Intelligent dust remover control method and system based on electrostatic adsorption

Abstract

The invention provides an intelligent dust remover control method and system based on electrostatic adsorption. The method comprises the following steps: acquiring dust concentration, temperature and humidity and particulate matter impedance spectrum data of a working environment, screening key measurement points, applying an alternating electric field to obtain an impedance phase difference map, performing frequency spectrum consistency verification on the impedance phase difference map and the impedance spectrum data after temperature and humidity compensation, and calculating conductivity discrimination parameters of dust with different particle sizes; deploying a parallel metal plate adsorption array containing an insulating dielectric layer, and adjusting the plate spacing to change the electric field intensity; generating a resistivity space distribution thermodynamic diagram based on the conductivity discrimination parameters, and cooperating with drive board spacing adjustment and voltage output according to the resistance gradient change of the insulation and conductive dust gathering area and the migration path; and a measurement point location screening strategy is updated in combination with the spacing adjustment amplitude and the voltage change rate, and the impedance spectrum precision is optimized. Precise identification of insulation and conductivity multi-mode dust and dynamic electric field self-adaptive adsorption are achieved, the dust removal efficiency of a complex environment is improved, and energy consumption is reduced.

Description

Technical Field

[0001] This application relates to the field of intelligent dust collector control technology, and in particular to an intelligent dust collector control method and system based on electrostatic adsorption. Background Technology

[0002] In typical open-plan home spaces like living rooms, the environment is characterized by complex and variable dust sources. Mixed dust may include human dander, pet hair, clothing fibers, food scraps, plant pollen, and externally infiltrated suspended particles. These dust particles exhibit significant differences in their physicochemical properties, leading to varying responses to different dust removal methods. Existing intelligent dust removal equipment typically operates in fixed adsorption modes (such as constant electric field strength or mechanical filtration), making dynamic optimization based on the real-time composition and characteristics of the dust (especially conductivity and insulation) difficult. This results in several key problems: unstable adsorption efficiency, poor removal of specific dust types (such as micron-sized lightweight fibers and high-resistivity dust); inability to actively distinguish dust types for targeted adsorption; and limited energy consumption optimization. Therefore, there is an urgent need for an intelligent control method capable of online sensing of the multimodal characteristics of dust (especially electrical characteristics) and adaptively adjusting core adsorption parameters (such as electric field strength and spatial distribution) in real time to improve the overall dust removal efficiency, adaptability, and energy efficiency ratio in complex and variable home environments.

[0003] Currently, a highly targeted approach is real-time monitoring technology for dust concentration and particle size distribution based on optical sensors (such as laser scattering). This approach utilizes a miniature laser emitter to illuminate dust particles in an airflow, captures the scattered light signal using a high-sensitivity photodetector, and then uses signal processing algorithms to calculate the particle concentration and approximate particle size distribution. The system can determine the type of pollution source or activate equipment based on the changing trends of different preset particle size concentration thresholds. Based on this real-time optical sensing data, some high-end devices can execute simple dynamic control strategies, such as automatically increasing fan speed to enhance airflow circulation when a surge in fine particulate matter concentration is detected. However, the core limitations of this approach are: the sensors mainly provide physical size and concentration information, making it difficult to reliably distinguish the chemical composition and key electrical properties of dust; optical scattering has weak ability to identify tiny, lightweight fibers or transparent particles and is easily affected by ambient light; it lacks the ability to perceive the specific aggregation morphology of dust in space; and it cannot utilize the differences in the electrical properties of dust to accurately match and optimize the intensity and distribution of electrostatic adsorption fields. Its dynamic control mainly remains at the level of macro air volume or fixed mode switching, and cannot provide the necessary input basis for multimodal dust differential adsorption strategies based on electrical characteristics. Summary of the Invention

[0004] This application provides a control method and system for an intelligent dust collector based on electrostatic adsorption, which solves the problems of dust removal efficiency imbalance caused by inaccurate dust type identification, sluggish electric field intensity adjustment, and poor adaptability to mixed working conditions in the prior art.

[0005] In a first aspect, this application provides a control method for an intelligent dust collector based on electrostatic adsorption, comprising: The system collects dust concentration, temperature and humidity data, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment. Key measurement points are selected based on the dust concentration distribution. An alternating electric field is applied to the key measurement points to obtain impedance phase difference spectrum. The impedance phase difference spectrum is then compensated based on the temperature and humidity. Obtain the spectral consistency verification results between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and calculate the conductivity discrimination parameters of dust particles of different sizes based on the spectral consistency verification results; Multiple sets of parallel metal plates are deployed to form an adsorption array. An insulating dielectric layer is embedded between the parallel metal plates to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted by a lifting device to change the electric field intensity distribution for dust adsorption control. The mixed distribution state of insulating dust and conductive dust is identified based on the conductivity discrimination parameters to generate a resistivity spatial distribution heat map. Based on the resistance gradient change of the insulating dust accumulation area and the migration path of conductive dust in the obtained resistivity spatial distribution heat map, the lifting device is coordinated to adjust the spacing of the parallel metal plates and match the voltage output value. Feedback data on the adjustment range of the spacing between parallel metal plates and the rate of change of the voltage output value are obtained, and the screening strategy of key measurement points is updated based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control.

[0006] Optionally, dust concentration, temperature and humidity data, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment are collected. Key measurement points are selected based on the dust concentration distribution, and an alternating electric field is applied to these key measurement points to obtain impedance phase difference spectra. The impedance phase difference spectra are then compensated based on the temperature and humidity data, including: Dust concentration acquisition equipment is deployed in the working environment to obtain real-time dust concentration at each point to generate a dust concentration distribution map. Simultaneously, temperature and humidity sensors are used to acquire temperature and humidity at each point to generate a temperature and humidity distribution map. Particulate matter impedance spectrum data is generated by multi-frequency electrical signal excitation. Obtain the outline of a continuous region in the dust concentration distribution map where the dust concentration exceeds a preset threshold, and mark the location coordinates of the continuous region outline as key measurement points. An alternating electric field is applied at the marked key measurement points, and the phase change of the current under the excitation of the alternating electric field is measured to generate an initial impedance phase difference spectrum. The temperature and humidity of each key measurement point in the temperature and humidity distribution map are extracted to calculate the temperature and humidity compensation coefficient of the corresponding key measurement point. The compensation coefficient is then superimposed on the initial impedance phase difference map to compensate the impedance phase difference map.

[0007] Optionally, the spectral consistency verification results of the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data are obtained, and the conductivity discrimination parameters of dust particles of different sizes are calculated based on the spectral consistency verification results, including: The phase difference values ​​at different frequency points in the compensated impedance phase difference spectrum are extracted, and the impedance characteristic values ​​at the same frequency points in the particulate matter impedance spectrum data are extracted, and the phase difference at each frequency point is recorded. By comparing the changing trend of the phase difference value with the changing trend of the impedance characteristic value at the same frequency point, if the changing trends are in the same direction and the difference in the fluctuation amplitude of the phase difference value and the impedance characteristic value is less than a preset error threshold, the frequency point is marked as a verification point. The percentage of verified points across all frequency points is statistically analyzed, and the proportion of the number of verified points to the total number of frequency points is calculated as the spectrum consistency verification result. For different particle size dust classification intervals, the average value of impedance characteristic values ​​at frequency points within each classification interval is extracted, and the threshold value of the discrimination boundary between insulating dust and conductive dust is determined based on the magnitude of the average value as the discrimination parameter for the conductivity performance of dust of different particle sizes.

