Self-adaptive air pressure regulating system of quartz sand non-powered powder separating device

Abstract

The application discloses a self-adaptive air pressure regulation system of a quartz sand non-powered powder separation device, and relates to the technical field of automatic control.The application collects air pressure and airflow disturbance signals, generates high-resolution graphs through Hampel filtering noise reduction, extracts features and identifies key influence areas through a convolutional neural network, solves the problem of unstable air pressure caused by airflow disturbance in mountainous areas, and improves particle separation precision; a natural potential energy distribution matrix is constructed based on building height differences, driving speed field reconstruction and airflow channel optimization, combined with a gradient descent algorithm to adjust process parameters, realizing accurate use of natural potential energy, reducing energy consumption, improving uneven efficiency, simulating airflow distribution and particle trajectory through a fluid dynamics model to analyze and predict sedimentation behavior, classifying trajectories through a support vector machine and generating regulation instructions, forming a closed loop through PID control to stabilize air pressure, avoiding problems such as mixing fine powder with coarse powder and coarse particle residues, reducing the risk of device blockage, and improving product purity stability.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, specifically to an adaptive air pressure control system for a non-powered quartz sand classifier. Background Technology

[0002] The mining and processing of quartz sand in mountainous areas faces unique environmental challenges. The efficient utilization of mineral resources is crucial in this sector, as quartz sand, a fundamental industrial material, is widely used in glass manufacturing and electronics. Its powder selection process directly impacts product quality and resource recovery rates. Traditional powder selection methods often rely on fixed wind pressure settings or external power equipment. However, in mountainous terrain with varying elevations, these solutions reveal practical limitations: firstly, the equipment struggles to cope with continuous airflow disturbances caused by the terrain, leading to unstable wind pressure and affecting particle separation accuracy; secondly, power-dependent systems increase energy consumption and maintenance burdens in unpowered scenarios, failing to fully utilize natural elevation potential energy, resulting in uneven overall efficiency and resource waste.

[0003] The core technical challenge lies in the real-time capture and dynamic control of wind pressure fluctuations in the complex mountainous environment. The fluctuation characteristics of wind pressure in the airflow channel originate primarily from the uneven distribution of natural airflow caused by terrain elevation differences. This unevenness further amplifies the local deviations in the airflow velocity field within the classifying chamber, making particle settling behavior difficult to predict. For example, in actual classifying operations, when the airflow on the upstream slope suddenly intensifies, the wind pressure within the chamber rises sharply, causing fine particles to be accidentally entrained and mixed into the coarse powder output, resulting in a product purity decrease of over 15%. Meanwhile, the weakening airflow in the downstream valley leads to insufficient settling, and the residual coarse particles increase the risk of equipment blockage. These interconnected wind pressure fluctuations and airflow velocity field deviations create a chain reaction of interference, hindering the stable operation of the classifying process. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive wind pressure control system for a non-powered quartz sand classifier, which solves the core problems of unstable wind pressure, high energy consumption, insufficient potential energy utilization, and difficulty in predicting particle settling, and achieves the goal of accurate, stable, energy-saving and efficient operation of the classifier process.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This application provides an adaptive air pressure control system for a non-powered quartz sand classifier, including:

[0007] The wind pressure data acquisition and preprocessing module collects real-time wind pressure fluctuation data and airflow disturbance signals from the mountainous terrain environment through a sensor network deployed at multiple locations in the powder classifier chamber, and obtains the initial wind pressure distribution map.

[0008] The wind pressure fluctuation pattern recognition module uses a convolutional neural network algorithm to process airflow disturbance signals based on the initial wind pressure distribution map, and determines the wind pressure fluctuation pattern and the area affected by particle separation accuracy.

[0009] If the wind pressure fluctuation pattern exceeds the preset threshold, the airflow velocity field optimization module will use the natural height difference potential energy to adjust the airflow velocity field deviation and obtain the optimized airflow channel parameters.

[0010] The particle settling behavior prediction module obtains particle settling behavior prediction data from the optimized airflow channel parameters to determine whether the separation accuracy has reached the target level.

[0011] The particle trajectory classification and control module uses a support vector machine algorithm to classify the trajectories of coarse and fine powder particles based on the predicted particle settling behavior data, and obtains a dynamic control command sequence.

[0012] The wind pressure stabilization and efficiency correction module drives the non-powered regulating valve through a dynamic control command sequence, obtains real-time feedback wind pressure stability indicators, and determines the efficiency unevenness correction scheme.

[0013] If the uneven efficiency correction scheme shows a risk of resource waste, the potential energy utilization path optimization module will use the gradient descent algorithm to optimize the potential energy utilization path and obtain the final powder selection process control sequence.

[0014] The system adaptability assessment module extracts product purity improvement indicators from the final powder selection process control sequence to determine whether the overall system response meets the requirements for adaptability to mountainous environments.

[0015] The beneficial effects of this invention are as follows:

[0016] By working together with the wind pressure data acquisition and preprocessing module and the wind pressure fluctuation pattern recognition module, the problem of unstable wind pressure caused by airflow disturbance in mountainous terrain is solved. Based on the collection of wind pressure fluctuation and airflow disturbance signals by the multi-position sensor network of the powder separation chamber, the high-resolution wind pressure map is generated after data processing. Then, the disturbance features are extracted and key influencing areas are identified through the convolutional neural network, and the dynamic wind pressure pattern is accurately captured. This breaks the limitation of traditional fixed wind pressure settings in dealing with terrain airflow interference and significantly improves the stability of particle separation accuracy.

[0017] By using the airflow velocity field optimization module and the potential energy utilization path optimization module, the problems of high energy consumption and insufficient utilization of natural elevation difference potential energy in traditional power-dependent systems are solved. When wind pressure fluctuations exceed the threshold, a natural potential energy distribution matrix is ​​constructed based on the building floor elevation difference to drive the velocity field reconstruction and optimize the airflow channel configuration. At the same time, the process parameter ratio is dynamically adjusted through the gradient descent algorithm, which realizes the precise utilization of natural potential energy, gets rid of dependence on external power equipment, effectively reduces system energy consumption, improves the uneven efficiency, and reduces resource waste.

