monitoring result is fed back to the control matrix generation module to form a closed loop adaptive adjustment path, effectively improving the continuous accuracy and real-time performance of the control strategy in complex environment.

[0057] (3) Traditional dust prevention scheme usually relies on fixed filter screen and other mechanical blocking structure, which is easy to block and affect the heat dissipation performance of the system. The present application introduces an adjustable electrostatic conduction system to dynamically apply electric field guidance based on dust particle path prediction results, realizing "path guided" dust particle deflection control. In the area where the deposition trend is significant, an electric field barrier is formed in advance to make the dust particles deviate from the key heat sensitive element area and gather in the detachable dust collection module, which balances the air flow and dust particle isolation, effectively reduces the dependence on static dust prevention structure, and improves the heat dissipation stability and protection response ability of the system.

[0058] (4) The invention splits the whole method into airflow modeling, particle path simulation, region regulation generation, control execution, deflection effect collection and feedback self-regulation in a modular manner, forming a self-consistent data flow closed loop and structure mapping closed loop. The system can be reused in other air-cooled equipment scenarios, such as military servers, rail transit computing platforms, etc., with universal adaptation capability. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A flowchart of a reinforced computer dust particle prediction and adaptive dust prevention method provided by an embodiment of the present application is provided.

[0060] Figure 2 A structural block diagram of a reinforced computer dust particle prediction and adaptive dust prevention system provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0061] Embodiment one: reference Figure 1 A flowchart of a reinforced computer dust particle prediction and adaptive dust prevention method provided by an embodiment of the present application is provided, which can at least include steps S100-S600:

[0062] S100, by acquiring wind speed, temperature and pressure data, a stable airflow parameter set is constructed to generate an airflow state boundary model.

[0063] S200, based on the airflow state boundary model, the dust particle motion path is estimated, the collision detection and stagnation trajectory calibration are performed, the candidate deposition path is generated, and the deposition probability distribution structure is calculated.

[0064] S300, the deposition probability distribution structure is obtained, the deposition high-risk area is extracted and the electrode regulation area is generated, the electrostatic conductive field regulation structure is constructed, and the control parameter matrix of the conductive electrode is calculated.

[0065] S400, the control parameter matrix of the conductive electrode is obtained, the control parameter matrix is converted into electrode instructions and drives the conductive control module, performs dust particle deflection action, and generates dust particle deflection path data.

[0066] S500, according to the dust particle deflection path data, the dust collection area is configured, the space aggregation and particle adsorption operation is performed, and the residual dust particle monitoring data is generated.

[0067] S600, combining the residual dust particle monitoring data and the historical control parameter to generate a feedback control vector, updating the control parameter to complete adaptive adjustment.

[0068] Step S100 at least includes steps S110-S130:

[0069] S110, the wind speed, temperature and pressure data in the air duct are obtained, and structured analysis and format uniform processing are performed to generate air duct airflow characteristic data.

[0070] Specifically, first, the micro multi-functional environmental sensor array arranged in multiple points of the cabinet heat dissipation air duct collects wind speed data, temperature data and pressure data. The sensor array includes a thermal type wind speed probe, a thermocouple type temperature collection unit and a piezoresistive micro pressure sensor, which are arranged at the inlet section, the middle section and the area close to the heat dissipation channel of the key components of the air duct, respectively, and the sampling frequency is not less than 50 times per second, meeting the dynamic flow field capture requirement. The wind speed collection data adopts a three-dimensional vector recording format, the temperature data adopts an absolute value storage method in Celsius, and the pressure data is recorded in Pascal units.

[0071] For the collected multi-dimensional original data, an embedded data preprocessing module is called to perform structured analysis on the original wind speed, temperature and pressure data. The structured analysis process includes data field merging processing and time stamp alignment operation. The wind speed data is field expanded according to the airflow direction (i.e. x-axis, y-axis, z-axis), a wind speed vector list is constructed, the temperature and pressure data are uniformly mapped to the corresponding wind speed sampling points according to the spatial coordinates, and the sampling time identifier is synchronized to form a joint sampling data matrix with spatio-temporal consistency.

[0072] Further, the parsed data is subjected to format unification processing, specifically including data unit conversion, data missing point interpolation and abnormal value elimination operation. Unit conversion includes converting wind speed into standard meter per second format, temperature data into standard physical degree, and pressure data into absolute pressure format; missing point interpolation uses linear interpolation or cubic spline interpolation method to complete the missing measurement in the time period caused by occasional failure of the sensor; abnormal value elimination uses the z-score statistical method based on sliding window to mark the mutation data exceeding 3σ range as abnormal and discard or smooth correct it. After the above processing, the air duct airflow characteristic data structure containing wind speed vector, temperature scalar and pressure scalar of each sampling point is generated.

[0073] The above air duct airflow characteristic data will be directly used as the input structure of S120 step, supporting the stable airflow parameter set generation operation, and also as the initial boundary parameter input of the dust particle path prediction model construction in S200.

[0074] S120, normalizing and removing disturbance of the air duct airflow characteristic data to generate a stable airflow parameter set.

[0075] Specifically, first, normalization processing is performed on the wind tunnel airflow characteristic data. The wind speed vector adopts component normalization, and for each wind speed direction quantity, a maximum minimum normalization function is constructed to map the three-axis wind speed value one by one; the temperature data and the pressure data adopt mean variance normalization, and the instantaneous data is dimensionless processed based on the historical mean and standard deviation of each parameter, so as to realize the amplitude scale alignment between parameters of different dimensions.

[0076] After normalization processing, disturbance removal processing is performed on the normalized result. The disturbance identification operation mainly aims at the following two types of disturbances: one is the airflow shock disturbance caused by local object blocking, fan start-stop, etc., and the other is the sampling mutation disturbance caused by electromagnetic interference or signal mutation. In order to realize disturbance identification and removal, the system adopts a sliding window method combined with an adaptive trend evaluation algorithm for processing. The sliding window is set to evaluate the wind speed, temperature and pressure change rate in a continuous time period, and if the change rate exceeds three times the historical standard deviation, it is marked as a disturbance section, and further through spatial collaborative analysis to confirm whether the disturbance affects the systematic stability judgment.

[0077] After disturbance removal, the spatial average field and the principal component vector are calculated for the remaining stable section data to form a stable airflow parameter set. The stable airflow parameter set includes the average wind speed principal vector field, the average temperature distribution scalar field, and the average pressure equipotential surface configuration, which are used as the boundary modeling input conditions required by S130.

[0078] The stable airflow parameter set is not only the basis for airflow state boundary model construction, but also the basis fluid input field data structure relied on by the CFD model startup in the subsequent S200 step, and its steady-state characteristics directly affect the accuracy and convergence of dust particle path estimation.

[0079] S130, based on the stable airflow parameter set, constructs an airflow state boundary model to complete the airflow state initialization processing.