[0008] Optionally, multiple sets of parallel metal plates are deployed to form an adsorption array. An insulating dielectric layer is embedded between the parallel metal plates to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted using a lifting device to change the electric field intensity distribution for dust adsorption control. This includes: Multiple sets of parallel metal plate arrays are arranged in the dust removal area. Each set of parallel metal plates is filled with an insulating dielectric layer to form a capacitor structure. The position coordinates and spacing parameters of each metal plate are recorded. The spacing parameters between the parallel metal plates are adjusted by the lifting device. When the spacing parameters are reduced to a preset threshold, the deformation detection of the insulating dielectric layer is triggered to ensure that the insulating dielectric layer uniformly covers the gaps between the parallel metal plates. The electric field intensity distribution between parallel metal plates under different spacing parameters is measured, and a correspondence spectrum between the spacing parameters and the electric field intensity distribution is generated based on the electric field intensity distribution data. Based on the corresponding relationship diagram, the spacing parameters are matched in reverse to the electric field intensity distribution, and the position of the parallel metal plates is dynamically adjusted to the target spacing value by the lifting device to stabilize the electric field intensity distribution for dust adsorption control.

[0009] Optionally, the spacing parameter between the parallel metal plates is adjusted by a lifting device. When the spacing parameter decreases to a preset threshold, deformation detection of the insulating dielectric layer is triggered to ensure that the insulating dielectric layer uniformly covers the gaps between the parallel metal plates, including: The displacement mechanism of the parallel metal plates is driven by the lifting device to reduce the distance between adjacent parallel metal plates, and the reduction process of the distance parameter is monitored in real time. When the spacing is reduced to a preset safety threshold, the deformation detection process of the insulating dielectric layer embedded between the metal plates is initiated. The deformation detection process collects the thickness change data of the dielectric layer on the telescopic sensors deployed at multiple points. Calculate the difference in thickness variation data at different points at the same time point. If the difference exceeds the allowable fluctuation range, mark it as an uneven coverage area and record its location. The spacing reduction operation is paused for the uneven coverage area, and the lifting device is controlled to fine-tune the tilt angle parameter of the corresponding metal plate in the area until the thickness change difference detected again is lower than the allowable fluctuation range; After the thickness variation difference of the entire area reaches the target, a confirmation signal is generated to ensure that the insulating dielectric layer is uniformly covered. The lifting device is then driven to continue to reduce the spacing to the target value to ensure that the insulating dielectric layer uniformly covers the gap between the parallel metal plates.

[0010] Optionally, the mixed distribution state of insulating dust and conductive dust is identified based on the conductivity discrimination parameters to generate a resistivity spatial distribution heat map. Based on the resistance gradient change in the insulating dust accumulation area and the migration path of the conductive dust in the obtained resistivity spatial distribution heat map, the lifting device is coordinated to adjust the spacing of the parallel metal plates and match the voltage output value, including: Extract the markers for insulating dust and conductive dust from the conductivity discrimination parameters at different locations, and merge adjacent areas with the same markers to generate a spatial distribution map of insulating and conductive areas; The resistivity difference at each point within the insulation region is calculated based on the spatial distribution map, and the boundary line where the change in resistivity difference exceeds a preset threshold is marked to generate the resistance gradient change trajectory of the insulation region. Identify continuous paths where conductivity increases continuously in a conductive region and trace the extension direction of the continuous paths to generate conductive dust migration path trajectories. The resistance gradient change trajectory and the conductive dust migration path trajectory are superimposed on the same spatial coordinates. When the resistance gradient and conductivity of the intersection region of the resistance gradient change trajectory and the conductive dust migration path trajectory increase synchronously, the intersection region is output as the key adjustment region. The magnitude of the resistance gradient change is obtained, and the spacing of the parallel metal plates is adjusted proportionally according to the magnitude. At the same time, the voltage output value is synchronously matched according to the spacing of the parallel metal plates.

[0011] Optionally, feedback data on the adjustment range of the spacing between the parallel metal plates and the rate of change of the voltage output value are obtained, and the screening strategy for key measurement points is updated based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control, including: The adjustment range of the parallel metal plate spacing performed by the lifting device is collected, and the rate of change of the voltage output value is recorded synchronously to generate a feedback dataset of adjustment range and rate of change. Extract the abnormal time periods in the feedback dataset where the adjustment amplitude exceeds a preset threshold, and mark the continuous intervals in which the rate of change of the voltage output value continuously increases within the abnormal time periods. Locate the spatial position of the work environment corresponding to the abnormal time period, and obtain the impedance phase difference spectrum data collected from the original key measurement points of the work environment spatial position; By comparing the fluctuation pattern of the impedance phase difference spectrum during the abnormal time period with the fluctuation pattern of the rate of change, if the difference in fluctuation pattern exceeds the allowable range, it is determined that there is an error in the measurement at that point. For points with errors, adjacent auxiliary measurement points are added, and the screening strategy for key measurement points is updated. The screening strategy involves including the adjacent areas of the error points into the set of key points and re-acquiring the compensated impedance phase difference spectrum to achieve iterative optimization of acquisition accuracy, thereby realizing multimodal dust adaptive adsorption control.

[0012] Secondly, this application provides an intelligent dust collector control system based on electrostatic adsorption, comprising: The acquisition module is used to acquire dust concentration, temperature and humidity, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment. It also selects key measurement points based on the dust concentration distribution, applies an alternating electric field to the key measurement points to obtain impedance phase difference spectrum, and compensates the impedance phase difference spectrum based on the temperature and humidity. The calculation module is used to obtain the spectral consistency verification results between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and to calculate the conductivity discrimination parameters of dust with different particle sizes based on the spectral consistency verification results. The deployment module is used to deploy multiple sets of parallel metal plates to form an adsorption array. The parallel metal plates are embedded with an insulating dielectric layer to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted by a lifting device to change the electric field intensity distribution for dust adsorption control. The generation module is used to identify the mixed distribution state of insulating dust and conductive dust according to the conductivity discrimination parameters to generate a resistivity spatial distribution heat map, and based on the resistance gradient change of the insulating dust accumulation area and the migration path of conductive dust in the obtained resistivity spatial distribution heat map, it drives the lifting device to adjust the spacing of the parallel metal plates and match the voltage output value. The optimization module is used to obtain feedback data on the adjustment range of the spacing between parallel metal plates and the rate of change of the voltage output value, and to update the screening strategy of key measurement points based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control.

[0013] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the intelligent dust collector control method based on electrostatic adsorption as described in the first aspect above.

[0014] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a smart dust collector control method based on electrostatic adsorption as described in the first aspect.

[0015] In this application's technical solution, precise identification of dust characteristics is achieved through multi-band electrical signal excitation and impedance phase difference spectrum analysis, combined with temperature and humidity compensation technology to improve measurement accuracy. The technical effects include: constructing a resistivity spatial distribution thermogram based on conductivity discrimination parameters to intelligently distinguish between insulating and conductive dust distributions; achieving adaptive matching of electric field strength by dynamically adjusting the spacing of parallel metal plates and voltage output, significantly improving the adsorption efficiency of complex dust components; and employing a feedback mechanism to continuously optimize the measurement strategy, forming a closed-loop control system. This method overcomes the limitations of traditional electrostatic dust removal technology in addressing single dust characteristics, achieving intelligent identification and precise adsorption of multimodal dust. It is suitable for the efficient treatment of mixed dust in industrial environments and can maintain stable dust removal performance even under complex working conditions.