[0018] By implementing closed-loop control through a particle settling behavior prediction module, a particle trajectory classification and control module, and a wind pressure stabilization and efficiency correction module, the problem of unpredictable particle settling caused by the chain interference formed by wind pressure fluctuations and airflow velocity field deviations is solved. Based on optimizing airflow parameters to simulate airflow distribution, combined with particle trajectory analysis to predict settling behavior, the trajectory is classified by support vector machine and control commands are generated. Then, the wind pressure is stabilized by PID control, which effectively avoids problems such as fine powder mixed with coarse powder and coarse particle residue, reduces the risk of equipment blockage, improves product purity stability, and ensures continuous and stable operation of the powder selection process. Attached Figure Description

[0019] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0020] Figure 1 This is a schematic diagram of the adaptive air pressure control system of the non-powered quartz sand classifier provided in Embodiment 1 of this application;

[0021] Figure 2 This is a flowchart illustrating the airflow velocity field optimization module in the adaptive wind pressure control system of the quartz sand non-powered classifier provided in Embodiment 1 of this application.

[0022] Figure 3 This is a flowchart illustrating the particle trajectory classification and control module in the adaptive wind pressure control system of the quartz sand non-powered classifier provided in Embodiment 1 of this application. Detailed Implementation

[0023] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0024] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0026] Example 1

[0027] Please see Figures 1-3 This embodiment provides an adaptive air pressure control system for a non-powered quartz sand classifier, including:

[0028] The wind pressure data acquisition and preprocessing module collects real-time wind pressure fluctuation data and airflow disturbance signals from the mountainous terrain environment through a sensor network deployed at multiple locations in the powder classifier chamber, and obtains the initial wind pressure distribution map.

[0029] Furthermore, the wind pressure data acquisition and preprocessing module includes:

[0030] Time series analysis is used to extract periodic variation features from the collected real-time wind pressure fluctuation data to obtain a wind pressure dynamic pattern. If there are outliers in the wind pressure dynamic pattern, Hampel filtering is used to smooth the wind pressure fluctuation data to obtain smoothed wind pressure data.

[0031] Among them, based on the real-time wind pressure fluctuation data collected by the multi-position sensor network of the powder selection cavity in the mountainous terrain environment, the time series analysis method is used to capture the dynamic change law of wind pressure over time. By mining the periodic features such as the recurring fluctuation period and amplitude change in the data, a wind pressure dynamic model that can accurately reflect the wind pressure change trend during the powder selection process is constructed. For the wind pressure fluctuation data collected by the powder selection cavity sensors in the mountainous terrain environment, which contains noise and extreme interference, Hampel filtering with strong robustness to outliers is used for smoothing: first, a sliding window adapted to the time series characteristics of wind pressure data is set, and the median of the data in the window is used as the benchmark value. The absolute deviation of each data point in the window from the median is calculated and the median of the absolute deviation is obtained. Then, the outlier judgment threshold is determined based on the median of the deviation. By replacing the outlier data points in the window that exceed the threshold with the median of the window, the complete wind pressure fluctuation dataset is traversed window by window. Finally, random noise and extreme interference caused by complex airflow and terrain disturbance in the mountainous area are removed to obtain smooth wind pressure data that can accurately reflect the real change trend of wind pressure in the powder selection cavity. The Hampel filtering is for the outlier processing of the initial collected data.

[0032] By calculating the Pearson correlation coefficient between smoothed wind pressure data and airflow disturbance data, the correlation strength between wind pressure and airflow disturbance is determined. Through spatial interpolation, smoothed wind pressure data deployed at multiple locations and airflow disturbance data are fused to generate a high-resolution wind pressure distribution map.

[0033] Specifically, the spatial interpolation method employs Kriging interpolation to perform spatial fusion and generate a high-resolution wind pressure distribution map. This involves spatial coordinate matching of wind pressure and airflow disturbance data at discrete sampling points to establish a sample dataset containing location information (latitude / longitude / cavity coordinates) and corresponding physical quantities. Then, spatial correlation of the sample data is analyzed using a variogram, calculating the distance and data variability between different sampling points. A theoretical variogram model reflecting the spatial variation law of the data, such as a spherical model or an exponential model, is fitted to determine the spatial autocorrelation range and intensity parameters. Finally, based on this model, a grid is applied to the entire powder selection cavity. The system is divided into grids (with high-resolution grid cell sizes set). For each grid node, weighting coefficients are calculated and weighted interpolation is performed based on its spatial distance from surrounding known sampling points and variogram parameters. This process integrates the spatial characteristics of smoothed wind pressure data and airflow disturbance data. Finally, the interpolation parameters are optimized through cross-validation (such as adjusting the search radius and iteratively optimizing the weight matrix) to reduce edge effects and data bias. Ultimately, a high-resolution wind pressure distribution map is generated that can accurately depict the details of the spatial distribution of wind pressure inside the cavity and has a resolution adapted to actual control needs. This map clearly presents the coordinated changes in wind pressure and airflow disturbance in different regions.

[0034] Cluster analysis was used to extract zoning features from high-resolution wind pressure distribution maps to determine the spatial pattern of wind pressure distribution. Combined with mountain terrain data, the deployment location of the sensor network was optimized through grid partitioning to obtain an optimized data acquisition system configuration.

[0035] Specifically, addressing the issues of noise and extreme values ​​in wind pressure data generated by complex airflow and terrain disturbances in mountainous terrain, as well as the difficulty of discrete sampling in reflecting the overall distribution and unreasonable sensor deployment, this study uses Hampel filtering to smooth the data and Kriging interpolation to generate high-resolution wind pressure distribution maps. Combined with cluster analysis and terrain data optimization, the study ultimately obtains high-quality data that accurately reflects the spatial synergistic changes in wind pressure and airflow disturbances, as well as an efficient data acquisition system configuration adapted to mountainous environments.

[0036] The wind pressure fluctuation pattern recognition module uses a convolutional neural network algorithm to process airflow disturbance signals based on the initial wind pressure distribution map, and determines the wind pressure fluctuation pattern and the area affected by particle separation accuracy.

[0037] Furthermore, the wind pressure fluctuation pattern recognition module includes:

[0038] Initial wind pressure distribution map data is obtained, and the wind pressure values ​​are standardized to obtain a standardized wind pressure data matrix. A convolutional neural network algorithm is used to extract features from the standardized wind pressure data matrix, and the perturbation signal feature vector is obtained through convolutional and pooling layers.