[0080] Specifically, the system first calls the wind speed principal vector field data and the temperature and pressure spatial distribution structure in the stable airflow parameter set and loads them into the airflow state boundary modeling unit. The modeling unit describes the wind tunnel geometric layout based on a graph structure topology, divides the wind tunnel physical domain through three-dimensional mesh division, sets boundary condition regions, internal control body regions, and key node positions. The airflow direction principal vector field is mapped to the boundary region input face to form the initial inlet boundary condition; the temperature field and the pressure field are mapped to the boundary nodes and the internal regions respectively to form the heat flow boundary constraint and the density field difference structure.

[0081] The first-order stable solution field operation is performed on the above boundary conditions by using a modeling engine to solve the flow field structure of the air duct under the initial passive disturbance condition, and a gas flow state boundary model is constructed. The model saves the following structural information: the mapping relationship between the three-dimensional structure of the air duct and the control body grid, the gas flow vector assignment structure of the boundary input surface, the temperature pressure coupling mapping table, and the air pressure gradient direction atlas related to the heat load distribution.

[0082] After the construction of the gas flow state boundary model is completed, the model data will be used as the initial boundary input structure of the dust particle path estimation algorithm in the subsequent step S210. In particular, the particle motion trajectory estimation in step S210 is based on the velocity field main direction and the isobaric surface flow state information provided by the boundary model, and further performs Euler-Lagrange path tracking operation. Therefore, the gas flow state boundary model output by S130 not only serves as the boundary driving condition for subsequent path prediction, but also uses the stable parameters it covers for deposition probability density mapping calculation in S230, ensuring the consistency and closed-loop stability of the overall reasoning link.

[0083] Through the continuous execution process of S110 to S130, the system realizes the dynamic collection, structured arrangement and stability modeling of the three parameters of wind speed, temperature and pressure in the air duct environment, providing a high timeliness and high consistency physical basic environment model for subsequent CFD path prediction and electrostatic flow regulation. Especially in this invention, the gas flow state boundary model output by S130 is directly used in step S200, ensuring that the dust particle motion path estimation has the accuracy constraint based on the real stable field, and enhancing the overall prediction reliability of the system and the closed-loop consistency of the flow regulation logic.

[0084] Step S200 includes at least steps S210-S230:

[0085] S210, obtain the gas flow state boundary model, and execute dust particle motion path estimation combined with particle size range setting parameters.

[0086] Specifically, after the construction of the gas flow state boundary model is completed in step S130, this step first calls the stable gas flow parameter set output in the model, which includes the average velocity vector field vorticity tensor field and pressure distribution field The set covers the three-dimensional space area of the air duct in the form of a grid

[0087] The system combines the user-set dust particle size range [D min ,D max ] to perform particle trajectory estimation simulation for each typical particle size (where k=1, 2,..., K, a total of K discrete particle sizes are sampled).

[0088] The particle path estimation is based on a Lagrangian particle tracking model, and a particle motion differential expression is constructed as follows:

[0089]

[0090] Wherein:

[0091] represents the spatial position vector of the dust particle numbered f at the i-th step at time t;

[0092] represents the velocity vector of the dust particle at the same time step;

[0093] t: time variable;

[0094] i: index of discrete time step;

[0095] f: particle number (used to distinguish different simulation particles).

[0096] Formula ① describes the position evolution process of the dust particle in three-dimensional space over time, which constitutes the motion backbone of the Lagrangian particle tracking model. The system performs path integral simulation on each particle based on this formula to generate its motion trajectory. High-precision position prediction of individual particles is realized, providing continuous path basis for subsequent collision detection and deposition area judgment.

[0097] Further, the update of the dust particle velocity is based on Newton's second law, and a mechanical model of unit mass particle is constructed as follows:

[0098]

[0099] Wherein:

[0100] acceleration of the dust particle at the current time step;

[0101] is the resistance acceleration term of the dust particle at the i-th step;

[0102] F G : constant gravity acceleration term.

[0103] Formula ② establishes a dynamic model of unit mass particle based on Newton's second law, simulates the evolution process of dust particle velocity by combining the effects of resistance and gravity. Ensure that the particle motion simulation can respond to changes in fluid dynamics and maintain the real influence of gravity, and improve the simulation physical consistency.

[0104] Further, the resistance term is constructed by using the Stokes resistance model correction expression as follows:

[0105]

[0106] wherein:

[0107] μ: air viscosity coefficient;

[0108] ρ f : density of dust particle;

[0109] is the current particle size sampling value;

[0110] is the air flow velocity vector at the current position of the dust particle, which is obtained by interpolation of the velocity field generated in S130.

[0111] Formula (3) is used to depict the viscous resistance between particles and air flow under low Reynolds number conditions, which reflects that the larger the particle size and the greater the flow rate difference, the stronger the resistance. The dynamic influence of resistance on particles of different sizes is modeled, providing a key adjustment factor for path prediction and improving the physical accuracy of small particle behavior simulation.

[0112] Further, to evaluate the interaction strength between particles and flow field, the Reynolds number is defined as:

[0113]

[0114] wherein:

[0115] Re (i) : Reynolds number of the i-th step particle;

[0116] ρ a : air density;

[0117] Formula (4) is used to quantify whether the kinetic behavior of the dust particle relative to the air flow belongs to the viscous dominant or inertial dominant interval. When Re (i) >1000, it means that the particle motion is more affected by inertia, and the subsequent path may deviate from the mainstream area, which needs to be strengthened in S220. The criterion support is provided for path deviation and deflection prediction, supporting the strengthening processing of high inertia particle path in S220, thereby improving the accuracy of deposition recognition.

[0118] The above formulas (1-4) constitute a difference iteration system for dust particle path estimation, which performs time domain discrete integration on each initial position point and particle size value to form a path vector sequence for next step.

[0119] S220, collision detection and stagnation trajectory calibration are performed on the dust particle motion path to generate a candidate deposition path.

[0120] This step receives the dust particle path sequence output by S210, and establishes a spatial collision detection mechanism for the inner wall of the air duct, the component boundary and the surface of the heat dissipation fin. Specifically, the air duct structure set is defined as represents L boundary surfaces or component surfaces.

[0121] For each path whether its minimum distance to any boundary surface is below a set threshold c , i.e.

[0122]

[0123] where

[0124] The dust particle numbered f on the i-th path at time t j corresponding position vector;

[0125] S l : set of duct structures the l-th boundary surface in the set;

[0126] L: total number of boundary surfaces;

[0127] ||·||: represents the Euclidean distance operation;

[0128] ∈ c : is the minimum contact distance threshold for collision detection;

[0129] represents the minimum distance to all boundary surfaces;

[0130] This expression is used to identify whether the particle trajectory is in physical contact or close proximity (less than c ) to any boundary surface. Once the condition is met, the system records it as a collision event and marks the point segment as a collision candidate position. By introducing a continuous spatial distance judgment mechanism, high-precision particle path and boundary interaction recognition is achieved, significantly improving the spatial resolution of deposition hotspots.