[0016] Furthermore, through the synergistic analysis of dust concentration distribution maps and temperature and humidity distribution maps, high-concentration dust areas are precisely located as key measurement points. The technical effects include: using multi-band electrical signal excitation to acquire particulate matter impedance characteristics, and combining this with alternating electric field phase difference measurement to improve the sensitivity of dust characteristic identification; dynamically correcting the impedance phase difference map using temperature and humidity compensation coefficients to effectively eliminate the interference of environmental factors on the measurement results. This method achieves intelligent identification and precise measurement of dust concentration hotspots, providing high-precision input parameters for subsequent electrostatic adsorption control, significantly improving the accuracy and reliability of dust monitoring in complex industrial environments, and laying the foundation for differentiated management of multimodal dust.

[0017] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a smart dust collector control method based on electrostatic adsorption provided in this application is shown; Figure 2 A scene diagram of an intelligent dust collector control system based on electrostatic adsorption provided in this application is shown; Figure 3 A schematic diagram of the structure of an intelligent dust collector control system based on electrostatic adsorption provided in this application is shown. Figure 4 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0021] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0022] Studies have shown that existing smart dust removal technologies for open spaces in homes have significant limitations: while optical sensing-based dust monitoring solutions can capture particulate matter concentration and size distribution, they lack precision in adsorption strategies because they cannot perceive the electrical properties and spatial aggregation morphology of dust. Specifically, laser scattering struggles to distinguish the essential differences between insulating pollen and conductive dander, resulting in serious missed detections of micron-sized lightweight fibers; fixed electric field strength adsorption modes cannot adapt to the spatial resistivity distribution differences of mixed dust, leading to the escape of high-resistivity dust or over-adsorption of conductive particles; and control strategies only provide a macroscopic response by adjusting airflow, failing to dynamically optimize the electric field strength by incorporating changes in dust resistance gradients. These shortcomings stem from the inherent blind spots in the perception of the chemical and electrical properties of dust in traditional technologies, necessitating a breakthrough from the constraints of monitoring only single physical parameters.

[0023] To address the aforementioned challenges, this invention proposes a smart dust collector control method based on electrostatic adsorption. Its innovation lies in achieving differentiated capture of multi-modal dust through a collaborative closed-loop mechanism of impedance spectrum dynamic interpretation and an adjustable electric field array. Specifically, it integrates multi-frequency electrical signal excitation to generate particulate matter impedance spectra, and combines impedance phase difference spectrum compensation technology under an alternating electric field to calculate the conductivity discrimination parameters of dust particles of different sizes in real time. Simultaneously, it deploys an array of parallel metal plates with adjustable spacing, and based on the insulating dust accumulation area and conduction damping path revealed by the resistivity spatial distribution thermogram, it collaboratively drives the dynamic matching of the metal plate spacing and voltage output. This method revolutionizes the traditional fixed adsorption model: spatial electrical characteristic sensing technology achieves millisecond-level dynamic differentiation between insulating and conductive dust for the first time, solving the blind spot of optical solutions for identifying lightweight particles; the adaptive adjustment mechanism of the electric field strength and distribution pattern of the parallel plate array directly transforms the change in dust resistance gradient into the basis for electric field optimization, eliminating the escape of high-resistivity dust and local over-adsorption from the root; and the closed-loop feedback mechanism continuously iterates and optimizes the monitoring strategy through adsorption efficiency data, ultimately forming a self-reinforcing control cycle of "characteristic sensing - field strength matching - adsorption verification", which significantly improves dust removal efficiency and energy utilization in complex home environments.

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] To address the aforementioned system, this application provides a method for controlling an intelligent dust collector based on electrostatic adsorption. Figure 1 A flowchart of a smart dust collector control method based on electrostatic adsorption is provided in this application embodiment, as shown below. Figure 1 As shown, the method includes: 101. Collect dust concentration, temperature and humidity data, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment. Select key measurement points based on the dust concentration distribution, apply an alternating electric field to the key measurement points to obtain impedance phase difference spectrum, and compensate the impedance phase difference spectrum based on the temperature and humidity.

[0026] Optionally, step 101 may specifically include the following steps: 1011. Deploy dust concentration acquisition equipment in the working environment to obtain real-time dust concentration at each point to generate a dust concentration distribution map. Simultaneously, acquire temperature and humidity at each point through temperature and humidity sensors to generate a temperature and humidity distribution map. Generate particulate matter impedance spectrum data through multi-frequency electrical signal excitation.

[0027] 1012. Obtain the outline of the continuous region in the dust concentration distribution map where the dust concentration exceeds a preset threshold, and mark the outline of the continuous region as the location coordinates of the key measurement point.

[0028] 1013. Apply an alternating electric field to the marked key measurement points and measure the current phase change value under the excitation of the alternating electric field to generate an initial impedance phase difference spectrum.

[0029] 1014. Extract the temperature and humidity of each key measurement point in the temperature and humidity distribution map to calculate the temperature and humidity compensation coefficient of the corresponding key measurement point, and superimpose the compensation coefficient with the initial impedance phase difference map to compensate the impedance phase difference map.

[0030] In the above scheme, dust concentration is the mass of dust contained in a unit volume of air in the working environment. Temperature and humidity are comprehensive parameters of ambient temperature and relative humidity. Multi-band electrical signal excitation is the operation of exciting particulate matter response through electrical signals of different frequencies. Particulate matter impedance spectrum data is a set of impedance characteristics of dust particles at different frequencies. Key measurement points are locations that require key monitoring when dust concentration exceeds limits. Alternating electric field is an electric field excitation source with periodically changing direction. Impedance phase difference spectrum is the phase difference distribution graph generated by dust under alternating electric field. Dust concentration distribution spectrum is the spatial distribution map of dust concentration values ​​in the working space. Temperature and humidity distribution spectrum is the spatial distribution map of temperature and humidity values ​​in the working space. Preset threshold is the critical value for determining whether dust concentration exceeds limits. Continuous region outline is the continuous spatial boundary of the area where dust concentration exceeds limits. Location coordinates are the spatial positioning data of key measurement points in the working environment. Current phase change value is the phase offset of current and voltage under alternating electric field. Initial impedance phase difference spectrum is the uncompensated original phase difference distribution map. Temperature and humidity compensation coefficients are calibration parameters used to correct for the effects of temperature and humidity. Superposition is the operation of fusing the compensation coefficients with the phase difference spectrum. Compensation is the process of correcting temperature and humidity errors in the impedance phase difference spectrum. Dust concentration acquisition equipment is a specialized instrument for measuring the dust content in the air. Temperature and humidity sensors are sensing devices that simultaneously detect temperature and humidity. Multi-band electrical signal excitation is a technique that excites particulate matter response through frequency sweeping. Alternating electric field excitation is a method of applying a periodically changing electric field to measure dust characteristics. Phase difference spectrum compensation is a process of correcting measurement errors using environmental parameters.

[0031] In this embodiment, firstly, through step 1011, dust concentration acquisition equipment and temperature and humidity sensors are deployed comprehensively within the working environment. The dust concentration acquisition equipment captures dust concentration data at various spatial locations in real time and generates a dust concentration distribution map. Simultaneously, the temperature and humidity sensors collect temperature and humidity data at the same locations in parallel to generate a temperature and humidity distribution map. The system synchronously activates a multi-band electrical signal exciter to emit excitation signals of specific frequencies into the air. By capturing the electrical response of particulate matter through receiving electrodes, particulate matter impedance spectrum data is generated, forming a multi-dimensional basic dataset of dust physicochemical properties.