[0039] The standardized wind pressure data matrix, which includes the spatial distribution information of wind pressure across the entire powder selection cavity, is used for feature extraction using a convolutional neural network algorithm. This involves applying convolutional kernels of different sizes to multiple convolutional layers, performing sliding window convolution operations on the data matrix to capture local correlation features of wind pressure in the spatial dimension (such as gradient changes and differences in regional disturbance intensity). An activation function is then used to enhance the expression of nonlinear features. Subsequently, a pooling layer downsamples the convolution results, preserving key features while reducing data dimensionality and minimizing redundant information interference. Through alternating rounds of convolution and pooling, the original wind pressure data matrix is ​​gradually transformed into a high-dimensional disturbance signal feature vector that condenses the core features of airflow disturbance (such as disturbance range, intensity distribution, and spatial correlation), providing accurate feature support for subsequent pressure gradient calculation and wind pressure fluctuation pattern recognition.

[0040] The pressure gradient change rate is calculated based on the feature vector of the disturbance signal. If the pressure gradient change rate at a certain monitoring point exceeds the preset threshold, it is determined that there is significant airflow disturbance at the corresponding monitoring point. The wind pressure value in the area of ​​significant airflow disturbance is analyzed in the time domain using the time series analysis method to obtain the time series parameters of the wind pressure fluctuation law.

[0041] The time-domain analysis method employs time series analysis to capture the amplitude characteristics of wind pressure fluctuations by calculating the mean, standard deviation, and range of wind pressure over different time periods using a sliding window. Autocorrelation analysis is used to identify recurring fluctuation cycles in the data (such as the periodic disturbance intervals caused by mountainous terrain). Trend decomposition (such as STL decomposition) is combined to separate the long-term trend, periodic, and random noise terms in the wind pressure sequence, clarifying the persistence characteristics of the disturbances. Finally, time-series parameters, including average wind pressure, fluctuation range, main cycle duration, peak frequency, and duration of continuous disturbances, are extracted to fully characterize the fluctuation pattern of wind pressure in the time domain, providing a time-domain characteristic basis for subsequent frequency domain transformation and particle separation accuracy correlation analysis.

[0042] Frequency domain transformation technology is used to convert time series parameters into frequency response data. The main fluctuation frequency range is determined based on the peak distribution of the frequency response data. Based on the correspondence between the main fluctuation frequency range and particle size, the target particle size range is obtained by looking up a pre-established particle size-frequency mapping table. If the target particle size range matches the expected separation accuracy requirement, the wind pressure fluctuation mode and the area affected by particle separation accuracy are determined.

[0043] Among them, the pre-established particle size-frequency mapping table has been established through statistical analysis to clarify the one-to-one correspondence between different quartz sand particle size ranges and the main fluctuation frequencies of wind pressure, covering the full particle size range from coarse powder to fine powder and the corresponding characteristic frequency thresholds.

[0044] Specifically, it solves the problems of complex and difficult-to-identify wind pressure fluctuation patterns in mountainous terrain and the difficulty in locating key areas affecting particle separation accuracy. By extracting airflow disturbance features through convolutional neural networks and combining time series analysis and frequency domain transformation to correlate particle size and fluctuation frequency, it can accurately identify wind pressure fluctuation patterns and clarify areas that have a significant impact on separation accuracy, providing a basis for precise control of the powder selection process.

[0045] If the wind pressure fluctuation pattern exceeds the preset threshold, the airflow velocity field optimization module will use the natural elevation difference potential energy to adjust the airflow velocity field deviation and obtain the optimized airflow channel parameters.

[0046] Furthermore, the airflow velocity field optimization module includes:

[0047] S11. Obtain real-time wind pressure sensor data, calculate the fluctuation range change sequence of wind pressure value through a sliding window, and if the fluctuation range exceeds the preset threshold point, trigger the potential energy difference calculation module to calculate the natural potential energy distribution matrix based on the height difference data of each floor of the building.

[0048] The process involves first collecting real-time wind pressure sensor data from the cavity and key airflow channels of the powder classifier, and then setting a sliding window size and step size adapted to the dynamic response characteristics of the wind pressure. The continuous time-series wind pressure data is then segmented and extracted piece by piece. The fluctuation intensity is quantified by calculating the difference between the maximum and minimum wind pressure values ​​within each window, thus forming a complete sequence of wind pressure fluctuation amplitude changes. The calculation of the natural potential energy distribution matrix involves retrieving the actual height difference data of each layer of the structure where the powder classifier is located. Based on the correlation conversion logic between gravitational potential energy and airflow potential energy, a natural potential energy distribution matrix that can accurately characterize the differences in natural potential energy distribution in different areas inside the equipment is generated through spatial grid modeling and global numerical extrapolation.

[0049] S12. The potential energy distribution matrix is ​​used to drive the velocity field reconstruction algorithm to correct the deviation distribution of the current airflow velocity field. The flow distribution coefficient and pressure gradient parameters of each airflow channel section are calculated using the corrected velocity field data.

[0050] The algorithm uses the natural potential energy distribution matrix as the core driving data and inputs it into a preset velocity field reconstruction algorithm. First, it combines the geometric parameters of the airflow channel of the powder classifier with the real-time monitoring data of the current airflow velocity field to locate the deviation areas in the velocity field that do not match the natural potential energy distribution and cause wind pressure fluctuations (such as areas where the local flow velocity is too fast or too slow, or areas of energy loss caused by airflow eddies). Then, based on the global potential energy gradient law characterized by the potential energy distribution matrix, it uses fluid dynamics numerical iterative calculation to dynamically adjust the velocity vector of the deviation area, gradually correcting the deviation distribution state of the current airflow velocity field, so that the velocity field and the natural potential energy distribution are in synergistic adaptation. After the velocity field reconstruction correction is completed, based on the real-time flow velocity data, cross-sectional geometric dimensions, and fluid viscosity coefficient of each airflow channel section after correction, it uses the continuity equation and Bernoulli equation to derive and calculate the flow distribution coefficient (quantifying the proportion of airflow flow in different channels) and pressure gradient parameters (reflecting the rate of pressure change along the airflow direction) of each airflow channel section, providing accurate fluid dynamics basis for subsequent airflow channel parameter adjustment.

[0051] S13. Adjust the channel diameter control command according to the flow distribution coefficient to obtain the opening adjustment value of each channel node. Use parameter group fusion processing to superimpose the opening adjustment value with the original channel parameters to obtain a new channel geometric configuration. If the optimization evaluation value under the new configuration reaches the convergence condition, output the final airflow channel parameter combination scheme to provide optimized airflow conditions for the quartz sand classifier.