[0131] Further, the system records all point segments that have contact or velocity slowing down below a threshold (such as ) to form a stagnation area. Among them, represents the velocity module of the dust particle at time step t j ; ∈ v is the set velocity threshold for determining whether the particle is moving slowly to be considered as stagnation.

[0132] For each path j, the system defines its deposition probability as follows:

[0133]

[0134] where

[0135] deposition probability prediction value of the j-th path

[0136] represents the proportion of stagnation segments in the jth path, calculated as the number of stagnation points divided by the total number of trajectory points; represents the number of points in the path that are determined to be stagnant; represents the total number of time steps of the path;

[0137] represents the collision frequency density, which is the ratio of the frequency of collisions in the path to the total length of the path; represents the number of collisions in the path; represents the path length, which can be obtained by accumulating the point set;

[0138] α1, α2: are weighting coefficients, by default α1=0.6 and α2=0.4, which can be adjusted according to empirical data. Formula ⑤ is the key innovative expression of this module, which is used to extract the "candidate deposition path" set in combination with the path dynamic characteristics where N c represents the total number of candidate paths; represents the candidate deposition path set; X j represents the jth dust particle motion path.

[0139] Formula ⑤ linearly combines two core dynamic indicators: the stagnation proportion δ (j) and the collision frequency density θ (j) , comprehensively reflecting the deposition tendency degree of the dust particle path, which is the probability measurement core of the deposition hotspot identification process. It realizes the numerical mapping of dust particle dynamics behavior to deposition trend, breaks through the limitations of traditional methods relying on static threshold or empirical rules to judge deposition, has high adjustability and interpretability, and provides a precise spatial basis for subsequent electrode regulation region generation.

[0140] S230, calculate the deposition probability distribution structure in the wind channel region based on the candidate deposition path.

[0141] This step is based on the above candidate path set to construct a three-dimensional space deposition probability distribution function to estimate the deposition tendency of each cell in the wind channel volume grid in a statistical region manner.

[0142] Specifically, let the spatial discrete grid be The deposition probability in each volume V m is defined as follows:

[0143]

[0144] where:

[0145] represents the spatial volume V mProbability of dust particle deposition inside;

[0146] is indicative of whether the path crosses the volume element or not; is indicative of whether the path X j crosses or passes through the volume element V m , the function takes the value 1, otherwise the value 0. It is used to filter paths crossing the current volume element;

[0147] Z: Normalization factor, equal to the sum of all path probabilities.

[0148] The final output is a deposition probability volume data structure for the input of the subsequent module S300 electrostatic regulation area extraction.

[0149] Through the whole process encapsulation of the above-mentioned S200 module, the system can realize particle size layering simulation, path estimation, behavior discrimination and spatial probability mapping based on stable airflow parameters. In particular:

[0150] Equations ①-④ construct the motion path estimation differential system;

[0151] Equation ⑤ realizes the comprehensive score of path behavior;

[0152] Equation ⑥ realizes high-dimensional probability mapping closed loop.

[0153] The technical effect of this module is to provide full-path-level particle deposition prediction capability based on CFD and multi-factor fusion algorithm, which constitutes the core reasoning basis of the entire "reinforced computer dust particle prediction and adaptive dust prevention method and system", and provides accurate, high-dimensional and real-time data basis support for subsequent electrostatic regulation strategy formulation.

[0154] Step S300 at least includes steps S310-S330:

[0155] S310, acquire the deposition probability distribution structure, extract the high deposition risk area of deposition and construct the spatial control area structure.

[0156] The system first acquires the deposition probability distribution structure output by the previous step S230. The deposition probability distribution structure is a three-dimensional distribution result calculated based on the candidate deposition path, which describes the normalized probability density value of dust particle deposition at different positions in the spatial coordinate domain. Specifically, the system calls the three-dimensional deposition probability structure atlas output by the S230 module, scans and calculates each voxel unit in the atlas, identifies the area unit higher than the deposition risk threshold as the candidate hot spot area of dust particle concentrated deposition.

[0157] Understandably, the deposition risk threshold is a static setting parameter, which is set according to historical operation data and equipment safety tolerance standard, and is usually a percentile coefficient corresponding to the probability density distribution function. When the deposition probability of a certain space unit exceeds the set threshold, the system determines that it is a high deposition risk point. In order to enhance the continuity of space recognition, the system clusters and labels adjacent high deposition probability units based on the region growing algorithm to form a structured deposition risk aggregation area.

[0158] Further, the system takes each deposition risk aggregation area as the core boundary to construct an envelope control body. The envelope control body is formed by bounding box fitting, boundary inflation and connection channel expansion, etc. to form a spatial control area structure for electrostatic conduction regulation. The spatial control area as the direct input data of subsequent electrode planning constitutes the geometric boundary basis of the conduction area.

[0159] In the process of constructing the spatial control area structure, the system needs to call the airflow state boundary model generated in S130 to filter the coincidence degree between the control area and the stable airflow boundary, and only keep the control body in the main airflow channel, so as to eliminate the non-core space unit which has limited influence on dust particle movement, and ensure the effectiveness and coverage efficiency of electrode resource allocation.

[0160] S320, the space control area structure is segmented by electric field boundary and the target deflection direction is planned to generate an electrode regulation area.

[0161] After completing the construction of the space control area structure, the system enters the electric field area generation stage. Specifically, the system first performs a space segmentation operation on each space control area structure, and divides it into several sub-electric field control units according to the distribution position of its boundary in the airflow vector field. The division rule of the sub-electric field control unit is mainly based on two parameters: one is the angle relationship between the airflow direction vector and the normal of the control area boundary, and the other is the distribution density of the deposition probability gradient field inside the control area.

[0162] For the boundary section with an angle close to vertical, the system prefers to use a high gradient electric field for forced deflection control; for the boundary section with a small angle, the system uses a weak electric field fine-tuning control to reduce the disturbance to the main air duct flow rate.

[0163] After the electric field boundary segmentation is completed, the system performs target deflection direction planning processing based on the particle size range setting parameter output in S210. In this process, the system needs to combine the particle size range setting parameter and the airflow state boundary model to calculate the best dust particle deflection direction in each electric field control unit. The direction is defined as: the direction vector that makes the dust particle motion path avoid the high-risk deposition area as much as possible and deviate to the dust collection module layout area.

[0164] To improve the control accuracy, the system also needs to introduce the stagnation trajectory calibration results output in step S220 during the target offset direction planning process. By fitting the aggregation direction of the stagnation trajectory in reverse, the system further determines the position points where dust particles are prone to deposit and their response sensitivity to the electric field direction. The above information is integrated into the target offset direction generation function to form a multi-electrode regulation direction adapted to different particle sizes.