[0032] Subsequently, through step 1012, the system analyzes the dust concentration distribution map and uses a contour extraction algorithm to identify the contours of continuous areas where the dust concentration continuously exceeds a preset threshold: based on image connected component analysis, the system scans the boundary points of high-concentration areas, calculates the set of coordinates of closed contours, and accurately marks the physical locations covered by these contours as the location coordinates of key measurement points, thereby realizing the automated calibration of high-risk dust areas.

[0033] Next, in step 1013, based on the coordinates of the marked key measurement points, the control electrode device applies an alternating electric field with specific parameters at the corresponding points. Simultaneously, the phase measurement module captures the phase shift of the current and voltage waveforms. Through waveform integration, an initial impedance phase difference spectrum reflecting the dielectric properties of particulate matter is generated. This spectrum is directly related to the charge storage and dissipation capacity of the dust.

[0034] Finally, in step 1014, the system extracts the temperature and humidity values ​​corresponding to each key measurement point in the temperature and humidity distribution map, converts the temperature and humidity values ​​into environmental parameter temperature and humidity compensation coefficients (such as the correction weight of humidity on dielectric constant) using a compensation calculator, maps the compensation coefficients to the initial impedance phase difference map according to the coordinate position, and uses a matrix multiplication algorithm to superimpose the map data and compensation coefficients to generate the final impedance phase difference map with environmental compensation, thus eliminating the interference error of temperature and humidity on the electrical measurement results.

[0035] In practical applications, in the operating environment of coal-fired boilers in thermal power plants, dust concentration acquisition equipment (high-sensitivity PM10 sensor array) is deployed at the boiler flue outlet and coal conveying corridor to acquire dust concentration at each point in real time and generate a dust concentration distribution map. Simultaneously, environmental parameters at each point are collected through a temperature and humidity sensor network to generate a temperature and humidity distribution map. At the same time, multi-frequency electrical signals are applied to the dust particles to excite them, and the charge response of the particles is measured to generate particulate impedance spectrum data (step 1011). Based on the dust concentration distribution map, the system identifies continuous high-concentration areas where the dust concentration exceeds a preset threshold (such as dust accumulation areas at boiler flue bends), extracts their spatial contours, and marks them as the location coordinates of key measurement points (step 1012). An alternating electric field is applied to the marked key measurement points (flue bend monitoring ports), and the current phase change value caused by dust charge migration under the action of the electric field is measured in real time to generate an initial impedance phase difference map reflecting the dielectric properties of the dust (step 1013). Subsequently, the system extracts the real-time temperature and humidity of the corresponding key measurement points in the temperature and humidity distribution map (such as the high temperature and high humidity environment of the flue), calculates the dielectric constant offset caused by the temperature and humidity change as the temperature and humidity compensation coefficient (for example, the phase lag effect needs to be compensated in the high temperature environment), and superimposes and fuses the coefficient with the initial impedance phase difference map, and finally outputs the compensated impedance phase difference map, providing accurate dust electrical characteristic parameters for electrostatic adsorption control (step 1014).

[0036] The overall scheme in step 101 above achieves collaborative sensing and dynamic compensation of multimodal dust characteristics. Key measurement points are located using dust concentration distribution maps, constructing a high-precision spatial sampling mechanism. By combining temperature and humidity distribution maps to calculate point compensation coefficients, environmental interference is eliminated from the impedance phase difference map under alternating electric fields, solving the accuracy degradation problem of traditional single-point measurements due to environmental temperature and humidity interference. This technology overcomes the limitations of static detection, realizing real-time linked acquisition and compensation of multi-source physical quantities (concentration / temperature / humidity / impedance), providing a high-fidelity data foundation for dust characteristic analysis.

[0037] 102. Obtain the spectral consistency verification results between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and calculate the conductivity discrimination parameters of dust particles of different sizes based on the spectral consistency verification results.

[0038] Optionally, step 102 may specifically include the following steps: 1021. Extract the phase difference values ​​at different frequency points in the compensated impedance phase difference spectrum, and at the same time extract the impedance characteristic values ​​at the same frequency points in the particulate matter impedance spectrum data, and record the phase difference at each frequency point.

[0039] 1022. Compare the changing trend of the phase difference value with the changing trend of the impedance characteristic value at the same frequency point. If the changing trends are in the same direction and the difference in the fluctuation amplitude of the phase difference value and the impedance characteristic value is less than the preset error threshold, then mark the frequency point as a verified point.

[0040] 1023. Calculate the percentage of verified points among all frequency points and use the percentage of verified points to the total number of frequency points as the spectrum consistency verification result.

[0041] 1024. For the classification intervals of dust with different particle sizes, the average value of the impedance characteristic value of the frequency point in each classification interval is extracted, and the threshold value of the discrimination boundary between insulating dust and conductive dust is divided according to the magnitude of the average value as the discrimination parameter of the conductivity performance of dust with different particle sizes.

[0042] In the above scheme, the spectrum consistency verification result is an indicator for evaluating the degree of matching between the impedance phase difference spectrum and the particulate matter impedance spectrum data. The conductivity discrimination parameter is a quantitative standard for distinguishing the conductivity characteristics of dust. Frequency points are measurement data points corresponding to specific electrical signal frequencies. Phase difference value is the phase shift between current and voltage under an alternating electric field. Impedance characteristic value is the impedance characteristic value of particulate matter at a specific frequency. The trend of change is the direction of increase or decrease in data with frequency. Fluctuation amplitude is the magnitude of the numerical range of data changes. The preset error threshold is the upper limit of the allowable error for determining data matching. Verified points are frequency measurement points that meet the spectrum consistency conditions. The proportion is the ratio of the number of verified points to the total number of points. The classification interval is the data grouping according to the dust particle size range. The average value of the impedance characteristic value is the arithmetic mean of the impedance data within the same particle size range. Insulating dust is a dust category with high resistance characteristics. Conductive dust is a dust category with low resistance characteristics. The discrimination boundary threshold is the critical parameter value for distinguishing between insulating and conductive dust. Particle size classification intervals are dust groups categorized according to particle size. Consistent direction refers to a trend of either increasing or decreasing. Fluctuation amplitude difference is the difference between phase difference and impedance characteristic fluctuation. Total frequency points are the set of all measured frequency points. Conductivity is the physical property of dust's ability to conduct electricity. Quantification standards are the numerical criteria for determining conductivity. Matching degree is the consistency evaluation level of two sets of data. Arithmetic mean is the method for calculating the average value of the same set of data. Critical parameter values ​​are key values ​​for classifying dust with different properties.

[0043] In this embodiment, firstly, through step 1021, the system extracts the phase difference values ​​at different frequency points in the compensated impedance phase difference spectrum, and simultaneously captures the impedance characteristic values ​​at the same frequency points in the particulate matter impedance spectrum data. This process ensures that the phase difference value at each frequency point is strictly aligned with the impedance characteristic value through a frequency coordinate matching algorithm, forming a dataset with a one-to-one correspondence in frequency dimensions.

[0044] Subsequently, in step 1022, a trend comparison is performed independently at each frequency point: the direction of change of the phase difference (e.g., monotonically increasing / decreasing) is dynamically analyzed to see if it is consistent with the direction of change of the impedance characteristic value (i.e., both rising or falling simultaneously), and the fluctuation amplitude difference (absolute difference between peaks and troughs) is calculated. If the directions are consistent and the fluctuation amplitude difference is less than the preset error threshold (the allowable deviation range set by the system), then the frequency point is marked as a verification pass point. Verification is achieved through dynamic slope calculation and amplitude difference algorithm.