[0052] The parameter group fusion processing includes: determining the corresponding relationship between the opening adjustment parameter group (including the opening angle increment / decrease and adjustment priority of each airflow channel node) and the original channel parameter group (including core parameters such as the initial pipe diameter, initial opening, cross-sectional geometric dimensions, and flow resistance coefficient of each channel), unifying parameter units and data formats, and verifying compliance; then, based on the airflow adaptation requirements of non-powered quartz sand powder selection, the parameter group fusion processing logic is adopted to perform precise node-by-node superposition calculations of the opening adjustment amount and the corresponding original channel parameters. That is, for each airflow channel node, the corresponding opening adjustment amount is superimposed based on the original channel opening, and the channel geometric correlation parameters are simultaneously fine-tuned in combination with the adaptability of parameters such as the original pipe diameter and cross-sectional dimensions and the flow distribution coefficient, to ensure that the superimposed parameters meet the constraints of channel structural mechanics and fluid dynamics; finally, a new channel geometric configuration containing the final opening of each channel node, the optimized pipe diameter, and the cross-sectional dimensions are generated, realizing the coordinated adaptation of the adjustment amount and the original parameters.

[0053] Specifically, it solves the problems of airflow velocity field deviation caused by wind pressure fluctuations exceeding preset thresholds in mountainous terrain, as well as insufficient utilization of natural elevation difference potential energy and poor adaptability of airflow conditions in non-powered sand classification scenarios. By triggering potential energy difference calculation, driving velocity field reconstruction and optimizing airflow channel parameters with natural potential energy distribution matrix, it ultimately corrects velocity field deviation and outputs a suitable airflow channel parameter combination scheme, providing stable and optimized airflow conditions for non-powered quartz sand classification and ensuring efficient operation of the classification process.

[0054] The particle settling behavior prediction module obtains particle settling behavior prediction data from the optimized airflow channel parameters to determine whether the separation accuracy has reached the target level.

[0055] Furthermore, the particle sedimentation behavior prediction module includes:

[0056] The optimized airflow channel parameters are obtained, and the airflow velocity distribution is obtained through a computational fluid dynamics model. Based on the airflow velocity distribution and particle size distribution, particle trajectory analysis is used to obtain the particle settling behavior.

[0057] The process involves optimizing airflow channel parameters (including core parameters such as the final opening of each channel, optimized pipe diameter, cross-sectional dimensions, and flow resistance coefficient) and inputting them, along with airflow medium property parameters (such as air density and viscosity coefficient), into a pre-defined computational fluid dynamics (CFD) model. Through numerical discretization (such as the finite volume method) and iterative solution, the flow state of the airflow in the powder selection cavity and each channel is simulated, generating three-dimensional airflow velocity distribution data covering the entire cavity and the cross-section of each airflow channel (clearly defining the magnitude, direction, and turbulence intensity of the flow velocity at different spatial locations). Subsequently, the actual particle size distribution data of the quartz sand raw material (including the proportion of particles in each size range, density, and other physical properties) is retrieved. Based on Newton's laws of motion and the fluid drag model, a particle trajectory analysis method is used. The airflow velocity distribution data is used as the flow field environment input for particle motion, tracking the force state and trajectory of particles of different sizes in the flow field one by one. The particle's velocity, direction changes, and residence time inside the cavity are recorded simultaneously. Finally, the data is compiled to form particle settling behavior data that comprehensively reflects the settling position, settling rate, and separation trend of quartz sand particles of different sizes in the optimized airflow field.

[0058] The settling velocity is extracted from the particle settling behavior, and the separation efficiency is calculated. If the separation efficiency is greater than the preset threshold, the separation accuracy is judged to have reached the target level, and the judgment result is output. If the separation efficiency is less than the preset threshold, the channel geometry is adjusted and the airflow channel parameters are recalculated.

[0059] Specifically, the spatial coordinates at different times are extracted from the motion trajectory data of quartz sand particles of various sizes recorded by particle settling behavior. The instantaneous settling rate of each particle is obtained by calculating the displacement change per unit time. Then, the average settling rate and rate distribution characteristics (such as the average settling rate of coarse particles and the suspension-settling boundary rate of fine particles) are statistically analyzed according to particle size range. Based on this, the effective settling areas of coarse and fine particles in the powder separation chamber are defined according to the preset target separation particle size, such as the bottom coarse powder collection area and the side fine powder separation channel. The proportion of particles that successfully settle into the corresponding effective area within the target particle size range is statistically analyzed. Combined with the adaptability of its average settling rate to the equipment operating cycle, that is, whether the settling separation is completed within the set time, the separation efficiency reflecting the effect of quartz sand particles being separated according to the target particle size is comprehensively calculated.

[0060] Based on the adjusted airflow channel parameters, the fluid dynamics model is re-executed to obtain a new airflow velocity distribution. The particle trajectory analysis is updated using the new airflow velocity distribution and particle size distribution to obtain a new separation accuracy determination result.

[0061] Specifically, it solves the problems of unpredictable particle settling behavior and inability to determine in advance whether the separation accuracy meets the standard under optimized airflow field in mountainous areas. By inputting the optimized airflow channel parameters into a computational fluid dynamics model to obtain the airflow velocity distribution, and combining it with the particle size distribution to conduct trajectory analysis to obtain settling behavior data, the separation efficiency is calculated and the channel parameters are iteratively adjusted when the standard is not met. Finally, the particle settling law is accurately predicted to ensure that the separation accuracy reaches the target level and to ensure stable powder selection quality.

[0062] The particle trajectory classification and control module uses a support vector machine algorithm to classify the trajectories of coarse and fine powder particles based on the predicted particle settling behavior data, and obtains a dynamic control command sequence.

[0063] Furthermore, the particle trajectory classification and control module includes:

[0064] S21. Sensors are used to acquire particle settling trajectory data, generating a raw dataset containing time and spatial coordinates. Through trajectory feature extraction algorithms, particle motion speed and direction features are extracted from the raw dataset to obtain particle motion patterns.

[0065] S22. If the particle motion pattern conforms to the preset classification criteria for coarse and fine powder particles, the support vector machine algorithm is used to classify the motion pattern to obtain the classification results of coarse and fine powder particles. Then, the trajectory dynamic characteristics of coarse and fine powder particles are analyzed to generate trajectory dynamic analysis data.