[0165] The system fuses the target offset direction, boundary attributes, and airflow field information in each control unit to generate an electrode regulation region. This electrode regulation region serves as the geometric reference basis for the generation of the electrostatic flow guide structure, and its data structure includes core fields such as three-dimensional position index, electric field scope boundary, target offset direction vector, and adapted particle size parameter range, for subsequent module reading and use.

[0166] S330, generating an electrostatic flow guide field regulation structure based on the electrode regulation region, and calculating the control parameter matrix of the flow guide electrode.

[0167] Based on the electrode regulation region generated by step S320, the system enters the electrostatic flow guide field regulation structure construction and control parameter calculation phase.

[0168] Specifically, the system first converts the electrode regulation region into a spatial discrete grid model and constructs an electrostatic flow sub-structure in each control unit. The sub-structure includes: electrode unit geometric layout, voltage action area allocation, polarity direction constraint, and edge spacing tolerance information. The system initializes the electric field direction in each electrostatic sub-structure according to the offset direction vector defined in the electrode regulation region.

[0169] Further, the system combines the deposition probability distribution structure generated in S230 to perform voltage gradient optimization distribution processing in each electrode control region. This processing iteratively optimizes the electrode voltage value so that the final electric field force vector field can effectively act on the dust particles, achieving real-time deflection of their movement path. The system uses a multi-dimensional parameter optimization model based on gradient descent to iteratively update the objective function. The objective function consists of the following parts: 1) the minimum error of the dust particle deviating from the deposition hotspot path, 2) the disturbance effect of the overall electric field intensity on the airflow stability, and 3) the compactness constraint of the electrode layout.

[0170] The output of the above optimization processing is the control parameter matrix of the flow guide electrode. This matrix structure contains the spatial coordinates, voltage intensity value, action polarity, and time sequence number of each electrode node. To improve the response efficiency and reliability of the control system, the system further converts the voltage scheduling value in the control parameter matrix into a drive signal format for direct use by the electrode command generation and module driving link in the subsequent steps.

[0171] During the whole process of S330, the system needs to read the airflow state boundary model output by S130 at the same time, to ensure that the electric field formed by the flow guiding electrode control does not produce critical disturbance to the main air duct flux, so as to maintain the stability of the overall heat dissipation performance of the system.

[0172] Through the continuous implementation of the above-mentioned three steps of S310 to S330, the present application realizes a closed-loop path from deposition risk perception, spatial electrode planning to electrostatic flow field generation. Specifically:

[0173] S310 realizes spatial recognition and structure calibration of high-risk deposition areas based on the deposition probability distribution structure;

[0174] S320 constructs an adaptive electrode regulation area through airflow field constraint and target direction reasoning;

[0175] S330 generates a fine and programmable electrostatic flow field control matrix on this basis, to ensure that dust particles are effectively deflected before entering the air duct, guiding them to leave the deposition hot spot area.

[0176] The above processing result will be directly used as the input basis for the electrode execution process of S400 module, to ensure the closed loop from perception to regulation, forming a dynamic adaptability structure of "prediction-planning-control".

[0177] Step S400 at least includes steps S410-S430:

[0178] S410, obtain the control parameter matrix of the flow guiding electrode, and perform driving signal conversion and flow guiding electrode instruction generation processing.

[0179] After completing the generation of the control parameter matrix of the flow guiding electrode in step S330, the system enters the conversion stage of the control parameter to the execution instruction. Specifically, the system first calls the control parameter matrix in the electrostatic flow field regulation structure generated in step S330. The control parameter matrix is a set of control structure sets describing the spatial configuration, voltage intensity and polarity direction of the electrode unit. The matrix data structure is in the form of a multi-dimensional discrete array in this embodiment, and has the characteristics of time synchronization and space grid matching.

[0180] Further, the system calls the electrode parameter mapping module to convert each electrode spatial index and deflection intensity value in the control parameter matrix into low-order voltage coding compatible with the driving circuit. The mapping module has a voltage quantization conversion interface, and its processing process includes:

[0181] I. Analyze the electrode index identification field in the control parameter matrix to extract its spatial coordinate attribute and electric field boundary correspondence;

[0182] II. Discretize the continuous control parameters into digital driving signals according to the modulation weight of each electrode unit and the target offset direction setting value, and encapsulate them in a programmable grid register structure format;

[0183] III. Time sequence encode the electrode driving signal sequence in combination with the scheduling time slice structure of the system to generate an electrode instruction set with time effectiveness identification.

[0184] The electrode instruction set is encapsulated in a byte stream structure and contains information fields such as the driving address of each electrode unit, deflection direction instruction, voltage value level, and control duration period. The system performs redundancy check and electrical tolerance verification on the instruction set structure in this step to ensure that it can be stably parsed and executed by the electrode control module in the subsequent steps.

[0185] The electrode instruction generation process also includes an error tolerance mechanism. When the system finds that there is a cross-border electrode overlap or polarity conflict field during the electrode control parameter matrix parsing process, the system will automatically execute the vector field rollback mechanism, calling the verified results of the last control parameter buffer as a temporary replacement to ensure the integrity of the driving logic.

[0186] S420, send the electrode control instruction to the electrode control module and execute the programmable electrode control operation.

[0187] After the encapsulation and generation of the electrode instruction set are completed, the system will enter the electrode instruction issuing and electrode control module driving operation phase. Specifically, the system calls the central control bus interface to establish a low-delay communication link with the electrode execution module. The communication link is implemented based on the CAN bus protocol or SPI high-speed bus protocol, and its structure has clock synchronization and multi-channel fault tolerance characteristics, ensuring the stability and accuracy of the instruction transmission.

[0188] In the transmission preparation stage, the system performs multi-channel announcement and scheduling tag setting on the electrode instruction set to ensure that the corresponding electrode execution unit in the electrode control module can accept the control signal according to the preset time sequence. Subsequently, the system sends each control byte stream containing the position index, deflection voltage, and polarity direction to the input buffer of the electrode control module in sequence.

[0189] The electrode control module is internally provided with a parsing component for receiving electrode control instructions, and its functions include:

[0190] I. Receive the control byte stream structure, perform address mapping and CRC check to ensure the integrity of the instruction;

[0191] II. Queue cache and execution cycle scheduling of the instruction based on the time sequence tag rearrangement module to realize clock synchronization control;

[0192] 3. Based on the analytical results, the unit modules in the guide electrode array are driven to perform dynamic deflection control operations. Each electrode unit can set the voltage amplitude, electric field direction and control duration in a programmable manner.

[0193] In this embodiment, the programmable current-guiding electrode unit is constructed based on a MEMS electrode array structure, and has the characteristics of small size, high response speed and low energy consumption. It can respond to the electric field change settings in the control instructions in real time and stably maintain the deflection action.

[0194] Furthermore, the flow control module includes a state feedback path that monitors the actual electrode deflection voltage, current response, and electric field formation, and transmits the detected values ​​to the central control module for subsequent feedback vector generation and adaptive parameter update processing.