[0045] Next, in step 1023, the total number of verified points across all frequency points is counted, and the percentage of verified points is calculated (e.g., number of verified points ÷ total number of points × 100%). The spectral consistency verification result is output in percentage form. This result quantifies the data matching reliability between the compensated spectrum and the original impedance spectrum.

[0046] Finally, in step 1024, based on the preset classification intervals for different particle sizes of dust (e.g., 0-5μm), the impedance characteristic values ​​at corresponding frequency points are extracted for each interval. After calculating the arithmetic mean of the impedance characteristic values ​​within each classification interval, the system automatically identifies significant inflection points in the average value distribution (e.g., locations where the mean value of adjacent intervals changes by more than 30%), and sets this inflection point value as the discrimination boundary threshold between insulating and conductive dust, generating the final conductivity discrimination parameters for dust particles of different sizes. These parameters provide the core electrical basis for dust explosion-proof design.

[0047] In practical applications, in the intelligent dust removal system of a cement plant packaging workshop, the system first acquires the compensated impedance phase difference spectrum (the dielectric property spectrum of cement dust corrected for temperature and humidity) and particulate matter impedance spectrum data (the original spectrum generated by multi-band electrical signal excitation). To accurately determine the conductivity of the dust, the system extracts the phase difference values ​​at different frequency points in the compensated impedance phase difference spectrum, and simultaneously extracts the impedance characteristic values ​​(the combined resistance and capacitance values ​​at the corresponding frequency points) at the same frequency points in the particulate matter impedance spectrum data, and records the phase difference at each frequency point completely (step 1021). Subsequently, the system compares the changing trend of the phase difference value with the changing trend of the impedance characteristic value at the same frequency point: taking a 10kHz frequency point as an example, if the phase difference value increases with the increase of frequency, and the impedance characteristic value shows a synchronous upward trend, and the difference in the fluctuation amplitude of the two is less than the preset error threshold (such as the trend direction is consistent and the amplitude deviation is controllable), then the frequency point is marked as a verification pass point (step 1022). After completing the full-band scan, the system calculates the percentage of verified points across all frequency points and uses the ratio of verified points to the total number of frequency points as the spectral consistency verification result to confirm the reliability of the coordination between impedance and phase data (step 1023). Based on this verification result, the system extracts the average impedance characteristic values ​​of frequency points within different particle size classification intervals (e.g., 0-5μm fine particles and 5-10μm coarse particles). Based on the magnitude of the average value, a threshold is established to distinguish between insulating dust (high impedance mean) and conductive dust (low impedance mean). For example, if the impedance mean in the fine particle interval is higher than a certain threshold, it is classified as insulating dust; otherwise, it is classified as conductive dust. This threshold serves as the conductivity discrimination parameter for dust particles of different sizes, providing a classification basis for the subsequent intelligent adjustment of electrostatic adsorption voltage (step 1024).

[0048] The overall scheme in step 103 above achieves cross-modal verification of impedance spectroscopy and phase difference spectroscopy, as well as the analysis of the dust conductivity mechanism. Based on the trend consistency determination mechanism of phase difference values / impedance characteristic values, a two-way verification model of spectral characteristics is constructed. The confidence level of the data is quantified through statistical analysis of the percentage of points, and insulation / conductivity gradient thresholds are defined by combining the mean impedance characteristics of particle size ranges. This technology establishes a quantitative mapping relationship between spectral characteristics and conductivity performance for the first time, breaking through the bottleneck of mechanism characterization in traditional single impedance spectroscopy analysis.

[0049] 103. Multiple sets of parallel metal plates are deployed to form an adsorption array. An insulating dielectric layer is embedded between the parallel metal plates to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted by a lifting device to change the electric field intensity distribution for dust adsorption control.

[0050] Optionally, step 103 may specifically include the following steps: 1031. Multiple sets of parallel metal plate arrays are arranged in the dust removal area. Each set of parallel metal plates is filled with an insulating dielectric layer to form a capacitor structure. The position coordinates and spacing parameters of each metal plate are recorded.

[0051] 1032. Adjust the spacing parameters between the parallel metal plates using a lifting device. When the spacing parameters are reduced to a preset threshold, trigger the deformation detection of the insulating dielectric layer to ensure that the insulating dielectric layer uniformly covers the gaps between the parallel metal plates.

[0052] Step 1032 may specifically include the following process: The displacement mechanism of the parallel metal plates is driven by a lifting device to reduce the distance between adjacent parallel metal plates, and the reduction process of the distance parameter is monitored in real time; when the distance is reduced to a preset safety threshold, a deformation detection process for the insulating dielectric layer embedded between the metal plates is initiated. This deformation detection process collects thickness change data of the dielectric layer on telescopic sensors deployed at multiple points; the difference in thickness change data at each point at the same time is calculated. If the difference exceeds the allowable fluctuation range, it is marked as an uneven coverage area and its location is recorded; the distance reduction operation is paused for the uneven coverage area, and the lifting device is controlled to fine-tune the tilt angle parameter of the corresponding metal plate in that area until the thickness change difference detected again is lower than the allowable fluctuation range; after the thickness change difference of the entire area meets the standard, a confirmation signal for uniform coverage of the insulating dielectric layer is generated, and the lifting device is driven to continue reducing the distance to the target value to ensure that the insulating dielectric layer uniformly covers the gap between the parallel metal plates.

[0053] 1033. Measure the electric field intensity distribution between parallel metal plates under different spacing parameters, and generate a correspondence spectrum between the spacing parameters and the electric field intensity distribution based on the electric field intensity distribution data.

[0054] 1034. Based on the corresponding relationship diagram, the electric field intensity distribution is matched with the spacing parameter in reverse, and the position of the parallel metal plate is dynamically adjusted to the target spacing value through the lifting device to stabilize the electric field intensity distribution for dust adsorption control.

[0055] In the above scheme, parallel metal plates are conductive flat plate components constituting the capacitor structure. The adsorption array is a dust collection system composed of multiple sets of parallel metal plates. The insulating dielectric layer is the insulating material filling the spaces between the metal plates for isolation. The capacitor structure is a capacitor unit composed of metal plates and the dielectric layer. The lifting device is a mechanical drive mechanism for adjusting the spacing between the metal plates. The electric field intensity distribution is the numerical distribution of the electric field intensity at various points in space. The dust removal area is the working space where dust adsorption treatment is required. The position coordinates are the spatial positioning data of the metal plates in the dust removal area. The spacing parameter is the numerical value of the vertical distance between adjacent metal plates. Deformation detection is the process of measuring the deformation of the insulating dielectric layer under pressure. The preset threshold is the critical spacing value that triggers deformation detection. The displacement mechanism is a mechanical device for adjusting the position of the metal plates. The safety threshold is the minimum spacing value to ensure the safety of the dielectric layer. The telescopic sensor is a sensing element for measuring the change in the thickness of the dielectric layer. The thickness change data is the measurement result of the amount of deformation of the dielectric layer under pressure. The allowable fluctuation range is the reasonable range of thickness variation differences. The uneven coverage area is the local location where the thickness difference of the dielectric layer exceeds the limit. The tilt angle parameter is the angle between the metal plate plane and the horizontal plane. The confirmation signal is a verification indicator that the dielectric layer uniformly covers the surface. The target value is the preset final spacing between the metal plates. The correspondence graph is a chart showing the mapping relationship between spacing and electric field strength. The target spacing value is the metal plate spacing required to achieve a specific electric field strength. Dust adsorption control is a dust collection operation achieved through electric field adjustment. Dynamic adjustment is the process of adjusting the metal plate spacing in real time according to demand. Stabilizing the electric field strength distribution is maintaining a uniform and controllable electric field state. Multi-point deployment is a method of installing detection devices at multiple locations. Difference calculation is a comparison of the differences in thickness changes at different points at the same time. Fine-tuning is an operation of making small-scale, precise adjustments to the position of the metal plates. Reverse matching is a method of finding the corresponding spacing based on the electric field requirements.