[0066] S23. Extract sedimentation behavior prediction parameters from the trajectory dynamic analysis data to obtain a sedimentation behavior prediction model. Generate a dynamic control command sequence corresponding to the particle sedimentation trajectory based on the sedimentation behavior prediction model. Input the control command sequence into the control system to adjust the particle sedimentation equipment parameters in real time and obtain optimized sedimentation trajectory data to ensure the efficient operation of the quartz sand classifier.

[0067] The process involves analyzing trajectory dynamics data, including the real-time velocity, directional deflection angle, distance deviation from the target settling area, and trajectory stability coefficient of fine particles. Key settling behavior prediction parameters are extracted, including the estimated time for particles to reach the preset separation area, the rate of change of trajectory deviation, and the coordinates of bifurcation points in the settling paths of particles of different sizes. These parameters are then correlated with high-quality trajectory-control effect samples labeled in historical settling data to construct a settling behavior prediction model. This model establishes a mapping relationship between prediction parameters and future settling states through regression analysis, outputting the trajectory trend and deviation risk value of particles in the future. Based on the model's real-time prediction of the current particle settling trajectory, and combined with the adjustable parameter range of the powder sorting equipment, such as the opening degree of the airflow channel valve and the guide plate angle, a targeted dynamic control command sequence is generated. This sequence includes the adjustment timing, adjustment range, and duration of each actuator. For example, for the prediction that the fine particle trajectory deviates from the collection area, the command "open valve 3 by 15° and maintain for 5 seconds" is output. This ultimately forms a control command sequence that can directly drive the control system, achieving precise intervention in the particle settling trajectory.

[0068] Specifically, it solves the problems of difficulty in accurately classifying the trajectories of coarse and fine powder particles and the tendency of settling trajectories to deviate from the target, which affects the powder selection accuracy. By acquiring particle settling trajectory data through sensors and extracting motion features, the system uses a support vector machine algorithm to classify coarse and fine powder trajectories, and combines a settling behavior prediction model to generate dynamic control commands. This allows for real-time adjustment of equipment parameters to optimize the settling trajectory, ensuring the accuracy and efficient operation of quartz sand powder selection.

[0069] The wind pressure stabilization and efficiency correction module drives the non-powered regulating valve through a dynamic control command sequence, obtains real-time feedback on wind pressure stability indicators, and determines the efficiency unevenness correction scheme.

[0070] Furthermore, the wind pressure stabilization and efficiency correction module includes:

[0071] The system acquires the current valve opening angle and the real-time value of the air pressure sensor. The data acquisition module records the timestamp and value change sequence of the air pressure data. The pressure fluctuation amplitude is calculated based on the air pressure data change sequence. If the pressure fluctuation amplitude exceeds the preset stability threshold, it is determined that the current valve status needs to be adjusted.

[0072] The Kalman filter algorithm is used to filter out noise from the wind pressure data to obtain a smoothed pressure trend curve and a predicted value. The wind pressure change pattern is analyzed through the pressure trend curve, the deviation between the current time and the target pressure value is calculated, and the valve adjustment direction and adjustment range are determined.

[0073] Specifically, for the real-time values ​​of wind pressure sensors (including the numerical change sequence with corresponding timestamps) collected during the control of the non-powered regulating valve, considering the complex airflow disturbances in mountainous areas and the random noise interference caused by equipment operation vibrations, a Kalman filter algorithm adapted to the dynamic tracking requirements of real-time feedback data is adopted. By constructing the state equation and observation equation of wind pressure time series changes, the prior prediction value of the current wind pressure is first estimated based on historical data and system model, and then the posterior error is corrected by combining the real-time collected wind pressure observation values. The filter gain is iteratively optimized to minimize the estimation error, and finally the high-frequency noise and sudden interference in the data are accurately removed, and a smooth pressure trend curve that can truly reflect the stable change trend of wind pressure is output. At the same time, the subsequent short-term wind pressure values ​​are predicted based on the time series correlation of the data, providing highly reliable data support for subsequent analysis of wind pressure change patterns and calculation of the deviation between the current and target pressure values.

[0074] The corresponding instruction sequence parameters are generated based on the adjustment range. The instruction sequence parameters include the valve rotation angle increment and the execution time interval. The instruction sequence parameters are processed by a PID control algorithm to output a standardized valve drive signal, which drives the unpowered valve to adjust its position according to the calculated angle.

[0075] In the process of using the PID control algorithm to process the command sequence parameters, the valve rotation angle increment contained in the command sequence is first used as the target value, and the current valve opening value collected in real time is used as the feedback value to calculate the deviation between the two (the difference between the target opening and the actual opening). Subsequently, the proportional (P) link outputs an instantaneous adjustment amount according to the magnitude of the deviation to quickly reduce the current deviation. The integral (I) link performs time accumulation calculation on the deviation to eliminate long-term steady-state errors and avoid adjustment lag. The derivative (D) link calculates the rate of change of the deviation, predicts the deviation trend, and outputs a suppression signal in advance to reduce overshoot. The outputs of the proportional, integral, and derivative links are weighted and superimposed to obtain the total control quantity. The total control quantity is then converted into a standardized valve drive signal, such as a pulse width modulation signal or current signal adapted to the non-powered valve actuator, to ensure that the signal amplitude and frequency meet the valve's mechanical action characteristics. This drives the valve actuator to rotate precisely according to the calculated angle increment, while simultaneously providing real-time feedback on the adjusted valve opening, forming a closed-loop control until the actual valve opening matches the target angle, completing the position adjustment.

[0076] Obtain the air pressure feedback value after valve adjustment, calculate the efficiency improvement ratio before and after adjustment, and if the efficiency improvement ratio is lower than the preset correction threshold, recalculate the adjustment parameters and generate a new correction strategy.

[0077] Kalman filtering is used for dynamic prediction of real-time data and is suitable for dynamic tracking of real-time feedback data.

[0078] Specifically, it solves the problems of large wind pressure fluctuations and strong data noise interference caused by complex airflow and equipment vibration in mountainous areas, as well as the problems of unstable wind pressure and uneven efficiency caused by inaccurate valve adjustment. By eliminating noise through Kalman filtering, precisely driving the adjustment of the non-powered valves through PID control, and iteratively correcting the strategy based on the efficiency improvement, it ultimately achieves stable wind pressure, improves powder selection efficiency, and ensures efficient operation of the equipment.

[0079] If the uneven efficiency correction scheme shows a risk of resource waste, the potential energy utilization path optimization module will use the gradient descent algorithm to optimize the potential energy utilization path and obtain the final powder selection process control sequence.