[0195] To prevent command failures in environments with complex airflow disturbances or high dust concentrations, the system incorporates redundant electrode zones and a skip-step control strategy. If a critical diversion electrode fails or responds late, the system automatically invokes redundant electrode units to intervene, adjusting the electric field topology and migrating the deflection path, ensuring uninterrupted dust particle deflection.

[0196] S430: Execute a dust particle deflection action according to the programmable guide electrode control operation to generate dust particle deflection path data.

[0197] After the programmable flow-guiding electrode successfully receives the control command from the flow-guiding control module and completes the electric field reconstruction, the electrostatic flow-guiding field within the duct space forms an adjustable non-uniform electric potential distribution. This electric potential distribution structure aligns with the target offset direction preset by the electrostatic flow-guiding field control structure in step S330, enabling spatial deflection of the particle swarm carried by the incoming airflow.

[0198] Specifically, the guide electrode array establishes a gradient-guided electric field structure within the air duct based on the spatial arrangement and voltage excitation values ​​set by the control parameter matrix. This structure, based on the boundaries of high-risk areas of deposition probability, forms a stable particle deflection field toward the dust collection module away from sensitive areas. This particle deflection field selectively deflects dust particles of varying sizes, charge states, and inertial parameters by applying voltage differences of varying strengths and polarities at different locations.

[0199] After dust particles enter the diversion field, their trajectory deviates from the original airflow path due to the combined effects of electrostatic and aerodynamic forces. The system uses a multi-channel capacitive particle trajectory sensing array, located on the duct wall or behind the diversion electrodes, to record these changes in particle trajectory in real time. This sensing array utilizes sub-millisecond response devices, enabling stable sensing of particle paths under complex airflow disturbances and archiving the extent of these deflections.

[0200] To ensure the reliability of particle deflection path data, the system performs the following steps on the initial deflection data output by the sensing array:

[0201] I. Denoising and segment registration of particle response values at each collection point to eliminate false responses caused by background airflow disturbances;

[0202] II. Combining known wind speed vector field and dust particle inertia characteristic model to calculate the instantaneous velocity vector and deflection acceleration of the particle motion;

[0203] III. According to the motion trajectory of the particle from the flow guide inlet to the dust collection target area, construct the deflection trajectory data set in three-dimensional space.

[0204] The system encapsulates the above particle trajectory data into a dust particle deflection path data structure, which includes but is not limited to: particle number, particle size level, initial position coordinates, final deflection position, average deflection vector per unit time, and whether to enter the preset dust collection area flag bit and other field information.

[0205] Further, the dust particle deflection path data will be used as the basis for subsequent S500 to perform spatial aggregation and particle adsorption processing, providing spatial layout support for the configuration and scheduling of the dust collection area. At the same time, the number and position of particles that have not been successfully deflected in the deflection path will also be used to generate feedback control vectors in the S600 stage, providing closed-loop basis for dynamic adjustment of the control parameter matrix.

[0206] The system also designs a dust particle deflection failure alarm mechanism in this step. When the particle does not achieve effective deflection in the flow guide area, or the deflection direction deviates too much from the target dust collection area, the system will record the abnormal particle event and generate a field identifier that maps the control parameter failure, for the adaptive module to perform parameter correction and flow guide electric field structure re-adjustment.

[0207] By implementing steps S410 to S430, the system completes the complete mapping of the flow guide electrode control parameter to the physical deflection action, constructs a closed-loop structure from the electrostatic field control instruction generation, instruction execution driving to the deflection path perception, ensures that the dust particles are effectively deflected before they occur in high-risk deposition areas, and realizes a high-time-efficiency, dynamic adaptive electrostatic flow control mechanism. The dust particle deflection path data output by this module not only provides accurate input for subsequent dust collection strategies, but also is a key basis for building a control parameter adaptive update mechanism, ensuring that the entire system has long-term stable dust prevention effect and self-optimization ability.

[0208] Step S500 includes at least steps S510-S530:

[0209] S510, obtain dust particle deflection path data, configure dust collection target area and schedule dust collection module.

[0210] Specifically, this step first acquires the dust particle deflection path data generated by the previous step S430. The dust particle deflection path data records the actual motion trajectory of the dust particle from the flow guiding starting surface to the air duct terminal under the programmable flow guiding electrode control operation, including the sequence of dust particle position points, velocity vectors, time stamp markers, and flow guiding offset coordinate system information. This deflection path data is a key input parameter for dust collection area configuration and dust collection module scheduling, and is organized in the form of a multi-dimensional tensor at the data structure level and transmitted to the data preprocessing unit of this module.

[0211] The data preprocessing unit performs path clustering operations and density evaluation analysis on the dust particle deflection path data. By analyzing the spatial aggregation degree of multiple deflection paths, a spatial aggregation region with high dust particle trajectory overlap degree can be extracted, and this region is then marked as a primary dust collection candidate region. Further, combined with the deposition probability distribution structure generated in step S230, the system further compares the intersection degree between the deflected path trajectory and the high deposition risk region, and screens and demarcates the final dust collection target region boundary.

[0212] To improve the dust collection accuracy and module scheduling efficiency, the system performs geometric projection mapping on the dust collection target region based on the electric field flow guiding terminal point distribution map and the aerodynamic distribution model, forming a matching dust collection region structure that is adapted to the air inlet and dust filter medium arrangement structure of the dust collection module. This structure describes the boundary information and particle guiding entrance position of the dust collection space in a matrix three-dimensional grid, and forms input parameters for subsequent dust collection module calls.

[0213] The system calls the dust collection control module interface and assigns initialization control instructions to the dust collection equipment through the interface scheduling strategy mapping function. The control instructions include but are not limited to: adsorption medium activation instructions, air pressure adjustment instructions, electrostatic adsorption plate opening instructions, and particle guiding valve synchronization instructions. All instructions are generated in real time based on the spatial layout of the dust collection target region and the air field stability model, and the execution tasks of module scheduling are completed through the dust collection module driving channel.

[0214] S520, perform spatial aggregation and particle adsorption operations of the dust collection module, and generate dust collection control result data.

[0215] Further, after completing the dust collection module scheduling operation, this step begins to perform the spatial aggregation and particle adsorption process of the dust collection module. The dust collection module at least includes an electrostatic adsorption plate structure, a programmable air flow guiding vane, a particle guiding channel, a dust filter material module, and a residual dust collection cavity. The above components operate cooperatively to complete the spatial aggregation, trajectory convergence, and particle capture processing of the dust particles in the target region.