[0056] In this embodiment, firstly, through step 1031, multiple sets of parallel metal plate arrays are deployed in the dust removal area: the metal plates are precisely arranged according to preset coordinates using a robotic arm positioning device, and an insulating dielectric layer is filled between each set of metal plates to construct a capacitor structure. The system synchronously records the position coordinates of each metal plate and the initial spacing parameters between adjacent plates, establishing a complete array spatial topology database to provide a reference for subsequent dynamic adjustments.

[0057] Subsequently, in step 1032, the lifting device is activated to adjust the metal plate spacing parameters: when the spacing is detected to have decreased to a preset safety threshold, the system immediately triggers the insulation dielectric layer deformation detection process—multi-point telescopic sensors embedded in the dielectric layer collect thickness change data in real time and calculate the difference in thickness data at each point simultaneously. If the difference exceeds the allowable fluctuation range, it is immediately marked as an uneven coverage area and its coordinate position is recorded. At the same time, the spacing reduction operation is paused, and the lifting device is controlled to fine-tune the tilt angle parameters of the metal plate corresponding to the abnormal area. After re-detecting the thickness difference until it is below the allowable range, a uniform coverage confirmation signal for the insulation dielectric layer is generated, and the lifting device continues to drive the spacing to decrease to the target value, ensuring that the dielectric layer covers the gaps between the plates without any gaps.

[0058] Next, in step 1033, the electric field intensity distribution under different spacing parameters is scanned in real time during the spacing adjustment process: the space between the plates is scanned with millimeter-level precision using a three-dimensional electric field probe array, the field strength value of each coordinate point is recorded, a mapping relationship set of spacing parameters-electric field intensity distribution covering the entire spacing range is generated, and it is converted into a visualized correspondence spectrum (such as a heat map to show the field intensity gradient change under different spacings).

[0059] Finally, in step 1034, the optimal electric field distribution is matched according to the target dust adsorption requirements: the spacing parameters that meet the target electric field intensity distribution are retrieved from the correspondence graph; the lifting device is controlled to dynamically position the metal plate group to the target spacing value; the spacing is finely adjusted in real time based on the feedback from the electric field sensor to keep the field intensity distribution between the plates stable and maintain the set state, thereby realizing the dynamic closed loop of dust adaptive adsorption control.

[0060] In practical applications, during the retrofitting of electrostatic precipitators in coal-fired power plants, the system first deploys multiple sets of parallel metal plate arrays (such as 10 sets of stainless steel plates) in the flue. Each set of parallel metal plates is filled with a ceramic-based insulating dielectric layer to form a capacitor structure, and the position coordinates and initial spacing parameters of each metal plate are recorded (step 1031). Subsequently, the system adjusts the spacing parameters between parallel metal plates through a lifting device (hydraulic drive mechanism): when the lifting device drives the displacement mechanism to reduce the spacing between adjacent parallel metal plates, the reduction process of the spacing parameters is monitored in real time; when the spacing is reduced to a preset safety threshold, the deformation detection process of the insulating dielectric layer embedded between the metal plates is initiated—the thickness change data of the dielectric layer is collected at 12 multiple points through a telescopic sensor (such as a piezoelectric micro-displacement probe) pre-installed on the edge of the metal plate; if the difference in thickness change data at each point at the same time exceeds the allowable fluctuation range, the area is marked as an uneven coverage area and the position coordinates are recorded (such as the third set of electrode plates at the top of the flue); the system immediately pauses the spacing reduction operation in this area, controls the lifting device to fine-tune the tilt angle parameter of the corresponding metal plate until the thickness change difference detected again is lower than the allowable fluctuation range; after the full area detection meets the standard, a confirmation signal of uniform coverage of the insulating dielectric layer is generated, and then the lifting device is driven to continue to reduce the spacing to the target value to ensure that there is no local compression and cracking of the dielectric layer (step 1032). After the spacing adjustment is completed, the system measures the electric field intensity distribution between the parallel metal plates under different spacing parameters and generates a graph showing the correspondence between the spacing parameters and the electric field intensity distribution (step 1033). Based on this graph, the system reverse-matches the spacing parameters to the target electric field intensity distribution in the flue and dynamically adjusts the parallel metal plates to the target spacing value through a lifting device, so that the electric field distribution is stably adapted to the real-time flue gas dust load (step 1034).

[0061] The overall scheme in step 103 above achieves a capacitive adsorption structure with dynamic adaptive electric field strength. Multiple sets of parallel metal plates are embedded in an insulating dielectric layer to form an adjustable capacitor structure, innovatively integrating a lifting device and a deformation detection system. The electric field strength distribution is changed in real time by adjusting the spacing, overcoming the adsorption efficiency limitations of traditional fixed electrodes. Its deformation detection process ensures uniform coverage of the dielectric layer through multi-point thickness monitoring and tilt angle fine-tuning, addressing the breakdown risk under high-intensity electric fields from a hardware structural perspective, and providing a controllable electric field environment for dynamic dust adsorption.

[0062] 104. Based on the conductivity discrimination parameters, identify the mixed distribution state of insulating dust and conductive dust to generate a resistivity spatial distribution heat map, and based on the resistance gradient change of the insulating dust accumulation area and the migration path of conductive dust in the obtained resistivity spatial distribution heat map, drive the lifting device to adjust the spacing of the parallel metal plates and match the voltage output value.

[0063] 1041. Extract the markers for insulating dust and conductive dust from the conductivity discrimination parameters at different locations, and merge adjacent areas with the same markers to generate a spatial distribution map of the insulating and conductive areas.

[0064] 1042. Calculate the resistivity difference of each point inside the insulation region according to the spatial distribution map, and mark the boundary line where the change of the resistivity difference exceeds a preset threshold to generate the resistance gradient change trajectory of the insulation region.

[0065] 1043. Identify a continuous path in a conductive region where the conductivity increases continuously, and trace the extension direction of the continuous path to generate a conductive dust migration path trajectory.

[0066] 1044. Superimpose the resistance gradient change trajectory and the conductive dust migration path trajectory onto the same spatial coordinates. When the resistance gradient and conductivity of the intersection region of the resistance gradient change trajectory and the conductive dust migration path trajectory increase synchronously, output the intersection region as the key adjustment region.

[0067] 1045. Obtain the magnitude of the resistance gradient change, and adjust the spacing of the parallel metal plates proportionally according to the magnitude, while simultaneously matching the voltage output value according to the spacing of the parallel metal plates.