[0080] Furthermore, the potential energy utilization path optimization module includes:

[0081] The system acquires the operating status data of the powder selection system, collects the energy consumption parameters and output ratio of each process node through the efficiency monitoring module, and obtains the current system efficiency distribution matrix. If there are nodes in the efficiency distribution matrix that are lower than the preset threshold, the resource configuration anomaly detection mechanism is triggered, and the resource utilization deviation value of each node is calculated through the waste identification algorithm.

[0082] Based on the energy consumption parameters (such as energy consumption per unit time and energy conversion loss rate) and output ratios (such as the separation amount of target particle size and the output efficiency of qualified products) of each process node in the powder selection system, the waste identification algorithm first calls the preset benchmark resource utilization model. This model is constructed based on historical best operating data, process design standards, and energy efficiency benchmark values ​​under the same operating conditions, and includes the theoretical optimal resource utilization threshold for each node. Then, the actual resource utilization rate of each node is calculated, and the actual output benefit per unit of resource input is reflected by quantifying the ratio of output ratio to energy consumption parameters. The difference between the actual resource utilization rate and the corresponding node theoretical threshold in the benchmark model is then calculated, and the deviation results are standardized in combination with the node process characteristics to eliminate the difference in the dimensions of parameters of different nodes. Finally, the resource utilization deviation value of each node is obtained. The larger the absolute value of the negative deviation value (actual utilization rate is lower than the theoretical threshold), the higher the risk of resource waste at that node, providing accurate waste node location and quantification basis for the construction of the subsequent potential energy analysis model.

[0083] Based on the deviation value of resource utilization rate, a potential energy analysis model is constructed. The gradient descent algorithm is used to perform derivative calculation on the potential energy function to obtain the optimization direction vector of each process parameter. The path planning strategy is updated by optimizing the direction vector, and the ratio of wind speed, rotation speed and feed rate in the powder selection process is adjusted to obtain a new parameter adjustment scheme.

[0084] The feasibility of the parameter adjustment scheme is verified by iterative calculation. If the loss function value continues to decrease, the optimization continues. If it converges, the iteration stops and the optimal parameter combination is determined. A control sequence instruction set is generated based on the optimal parameter combination. The instructions are sent to each execution unit through the process control system to automate the powder selection process.

[0085] The system obtains operational data after adjustment, monitors the optimization effect through the energy consumption management module, records the improvement in potential energy utilization efficiency and quantitative indicators of resource saving, and provides data support for the energy-saving operation of quartz sand classifiers.

[0086] Specifically, it addresses the risks of resource waste revealed by the uneven efficiency correction scheme, as well as the problems of insufficient utilization of natural potential energy and unreasonable process parameter ratios. By collecting energy consumption and output data at each process node, it calculates the deviation value of resource utilization rate, optimizes the ratio of wind speed, rotation speed, and feed rate based on the potential energy analysis model and gradient descent algorithm, and iteratively verifies the results. Finally, it optimizes the potential energy utilization path, reduces resource waste, improves potential energy utilization efficiency, generates a precise powder selection process control sequence, and ensures energy-saving and efficient operation of the equipment.

[0087] The system adaptability assessment module extracts product purity improvement indicators from the final powder selection process control sequence to determine whether the overall system response meets the requirements for adaptability to mountainous environments.

[0088] Furthermore, the system adaptability assessment module includes:

[0089] A sensor array is used to collect temperature, pressure and flow parameters in the powder selection control sequence in real time to obtain an environmental data set. The environmental data is then processed with time stamping according to a preset sampling frequency to obtain a time-stamped environmental parameter sequence.

[0090] The environmental parameter sequence is classified and trained using the support vector machine algorithm to establish a mountainous environmental condition identification model. If the current environmental parameter exceeds the preset threshold range, the environmental adaptability assessment program is triggered to obtain the environmental adaptability assessment result.

[0091] The product purity benchmark value is calculated based on the environmental adaptability assessment results. The purity value of the current product sample is detected by a spectrometer. The purity deviation quantification data is obtained through comparative analysis to determine whether the product purity meets the expected standard. If the purity deviation quantification data shows that the deviation value is too large, the control parameter optimization program is called to correct the powder selection process parameters.

[0092] The random forest algorithm is used to perform correlation analysis on historical control parameters and purity data to determine the optimal parameter combination scheme. The operating parameters of the powder classifier are updated according to the optimal parameter combination scheme to obtain the updated product purity monitoring data.

[0093] By continuously monitoring the trend of product purity data changes, the stability of the purity improvement effect is determined. If the stability of the purity improvement effect meets the preset requirements, the current system response time and control accuracy data are recorded, and statistical analysis methods are used to calculate the system response characteristic parameters to obtain the comprehensive adaptability evaluation index of the system in mountainous environments.

[0094] Specifically, it solves the problems of unclear system adaptability after regulation and difficulty in ensuring product purity and stability caused by the complex and changeable mountain environment. By collecting environmental parameters to establish a mountain environment identification model, combining spectral analysis to detect product purity, random forest algorithm to optimize process parameters, monitoring purity to improve stability and calculating comprehensive adaptability index, it can accurately determine whether the system meets the requirements of mountain environment adaptation, ensure stable product purity, and ensure that the system operates efficiently and adaptably in mountain environment.

[0095] Example 2

[0096] This embodiment provides an adaptive wind pressure control system for a non-powered quartz sand classifier, adapted to the high-potential energy classification scenario of quartz sand mines in mountainous areas. It combines a fuzzy PID adaptive algorithm to dynamically adjust the damper opening, which differs significantly from the PID control in Embodiment 1. The core of this system is to use fuzzy logic to adapt to the wind speed fluctuation characteristics in mountainous areas in real time, achieving precise dynamic control of the damper opening (0-90°).

[0097] The specific content includes:

[0098] By deploying wind speed sensors and particle settling monitoring devices near the damper, the instantaneous wind speed fluctuation value (including turbulence intensity) and the actual settling velocity of particles of different sizes in the mountainous area are collected in real time and used as input to the fuzzy controller.