[0216] Specifically, the electrostatic adsorption plate structure establishes a dynamic electric field by controlling the voltage field injection instruction in the control module. The direction of the electric field is adjusted synchronously according to the control parameter matrix of the flow guide electrode in S330, so that the particles in the dust collection area are guided to the dust filtering surface under the action of the electric field. The dust filtering material module selects high-efficiency and low-resistance electrostatic fiber medium, which can implement non-mechanical blocking adsorption processing on multiple types of dust particles with particle sizes ranging from sub-micron to dozens of microns, thereby balancing the maintenance of high-precision adsorption and air flow flux.

[0217] To avoid secondary diffusion or unintended trajectory deviation of the particles under air disturbance, the system further dispatches the programmable air flow guide vane module to establish a reverse air flow pressure difference layer at the entrance area of the dust collection channel. The air flow structure is automatically generated and updated by the system according to the dust particle deflection path data, realizes the aerodynamic capture effect in the dust particle aggregation process, and improves the particle adsorption efficiency.

[0218] During the dust collection process, each sub-module synchronously records the operating parameters, including the adsorption strength change, particle density change, electric field response amplitude, and dust filter flux change, etc. The system encapsulates the above data to form dust collection control result data, which is output in a structured data format for subsequent residual identification. The dust collection control result data at least includes: dust collection target area identification number, control time period identifier, dust filtering unit capture efficiency, adsorption plate residual charge capacity, aggregation air flow stability index, and abnormal adsorption event count.

[0219] S530, residual particle identification and counting processing is performed on the dust collection control result data to generate residual dust particle monitoring data.

[0220] After the dust collection operation is completed and the dust collection control result data is obtained, the system enters the residual particle identification and counting processing stage. The core of this stage is to identify the residual particles that are not adsorbed after dust collection, monitor the actual working efficiency of the dust collection system, and provide a quantitative basis for subsequent feedback control.

[0221] Specifically, the system first calls the high-resolution imaging module of the dust collection area, which is located on the inner wall of the dust collection structure or the side wall of the downwind duct, and immediately collects the target area image sequence after the dust collection task is completed. The image sequence is subjected to background modeling and difference extraction operations by the image preprocessing module to form an intermediate image structure containing dust particle outline information.

[0222] The system calls the particle identification neural network model to perform multi-scale particle identification operations on the above-mentioned intermediate image structure to calibrate the position, size, reflectivity, and morphology parameters of various un-adsorbed particles. The identification model constructs a residual feature template based on a training set and eliminates misidentified items in combination with spatial position distribution constraints. The confirmed residual particle information is stored in the particle residual data set for subsequent counting and statistical analysis.

[0223] The system performs quantitative analysis processing based on the identified residual particle set data, calculates the dust particle residual density per unit area per unit time through the particle category density function and the area activity rate model, and compares it with historical data or a preset threshold to complete the residual over-standard judgment. All statistical parameters and analysis results are organized as structured residual dust particle monitoring data for the S600 module to execute feedback control parameter update processing.

[0224] Through the continuous processing flow of S510 to S530 in this step, the application realizes the dynamic mapping of the dust particle deflection path data to the physical dust collection structure action. Therefore, not only is the link closed loop between dust particle motion path prediction and physical adsorption response established, but also the spatial partition control of the dust collection area and the modular decoupling of particle adsorption execution are realized. Finally, the formation of residual dust particle monitoring data provides reliable data input basis for subsequent control parameter update, ensuring that the adaptive control strategy has a quantifiable feedback path, forming a "prediction-control-collection-evaluation-feedback" closed loop control structure.

[0225] Step S600 includes at least steps S610-S630:

[0226] S610, obtain residual dust particle monitoring data, combine the deposition probability distribution structure and the control parameter matrix to generate a feedback control vector.

[0227] Specifically, the system first calls the residual dust particle monitoring data generated in step S530, which is the structured particle residual index set output by the dust collection control module after performing particle adsorption and identification. This set is obtained by statistical processing of the number of dust particles in the dust collection area that have not been adsorbed, particle size distribution, spatial distribution density, and residual hot spot area density. The residual particle identification method includes the weighted fusion results of particle image reflection analysis, capacitance anomaly recognition, and dust collection surface adsorption redundancy verification.

[0228] Further, the system calls the deposition probability distribution structure constructed in step S230. This distribution structure is constructed through dust particle motion path simulation, candidate deposition path generation, and multi-region probability estimation function calculation, and has spatial coordinate resolution and probability density dual identification capabilities, and covers the entire heat dissipation air duct area. Combined with the above two structure data, the system compares the predicted deposition probability value of each deposition high-risk area with the actual residual dust particle monitoring value, performs difference mapping operation and time sequence alignment, and forms a structured residual parameter set.

[0229] The system further introduces the control parameter matrix output in step S330, which records the voltage parameters of each electrode in the electrostatic flow field regulation structure, the electrode activation timing, the deflection angle control factor, and the polarity adjustment strategy parameters. To construct the feedback vector, the system applies a high-dimensional parameter compression and encoding function based on the nonlinear mapping relationship between the control parameter matrix and the residual parameter set to generate the feedback control vector. The feedback control vector is a set of multi-dimensional control deviation indicators, which is used to describe the deviation between the current flow control strategy and the real dust behavior, and supports the subsequent element-by-element adjustment of the control parameters.

[0230] The generation process of the feedback control vector is executed in the adaptive update module 60 in the system of the present application. After calling the aforementioned residual dust monitoring data, deposition probability distribution structure, and control parameter matrix, and completing the structure standardization and dimension normalization processing, the encoding, alignment, and combination operations are performed to form a multi-dimensional feedback control vector, which is used by the subsequent update module.

[0231] S620, updating the control parameters in the electrostatic flow field regulation structure according to the feedback control vector.

[0232] In this step, after receiving the feedback control vector, the system automatically calls the electrostatic flow field regulation structure constructed in step S330 and locks the internal control parameter submodule to start the control parameter update process.

[0233] Specifically, the electrostatic flow field regulation structure includes multiple flow electrode nodes, boundary deflection units, and electric field gradient balancing units. The control parameter submodule includes five key control attributes: the spatial arrangement order of the flow electrode, the driving voltage amplitude, the polarity configuration direction, the response time delay, and the regulation electric field stability coefficient. During the control parameter update process, the system performs parameter-by-parameter correction operation according to the residual direction, adjustment amplitude, and control deviation factor carried in the feedback control vector.

[0234] During the correction, the system uses a distributed adaptive filtering algorithm to perform step-by-step gradient update on each control parameter. The system sequentially calls the previous period control parameter value, the corresponding element of the current feedback control vector, the target deflection direction of the flow field, and the regional priority strategy factor to form a parameter offset calculator, and maps it to the corresponding position of each module in the electrostatic flow field regulation structure to complete the parameter update.

[0235] Further, in order to ensure the stability and continuity of the control parameter update, the system performs three stability check operations after each round of update: first, perform physical consistency check on the updated control parameter matrix to ensure that the voltage value, polarity direction and response time delay do not violate the electric field safety domain; second, perform electrode cascade response check to verify that the deflection operation will not cause regional electric field interference conflict; third, perform heat conduction influence constraint evaluation to avoid local heat conduction bottleneck caused by electrode deflection control in high heat area.