[0068] In the above scheme, the resistivity spatial distribution heatmap is a visual graphic reflecting the resistivity differences of dust. The mixed distribution state is the spatial arrangement pattern of insulating and conductive dust coexisting. The insulating dust discrimination mark is a data identifier for identifying high-resistivity dust. The conductive dust discrimination mark is a data identifier for identifying low-resistivity dust. The insulating region is the spatial range where high-resistivity dust accumulates. The conductive region is the spatial range where low-resistivity dust accumulates. The resistivity difference is the numerical difference in resistivity between adjacent points. The preset threshold is the critical value for determining a significant change in resistivity. The boundary line is the dividing line formed when the resistivity difference exceeds the limit. The resistance gradient change trajectory is the path of resistivity change in the insulating region. A continuous increase in conductivity indicates an increasing conductivity in the conductive region. The continuous path is the coherent channel for the migration of conductive dust. The extension direction is the spatial direction of the conductive dust migration path. The conductive dust migration path trajectory is a record of the route of conductive dust movement. Spatial coordinates are a unified reference system for the location of dust distribution. The intersection area is the spatial location where the resistance gradient and the conductive path intersect. The key adjustment area refers to the location where electric field parameters need to be adjusted first. Resistance gradient is the rate of change of resistivity with spatial location. Conductivity is a quantitative indicator of electrical conductivity. Synchronous increase in resistance gradient and conductivity increases simultaneously. Amplitude is the numerical value of the resistance gradient change. Proportional adjustment is the operation of linearly adjusting the spacing according to the change amplitude. Voltage output value is the voltage value supplied to the metal plate. Matching voltage output value is the supply voltage adjusted according to the spacing change. High-resistivity dust refers to dust particles with strong insulating properties. Low-resistivity dust refers to dust particles with strong conductive properties. Data identifiers are characteristic marks that distinguish dust types. Numerical difference is the difference in resistivity between two points. Critical value is the boundary parameter for determining whether a change is significant. Separating line is the boundary line that distinguishes regions with different resistivity. Pathway is the trend direction of resistivity change. Continuous channel is the route of continuous movement of conductive dust. Unified reference system is the standard coordinate system of spatial location. Intersection position is the spatial point where two trajectories intersect. Priority adjustment refers to the electric field adjustment area that needs to be addressed first. The rate of change is the change in resistivity per unit distance. A quantitative indicator is a numerical representation of electrical conductivity. Linear adjustment is the method of changing the spacing by a fixed proportion. The supply voltage is the potential difference applied to the metal plate.

[0069] In this embodiment, firstly, through step 1041, the system extracts the insulating dust discrimination markers (such as marker value 1) and conductive dust discrimination markers (such as marker value 0) from the conductivity discrimination parameters at different spatial locations. Then, a spatial clustering algorithm is used to merge adjacent coordinate points with the same discrimination marker into continuous regions: adjacent points with marker value 1 are clustered into insulating regions, and adjacent points with marker value 0 are clustered into conductive regions, generating a complete spatial distribution map of insulating and conductive regions, thus achieving physical spatial modeling of dust characteristics.

[0070] Subsequently, in step 1042, the resistivity difference between points within the insulation region (such as the resistivity difference between adjacent points A and B) is calculated. An edge detection algorithm is used to identify abrupt boundary changes where the difference exceeds a preset threshold (which can be ±10%), and these boundary coordinate points are marked to form the resistance gradient change trajectory of the insulation region (such as connecting abrupt change points with a broken line), visually presenting the steep change path of the insulation region's resistance.

[0071] Next, in step 1043, a continuous spatial path with continuously increasing conductivity is scanned in the conductive region: the coordinate sequence with monotonically increasing conductivity is identified by time series analysis, and the path extension trajectory is traced along the direction of increase (such as the increasing direction from coordinates (x1,y1) to (x2,y2)) to generate the conductive dust migration path trajectory, which dynamically reflects the aggregation and flow trend of conductive dust.

[0072] Then, in step 1044, the resistance gradient change trajectory and the conductive dust migration path trajectory are superimposed on the same spatial coordinate system: when the two trajectories simultaneously detect an increase in resistance gradient (such as an increase in the resistance value of the insulation area) and an increase in conductivity (such as an increase in charge conduction in the conductive area) in the intersection area; the change direction is confirmed to be consistent through the dual-channel data synchronization verification mechanism; the intersection area is automatically marked as a key adjustment area, and the dust mixing area that needs to be focused on is identified.

[0073] Finally, in step 1045, the resistance gradient change amplitude (e.g., the change in resistance per meter) of the key adjustment area is obtained in real time. The spacing of the parallel metal plates is adjusted proportionally through a linear mapping relationship (e.g., the spacing between plates increases by 5% for every 10% increase in the gradient). Simultaneously, the voltage output value is matched according to the plate spacing adjustment value (e.g., the voltage increases by 8% for every 5% increase in spacing), and the dust charging efficiency is kept stable through a voltage-spacing coupling algorithm.

[0074] In practical applications, in the intelligent control system of electrostatic precipitators in coal-fired power plants, the system first extracts the markers for insulating dust (such as high resistivity fly ash) and conductive dust (such as low resistivity carbon powder) from the conductivity discrimination parameters at different locations. Adjacent areas with the same markers are merged to generate a spatial distribution map of insulating areas (fly ash enrichment areas) and conductive areas (carbon powder accumulation areas) (step 1041). Based on this map, the system calculates the resistivity difference at each point within the insulating area (such as the resistivity abrupt change in the fly ash deposition area at the rear of the boiler flue), marks the boundary line where the resistivity difference exceeds a preset threshold (such as the resistance jump boundary at the flue corner), and generates the resistance gradient change trajectory of the insulating area (step 1042). Simultaneously, it identifies continuous paths where conductivity continuously increases in the conductive area (such as the path of carbon powder migrating towards the outlet with the airflow), and tracks the extension direction of the continuous path to generate the conductive dust migration path trajectory (step 1043). Subsequently, the resistance gradient change trajectory and the conductive dust migration path trajectory are superimposed onto the same spatial coordinates. When the resistance gradient and conductivity change synchronously increase in the intersection area (such as the mixing zone in the middle of the flue) (a sharp increase in the resistance gradient in the insulation zone is accompanied by a jump in the conductivity in the conductive zone), the intersection area is output as the key adjustment area (step 1044). The system obtains the magnitude of the resistance gradient change (such as the gradient increase caused by fly ash deposition), and adjusts the spacing of the parallel metal plates proportionally according to the magnitude (such as reducing the spacing by 10% to enhance the electric field). At the same time, the voltage output value is synchronously matched according to the spacing of the parallel metal plates (such as increasing the voltage by 5kV after reducing the spacing to maintain the electric field strength) to ensure the optimal adsorption efficiency of the mixed dust (step 1045).

[0075] The overall scheme in step 104 above achieves coordinated regulation of the spatiotemporal evolution of dust resistivity. Based on the resistivity spatial distribution thermogram, a dual-feature analysis model of "resistivity gradient change trajectory" and "conductive dust migration path trajectory" is innovatively constructed. By identifying the trajectory intersection area and matching the change amplitude, the lifting device is synchronously driven to adjust the metal plate spacing and control the output voltage in a closed loop. This technology achieves for the first time the linkage feedback control of the spatial distribution of insulating / conductive dust, overcoming the matching hysteresis problem of mixed dust adsorption.

[0076] 105. Obtain feedback data on the adjustment range of the spacing between parallel metal plates and the rate of change of the voltage output value, and update the screening strategy of key measurement points based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control.

[0077] Optionally, step 105 may specifically include the following steps: 1051. Collect the adjustment range of the spacing between the parallel metal plates operated by the lifting device, and synchronously record the rate of change of the voltage output value to generate a feedback dataset of adjustment range and rate of change.

[0078] 1052. Extract the abnormal time period in the feedback data where the adjustment amplitude exceeds the preset threshold, and mark the continuous interval in which the rate of change of the voltage output value continuously increases within the abnormal time period.