[0099] A fuzzy rule base was constructed to adapt to the wind speed characteristics in mountainous areas: Wind speed fluctuation amplitude (divided into three levels: minor, moderate, and severe disturbance) and airflow velocity field uniformity (quantified by the velocity field standard deviation: ≤0.5 m / s is uniform, 0.5-1.2 m / s is moderate, and ≥1.2 m / s is turbulent) were used as input fuzzy variables. The critical settling velocity of particles was calculated according to Stokes' law: v = (2r²(ρp-ρf)g) / (9μ), where r is the particle radius, ρp is the density of quartz sand, ρf is the air density, g is the gravitational acceleration, and μ is the aerodynamic viscosity. The deviation from the actual settling velocity (divided into five levels: large negative deviation, small negative deviation, zero deviation, small positive deviation, and large positive deviation) is used as the output fuzzy variable. Through 27 fuzzy rules such as "if the wind speed is strongly disturbed and the velocity field is disordered, and the actual settling velocity is more than 15% lower than the critical value, then the proportional coefficient Kp is increased to 1.2 times and the differential coefficient Kd is increased to 1.5 times in advance", the Kp, Ki, and Kd parameters of the PID controller are adjusted in real time.

[0100] Calculate the matching degree between the uniformity of the focused airflow velocity field and the critical settling velocity of particles: Define the matching degree γ = (average actual settling velocity / critical settling velocity) × (1 - standard deviation of velocity field / average wind speed). When γ ≥ 0.9, the matching is considered good and no adjustment is required; when 0.7 ≤ γ < 0.9, the fuzzy PID outputs a fine adjustment command for the damper opening (±5°); when γ < 0.7, a large step adjustment command (5-15°) is output, and the upper limit of the opening does not exceed 90° and the lower limit is not lower than 0°. For example, if a sudden gust of wind in a mountainous area causes a sharp increase in wind speed of 3 m / s, the standard deviation of the velocity field rises to 1.8 m / s, and the actual settlement velocity decreases by 20% from the critical value, then γ = 0.63. The fuzzy rule triggers the parameter combination of "Kp = 1.3 × initial value, Kd = 1.6 × initial value, Ki = 0.8 × initial value", driving the damper opening to increase from the current 30° to 45°. Within 3 seconds, the standard deviation of the velocity field drops to 0.6 m / s, the actual settlement velocity rises back to 92% of the critical value, and γ rises to 0.91, thus completing the rapid cancellation of wind speed fluctuations.

[0101] The entire process achieves online self-tuning of PID parameters through fuzzy logic, eliminating the need to preset fixed adjustment thresholds. It is more suitable for the complex characteristics of alternating "sudden change-gradual change" wind speed in mountainous areas. Compared with the conventional PID adjustment in Example 1, the response speed is improved by 40%, the overshoot is reduced to within 5%, and the damper opening adjustment accuracy is controlled within ±1°, ensuring that the particle separation efficiency is stable at over 92%.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An adaptive air pressure control system for a non-powered quartz sand classifier, characterized in that: include: The wind pressure data acquisition and preprocessing module collects real-time wind pressure fluctuation data and airflow disturbance signals from the mountainous terrain environment through a sensor network deployed at multiple locations in the powder classifier chamber, and obtains the initial wind pressure distribution map. The wind pressure fluctuation pattern recognition module uses a convolutional neural network algorithm to process airflow disturbance signals based on the initial wind pressure distribution map, and determines the wind pressure fluctuation pattern and the area affected by particle separation accuracy. If the wind pressure fluctuation pattern exceeds the preset threshold, the airflow velocity field optimization module will use the natural height difference potential energy to adjust the airflow velocity field deviation and obtain the optimized airflow channel parameters. The particle settling behavior prediction module obtains particle settling behavior prediction data from the optimized airflow channel parameters to determine whether the separation accuracy has reached the target level. The particle trajectory classification and control module uses a support vector machine algorithm to classify the trajectories of coarse and fine powder particles based on the predicted particle settling behavior data, and obtains a dynamic control command sequence. The wind pressure stabilization and efficiency correction module drives the non-powered regulating valve through a dynamic control command sequence, obtains real-time feedback wind pressure stability indicators, and determines the efficiency unevenness correction scheme. If the uneven efficiency correction scheme shows a risk of resource waste, the potential energy utilization path optimization module will use the gradient descent algorithm to optimize the potential energy utilization path and obtain the final powder selection process control sequence.

2. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The wind pressure data acquisition and preprocessing module includes: Time series analysis is used to extract periodic variation features from the collected real-time wind pressure fluctuation data to obtain a wind pressure dynamic pattern. If there are outliers in the wind pressure dynamic pattern, Hampel filtering is used to smooth the wind pressure fluctuation data to obtain smoothed wind pressure data. By calculating the Pearson correlation coefficient between smoothed wind pressure data and airflow disturbance data, the correlation strength between wind pressure and airflow disturbance is determined. Through spatial interpolation, smoothed wind pressure data deployed at multiple locations and airflow disturbance data are fused to generate a high-resolution wind pressure distribution map. Cluster analysis was used to extract zoning features from high-resolution wind pressure distribution maps to determine the spatial pattern of wind pressure distribution. Combined with mountain terrain data, the deployment location of the sensor network was optimized through grid partitioning to obtain an optimized data acquisition system configuration.

3. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The wind pressure fluctuation pattern recognition module includes: Initial wind pressure distribution map data is obtained, and the wind pressure values ​​are standardized to obtain a standardized wind pressure data matrix. A convolutional neural network algorithm is used to extract features from the standardized wind pressure data matrix, and the perturbation signal feature vector is obtained through convolutional and pooling layers. The pressure gradient change rate is calculated based on the feature vector of the disturbance signal. If the pressure gradient change rate at a certain monitoring point exceeds the preset threshold, it is determined that there is significant airflow disturbance at the corresponding monitoring point. The wind pressure value in the area of ​​significant airflow disturbance is analyzed in the time domain using the time series analysis method to obtain the time series parameters of the wind pressure fluctuation law. Frequency domain transformation technology is used to convert time series parameters into frequency response data. The main fluctuation frequency range is determined based on the peak distribution of the frequency response data. The correspondence between the main fluctuation frequency range and particle size is established. The target particle size range is obtained by looking up a pre-established particle size-frequency mapping table. If the target particle size range matches the expected separation accuracy requirement, the wind pressure fluctuation mode and the area affected by particle separation accuracy are determined.

4. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The airflow velocity field optimization module includes: The system acquires real-time wind pressure sensor data and calculates the fluctuation range sequence of wind pressure values ​​through a sliding window. If the fluctuation range exceeds a preset threshold, the potential energy difference calculation module is triggered to calculate the natural potential energy distribution matrix based on the height difference data of each floor of the building. A potential energy distribution matrix-driven velocity field reconstruction algorithm is used to correct the deviation distribution in the current airflow velocity field. The flow distribution coefficient and pressure gradient parameters of each airflow channel section are calculated using the corrected velocity field data. The channel diameter size control command is adjusted according to the flow distribution coefficient to obtain the opening adjustment value of each channel node. The parameter group fusion processing is used to superimpose the opening adjustment value with the original channel parameters to obtain a new channel geometry configuration. If the optimization evaluation value under the new configuration reaches the convergence condition, the final airflow channel parameter combination scheme is output.

5. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The particle sedimentation behavior prediction module includes: The optimized airflow channel parameters are obtained, and the airflow velocity distribution is obtained through a computational fluid dynamics model. Based on the airflow velocity distribution and particle size distribution, particle trajectory analysis is used to obtain the particle settling behavior. The settling velocity is extracted from the particle settling behavior, and the separation efficiency is calculated. If the separation efficiency is greater than the preset threshold, the separation accuracy is judged to have reached the target level, and the judgment result is output. If the separation efficiency is less than the preset threshold, the channel geometry is adjusted and the airflow channel parameters are recalculated. Based on the adjusted airflow channel parameters, the fluid dynamics model is re-executed to obtain a new airflow velocity distribution. The particle trajectory analysis is updated using the new airflow velocity distribution and particle size distribution to obtain a new separation accuracy determination result.

6. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The particle trajectory classification and control module includes: Sensors are used to acquire particle settling trajectory data, generating a raw dataset containing time and spatial coordinates. A trajectory feature extraction algorithm is used to extract particle motion velocity and direction features from the raw dataset to obtain the particle motion pattern. If the particle motion pattern meets the preset classification criteria for coarse and fine powder particles, the support vector machine algorithm is used to classify the motion pattern to obtain the classification results of coarse and fine powder particles. Then, the trajectory dynamic characteristics of coarse and fine powder particles are analyzed to generate trajectory dynamic analysis data. Settlement behavior prediction parameters are extracted from the dynamic analysis data of the trajectory to obtain a settlement behavior prediction model. Based on the settlement behavior prediction model, a dynamic control command sequence corresponding to the particle settlement trajectory is generated to obtain the control command sequence. The control command sequence is input into the control system to adjust the particle settlement equipment parameters in real time to obtain optimized settlement trajectory data.

7. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The wind pressure stabilization and efficiency correction module includes: The system acquires the current valve opening angle and the real-time value of the air pressure sensor. The data acquisition module records the timestamp and value change sequence of the air pressure data. The pressure fluctuation amplitude is calculated based on the air pressure data change sequence. If the pressure fluctuation amplitude exceeds the preset stability threshold, it is determined that the current valve status needs to be adjusted. The Kalman filter algorithm is used to filter out noise from the wind pressure data to obtain a smoothed pressure trend curve and a predicted value. The wind pressure change pattern is analyzed through the pressure trend curve, the deviation between the current time and the target pressure value is calculated, and the valve adjustment direction and adjustment range are determined. The corresponding instruction sequence parameters are generated based on the adjustment range. The instruction sequence parameters include the valve rotation angle increment and the execution time interval. The instruction sequence parameters are processed by a PID control algorithm to output a standardized valve drive signal, which drives the unpowered valve to adjust its position according to the calculated angle. Obtain the air pressure feedback value after valve adjustment, calculate the efficiency improvement ratio before and after adjustment, and if the efficiency improvement ratio is lower than the preset correction threshold, recalculate the adjustment parameters and generate a new correction strategy.

8. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: The potential energy utilization path optimization module includes: The system acquires the operating status data of the powder selection system, collects the energy consumption parameters and output ratio of each process node through the efficiency monitoring module, and obtains the current system efficiency distribution matrix. If there are nodes in the efficiency distribution matrix that are lower than the preset threshold, the resource configuration anomaly detection mechanism is triggered, and the resource utilization deviation value of each node is calculated through the waste identification algorithm. Based on the deviation value of resource utilization rate, a potential energy analysis model is constructed. The gradient descent algorithm is used to perform derivative calculation on the potential energy function to obtain the optimization direction vector of each process parameter. The path planning strategy is updated by optimizing the direction vector, and the ratio of wind speed, rotation speed and feed rate in the powder selection process is adjusted to obtain a new parameter adjustment scheme. The feasibility of the parameter adjustment scheme is verified by iterative calculation. If the loss function value continues to decrease, the optimization continues. If it converges, the iteration stops and the optimal parameter combination is determined. A control sequence instruction set is generated based on the optimal parameter combination. The instructions are sent to each execution unit through the process control system to automate the powder selection process. Acquire system operation data after regulation, monitor the optimization effect through the energy consumption management module, and record the improvement in potential energy utilization efficiency and quantitative indicators of resource saving.

9. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 1, characterized in that: Also includes: The system adaptability assessment module extracts product purity improvement indicators from the final powder selection process control sequence to determine whether the overall system response meets the requirements for adaptability to mountainous environments.

10. The adaptive air pressure control system for the non-powered quartz sand classifier according to claim 9, characterized in that: The system adaptability assessment module includes: A sensor array is used to collect temperature, pressure and flow parameters in the powder selection control sequence in real time to obtain an environmental data set. The environmental data is then processed with time stamping according to a preset sampling frequency to obtain a time-stamped environmental parameter sequence. The environmental parameter sequence is classified and trained using the support vector machine algorithm to establish a mountainous environmental condition identification model. If the current environmental parameter exceeds the preset threshold range, the environmental adaptability assessment program is triggered to obtain the environmental adaptability assessment result. The product purity benchmark value is calculated based on the environmental adaptability assessment results. The purity value of the current product sample is detected by a spectrometer. The purity deviation quantification data is obtained through comparative analysis to determine whether the product purity meets the expected standard. If the purity deviation quantification data shows that the deviation value is too large, the control parameter optimization program is called to correct the powder selection process parameters. The random forest algorithm is used to perform correlation analysis on historical control parameters and purity data to determine the optimal parameter combination scheme. The operating parameters of the powder classifier are updated according to the optimal parameter combination scheme to obtain the updated product purity monitoring data. By continuously monitoring the change trend of the product purity data, the stability of the purity improvement effect is judged. If the stability of the purity improvement effect meets the preset requirements, the current system response time and control accuracy data are recorded, and the system response characteristic parameters are calculated using statistical analysis methods to obtain the comprehensive adaptability evaluation index of the system in the mountainous environment.

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