[0236] After completing the above multiple checks, the system formally injects the updated control parameters into the regulation structure, completing the refresh operation of the current control parameter matrix.

[0237] S630, the control parameter update result is re-injected into the flow guiding electrode instruction generation process to complete the adaptive parameter adjustment process.

[0238] After completing the update of the control parameter matrix, the system enters the instruction update process reconstruction phase. First, the adaptive update module reads the updated control parameter matrix and maps it to the flow guiding electrode instruction generation process defined in step S410.

[0239] Specifically, the system sends the voltage control vector, polarity configuration vector, response time sequence vector and spatial positioning matrix in the new control parameter matrix into the instruction conversion module one by one. The module performs the electrode instruction construction process according to the pre-set driving signal model and instruction coding specification. The process includes four stages: signal standardization processing, electrode address mapping, regulation mode matching and output sequence synthesis, finally generating a new flow guiding electrode instruction set.

[0240] The system sends the flow guiding electrode instruction set to the flow guiding control module and connects with the programmable flow guiding electrode control operation in step S420. To ensure the atomicity and consistency of the instruction update, the system sets up a double-channel instruction cache mechanism, that is, the new instruction loading is completed in parallel during the old instruction execution period, and the content of the flow guiding electrode instruction cache area is replaced synchronously with a switch trigger identifier before the current control period ends.

[0241] It is worth noting that the deflection path generation logic in the flow guiding electrode instruction generation process also needs to be updated synchronously at this stage. The system combines the instruction set with the current deposition probability distribution structure to perform path mapping backtracking operation to ensure that the new control strategy can effectively cover all high-risk deposition areas and realize real-time synchronous regulation of dust particle deflection path data.

[0242] At this point, the adaptive parameter adjustment process is completed, and the system realizes closed-loop correction of the flow guiding control logic.

[0243] The coordinated execution of steps S610, S620, and S630 above enables dynamic updating and real-time writeback of dust deflection control parameters, ensuring that the system continuously optimizes the diversion control strategy based on the deviation trend between the current residual dust behavior and the historical deflection control strategy. Compared to traditional fixed control parameter models, the adaptive parameter adjustment mechanism described in this embodiment enhances closed-loop control performance without external intervention, significantly improving the accuracy and reliability of dust deflection, and ensuring that the system maintains an optimal heat dissipation and dust prevention balance during long-term operation.

[0244] The key innovations of the present invention include:

[0245] (1) CFD-based particle size stratification motion path modeling and Reynolds number coupled trajectory deduction mechanism. This achieves a dynamic correlation between particle velocity, position, resistance, and airflow velocity, and introduces a closed-loop modeling of "path-level differential estimation and dynamic scoring" for the first time in air-cooled system dust prevention, with high precision and real-time performance.

[0246] (2) Construct a deposition probability function and a three-dimensional mapping structure for the candidate spatial paths. An explicit regional statistical mechanism is introduced to form a voxel-level probability heat map, overcoming the lack of predictive capability in traditional dust control systems and providing a precise positioning basis for deflection strategies.

[0247] (3) Automatic generation of electrode control area and real-time calculation method of electric field deflection direction. The system generates the spatial electrode control area and electrostatic diversion parameter matrix through S310–S330 to achieve precise guidance of the electric field before air intake, effectively avoiding the "deflection dislocation" problem.

[0248] (4) Construction and execution mechanism of multi-level feedback adaptive control path. The system introduces a mechanism to compare residual dust particle monitoring results with control parameters. It can reversely adjust the deflection intensity and spatial control area coverage according to the residual dust particle distribution, thus achieving periodic self-regulation.

[0249] The following are its main beneficial effects:

[0250] (1) Significantly improve the spatial resolution and physical consistency of dust particle distribution prediction. The present invention constructs a stable airflow state boundary model in step S100 and introduces a Lagrangian path differential model based on Reynolds number and Stokes drag in steps S210–S230 to achieve precise estimation of the spatial paths of dust particles of different sizes. Furthermore, the collision detection and stagnation assessment mechanism is integrated with the spatial deposition probability mapping function to accurately identify dust particle deposition hotspots in the air duct. Compared with traditional particle distribution methods based on empirical rules or macroscopic estimation, the prediction error is lower.

[0251] (2) Realize dynamic self-adaptive adjustment of dust particle deflection control. The system constructs an electrode regulation region and generates an electrostatic flow control parameter matrix in S300-S330, and in combination with instruction conversion in S400 and a programmable flow guiding execution mechanism, the deflection direction and voltage intensity can be automatically adjusted according to the spatial distribution change of dust particles. Further, through the feedback path in S600, the residual dust particle monitoring result is fed back to the control matrix generation module, forming a closed-loop adaptive adjustment path, which effectively improves the continuous precision and real-time performance of the control strategy in complex environments.

[0252] (3) Reduce the dependence on fixed structure dustproof components and improve the heat dissipation efficiency. Traditional dustproof schemes usually rely on mechanical blocking structures such as fixed filters, which are prone to clogging and thus affect the system's heat dissipation performance. The present application introduces an adjustable electrostatic flow system to dynamically apply an electric field guide based on the dust particle path prediction result, realizing a “path-guided” dust particle deflection control. In areas with significant deposition trends, an electric field barrier is formed in advance to make dust particles deviate from the key heat-sensitive element area and gather into the detachable dust collection module, balancing airflow and dust particle isolation, effectively reducing the dependence on static dustproof structures, and improving the heat dissipation stability and protection response capability of the system.

[0253] (4) Form a universal and modular dustproof logic system. The present application divides the entire method into airflow modeling, particle path simulation, region regulation generation, control execution, deflection effect collection and feedback self-adjustment in a modular manner, forming a self-consistent data flow closed loop and structure mapping closed loop. This system can be reused in other air-cooled device scenarios, such as military servers, rail transit computing platforms, etc., with universal adaptability.

[0254] Embodiment Two: Figure 2 A structural diagram of a dust particle prediction and adaptive dustproof system for a ruggedized computer according to an embodiment of the present application is shown. As shown in Figure 2 , the structure can include:

[0255] An airflow parameter acquisition module 10 is used to obtain wind speed, temperature and pressure data from the heat dissipation air duct, perform structured analysis and format unification processing on the original data, and complete normalization and disturbance removal operations, outputting a stable airflow parameter set and an airflow state boundary model.

[0256] A dust particle path prediction module 20 is used to call the airflow state boundary model and combine particle size range setting parameters to perform dust particle motion path estimation; perform collision detection and stagnation trajectory calibration on the estimation result to form a candidate deposition path; and calculate a deposition probability distribution structure based on the candidate deposition path.

[0257] The electrostatic flow guide field generation module 30 is configured to read the deposition probability distribution structure, extract the deposition high-risk area, and construct a space control area structure; on this basis, complete the electric field boundary segmentation and target deflection direction planning, generate an electrode control area; construct an electrostatic flow guide field control structure according to the electrode control area and calculate the control parameter matrix of the flow guide electrode.

[0258] The flow guide electrode execution module 40 is configured to receive the control parameter matrix, convert it into flow guide electrode instructions, and send it to the flow guide control module; drive the programmable flow guide electrode to form a dynamic electric field, guide the dust particles to deflect, and output dust particle deflection path data.

[0259] The dust collection and residual monitoring module 50 is configured to configure a dust collection target area according to the dust particle deflection path data, schedule the dust collection module to perform space aggregation and particle adsorption operations, and generate dust collection control result data; then, identify and count residual particles from the dust collection control result data, and generate residual dust particle monitoring data.

[0260] The adaptive update module 60 is configured to obtain residual dust particle monitoring data, generate a feedback control vector in combination with the deposition probability distribution structure and the control parameter matrix; update the control parameters in the electrostatic flow guide field control structure according to the feedback control vector, and write the update result back to the flow guide electrode execution module to form a closed-loop regulation.

[0261] The system realizes dynamic prediction and active deflection control of dust particle deposition risk under the premise of maintaining the flux of the heat dissipation air duct, greatly improves the reliability and service life of the ruggedized computer in a high-dust environment through the closed-loop architecture of "real-time airflow parameter acquisition-dust particle path prediction-electrostatic flow guide field generation-flow guide electrode execution-dust collection and residual monitoring-adaptive update".

[0262] Obviously, the above-described embodiments are only some of the embodiments of the present application, not all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent replacements to some technical features. Any equivalent structure made by using the contents of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the present application.

Claims

1. A method for strengthening computer dust particle prediction and adaptive dust prevention, characterized in that: The following steps are involved: The raw data of wind speed, temperature and pressure are collected by a micro-multifunctional environmental sensor array, and data field merging and timestamp alignment operations are performed on the collected raw data to construct a stable airflow parameter set and generate an airflow state boundary model. The sensor array includes a thermal wind speed probe, a thermocouple temperature acquisition unit and a piezoresistive micro pressure sensor. The wind speed acquisition data is recorded in a three-dimensional vector format, the temperature data is stored in Celsius absolute value, and the pressure data is recorded in Pascal units. The airflow state boundary model includes a mapping relationship between the three-dimensional structure of the air duct and the control body grid points, an airflow vector assignment structure on the boundary input surface, a temperature-pressure coupling mapping table, and an air pressure gradient map. Based on the airflow state boundary model, the dust particle movement path is estimated, collision detection and stagnation trajectory calibration are performed, candidate deposition paths are generated, and the deposition probability distribution structure is calculated; The expression of the deposition probability distribution is: in, Represents the spatial voxel V m The probability of dust particles depositing in the c Indicates the total number of candidate paths; is the predicted value of the deposition probability of the jth path; X j represents the j-th dust particle movement path; is the indicator function; Obtain the deposition probability distribution structure, extract high-risk deposition areas and generate electrode control areas, construct the electrostatic diversion field control structure, and calculate the control parameter matrix of the diversion electrode; Obtaining the control parameter matrix of the diversion electrode, converting the control parameter matrix into electrode instructions and driving the diversion control module to perform dust particle deflection actions and generate dust particle deflection path data; Configure dust collection areas based on dust particle deflection path data, perform spatial aggregation and particle adsorption operations, and generate residual dust particle monitoring data; The residual dust monitoring data and historical control parameters are combined to generate a feedback control vector, and the control parameters are updated to complete the adaptive adjustment.

2. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The constructing of a stable airflow parameter set includes: Obtain wind speed, temperature and pressure data in the air duct, perform structured analysis and format unified processing, and generate air duct airflow characteristic data; Normalize and remove disturbances from the air duct airflow characteristic data to generate a stable airflow parameter set; An airflow state boundary model is constructed based on a set of stable airflow parameters.

3. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The estimating of the dust particle movement path comprises: Obtain the airflow state boundary model, set parameters based on the particle size range, and perform dust particle movement path estimation.

4. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The execution of collision detection and stagnation trajectory calibration includes: The dust particle movement path is collided with the detection and stagnation trajectory is calibrated to generate candidate deposition paths.

5. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The calculation of the deposition probability distribution structure includes: The deposition probability distribution structure in the wind channel area is calculated based on the candidate deposition paths.

6. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The construction of the electrostatic flow field control structure includes: Obtain the sedimentation probability distribution structure, extract high-risk sedimentation areas and construct the spatial control area structure; Perform electric field boundary segmentation and target offset direction planning on the spatial control region structure to generate the electrode control region; The electrostatic flow field control structure is generated based on the electrode control area, and the control parameter matrix of the flow-guiding electrode is calculated.

7. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The dust particle deflection operation includes: Obtain the control parameter matrix of the guide electrode to perform drive signal conversion and electrode instruction generation processing; Sending electrode instructions to the diversion control module to perform programmable diversion electrode control operations; The dust particle deflection action is performed according to the programmable guide electrode control operation to generate dust particle deflection path data.

8. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: Generating residual dust particle monitoring data includes: Obtain dust particle deflection path data, configure dust collection target areas, and schedule dust collection modules; Execute the spatial aggregation and particle adsorption operations of the dust collection module to generate dust collection control result data; The dust collection control result data is processed by residual particle identification and counting to generate residual dust particle monitoring data.

9. The method for strengthening computer dust particle prediction and adaptive dust prevention according to claim 1, characterized in that: The self-adaptive adjustment includes: Obtain residual dust monitoring data, combine the deposition probability distribution structure with the control parameter matrix, and generate a feedback control vector; Update the control parameters in the electrostatic flow field control structure according to the feedback control vector; The control parameter update results are reinjected into the electrode instruction generation process to complete the adaptive parameter adjustment process.

10. A reinforced computer dust particle prediction and adaptive dust prevention system, applied to the reinforced computer dust particle prediction and adaptive dust prevention method according to any one of claims 1 to 9, characterized in that: include: Airflow parameter acquisition module, used to obtain wind speed, temperature and pressure data and build an airflow state boundary model; a dust particle path prediction module, configured to estimate the dust particle movement path based on the airflow state boundary model and generate a deposition probability distribution structure; An electrostatic flow field generation module is used to obtain the deposition probability distribution structure, generate an electrostatic flow field control structure and calculate a control parameter matrix; A guide electrode execution module is used to convert the control parameter matrix into a guide electrode instruction and perform dust particle deflection operations; The dust collection and residual monitoring module is used to configure the dust collection area according to the dust particle deflection path data and collect the residual dust particle monitoring data; The adaptive update module is used to generate a feedback control vector according to the residual dust particle monitoring data and the control parameter matrix, and complete the control parameter update process.

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