[0079] 1053. Locate the spatial position of the work environment corresponding to the abnormal time period, and obtain the impedance phase difference spectrum data collected from the original key measurement points of the work environment spatial position.

[0080] 1054. Compare the fluctuation pattern of the impedance phase difference spectrum during the abnormal time period with the fluctuation pattern of the rate of change. If the difference in fluctuation pattern exceeds the allowable range, it is determined that there is an error in the point measurement.

[0081] In the above scheme, the adjustment amplitude is the numerical range of the change in the spacing between the parallel metal plates. The rate of change of the voltage output value is the rate of change of the supply voltage over time. Feedback data is a set of records of spacing adjustment and voltage change. Key measurement points are the key locations for dust monitoring. The impedance phase difference spectrum is the phase response distribution of dust under an alternating electric field. Multimodal dust adaptive adsorption control is an intelligent adsorption method for different dust characteristics. The preset threshold is the critical value for judging abnormal adjustment amplitude. The abnormal time period is the duration of the adjustment amplitude exceeding the limit. The continuous interval is the time range of the voltage change rate continuously increasing. The spatial location of the working environment is the spatial coordinate point where the abnormality occurs. The fluctuation pattern is the characteristic pattern of data changing over time. The difference is the degree of difference between the fluctuation characteristics of two sets of data. The allowable range is the reasonable difference range for judging measurement error. Measurement error is the deviation between the collected data and the true value. The original collected data is the impedance phase difference spectrum obtained before the abnormality. Comparative analysis is the operation of comparing the characteristics of data in different time periods. The intelligent adsorption method is a control strategy that automatically adapts to dust characteristics. The duration is the length of time that the abnormal state continues to exist. Spatial coordinates refer to specific locations in three-dimensional space. Characteristic patterns represent the regularity of data changes. The degree of difference is a quantitative indicator of morphological differences. Reasonable difference is the allowable range of measurement error. The true value is the actual impedance characteristic of the dust. Deviation is the magnitude of the difference between the measured value and the true value. Automatic adaptation is a control method that automatically adjusts based on feedback. Regularity refers to the identifiable characteristics of data changes. Quantitative indicators are the numerical expression of the degree of difference. Control strategies are the processing methods used to achieve adsorption targets.

[0082] In this embodiment of the application, firstly, through step 1051, the system collects in real time the data of the spacing adjustment amplitude of the parallel metal plates executed by the lifting device (such as the spacing change in millimeters), and simultaneously monitors and records the rate of change of the voltage output value (such as the voltage change per second percentage). After aligning the two types of data by timestamp, a feedback dataset of adjustment amplitude and rate of change is generated, forming a complete time-series record of the device control behavior.

[0083] Subsequently, in step 1052, the feedback dataset is analyzed to extract abnormal time periods where the adjustment amplitude exceeds a preset threshold (e.g., a single adjustment can exceed 10% of the initial gap). Simultaneously, continuous intervals within these time periods where the rate of change of the voltage output value continuously increases (e.g., the rate of change can increase continuously for 5 seconds) are detected. These continuous intervals are then labeled using a trend recognition algorithm, establishing a correlation mapping between abnormal time periods and equipment status fluctuations.

[0084] Next, through step 1053, locate the spatial location of the working environment corresponding to the abnormal time period: according to the equipment log, look up the coordinates of the working range of the dust adsorption system during that time period, and simultaneously obtain the impedance phase difference spectrum data collected from the original key measurement points at that spatial location to ensure that the spectrum data is aligned with the time and space of the abnormal equipment status.

[0085] Finally, in step 1054, the fluctuation pattern of the impedance phase difference spectrum (such as the phase difference value oscillation waveform) and the fluctuation pattern of the voltage change rate (such as the voltage acceleration curve) are compared during the abnormal time period. If the difference between the two fluctuation patterns (such as the waveform similarity coefficient) exceeds the allowable range (preset similarity threshold), the system determines that there is a significant error in the measurement at that point and triggers the iterative update of the screening strategy for key measurement points (such as lowering the similarity threshold or increasing the monitoring frequency), thereby optimizing the acquisition accuracy of subsequent impedance phase difference spectra and realizing the adaptive adsorption control closed-loop upgrade of multimodal dust.

[0086] In practical applications, during the operation of electrostatic precipitators in coal-fired power plants, the system continuously collects the adjustment range of the parallel metal plate spacing executed by the lifting device (e.g., the adjustment amount of the plate spacing in the middle of the flue can be adjusted from 5mm to 3mm), and simultaneously records the rate of change of the voltage output value (e.g., the rate of voltage change from 10kV to 15kV), generating a feedback dataset of adjustment range and rate of change (step 1051). Based on this dataset, the system extracts abnormal time periods in the feedback dataset where the adjustment range exceeds a preset threshold (e.g., the period from 14:00 to 14:30 on a certain day when the spacing suddenly drops by 40%), and marks the continuous intervals in which the rate of change of the voltage output value continuously increases during the abnormal time period (e.g., the interval where the rate of voltage change increases continuously for 5 minutes) (step 1052). Subsequently, the system locates the spatial position of the working environment corresponding to the abnormal time period (e.g., the third electric field region at the rear of the flue), and obtains the impedance phase difference spectrum data collected from the original key measurement points at the working environment spatial position (step 1053). By comparing the fluctuation patterns of the impedance phase difference spectrum with the fluctuation patterns of its rate of change during abnormal time periods (such as the temporal correlation between a sudden increase in the spectrum phase difference and a sharp rise in the voltage rate), if the difference between the two fluctuation patterns exceeds the allowable range (such as the spectrum fluctuation lagging behind the voltage rate change), it is determined that there is an error in the measurement at that point (step 1054). Based on this error determination result, the system dynamically updates the selection strategy for key measurement points (such as adding high-frequency monitoring points in the downstream section of the flue), thereby iteratively optimizing the acquisition accuracy of the impedance phase difference spectrum, and ultimately achieving dynamic matching of the dust's electrical characteristics and electric field parameters, improving the accuracy of multimodal dust adaptive adsorption control.

[0087] The overall scheme in step 105 above achieves a self-optimizing closed loop for the adsorption control system. By adjusting the abnormal fluctuation detection of amplitude / voltage change rate, the acquisition error area of ​​the impedance phase difference spectrum is located. A feedback mechanism of "abnormal morphological difference degree - auxiliary point supplementation" is established to iteratively update the key measurement point screening strategy and reacquire the compensation spectrum. This technology forms a dynamic optimization loop from control feedback to data acquisition, breaking through the system bottleneck that static parameters cannot adapt to the dynamic migration of dust, and realizing continuous and precise control of multimodal dust adsorption.

[0088] The following is a complete embodiment for steps 101-105, such as Figure 2 As shown: Inside the boiler flue of a coal-fired power plant, the system first deploys dust concentration acquisition equipment (laser PM sensor array) to obtain real-time dust concentration at various points, generating a dust concentration distribution map. Simultaneously, temperature and humidity distribution maps are generated using temperature and humidity sensors, and multi-frequency electrical signals are applied to generate particulate matter impedance spectrum data. Based on the dust concentration distribution map, the system filters continuous areas where the dust concentration exceeds a preset threshold (such as high-concentration accumulation areas at flue bends) and marks their outlines as the location coordinates of key measurement points. An alternating electric field is applied to the marked points, and the current phase change value is measured to generate an initial impedance phase difference map. The temperature and humidity of the corresponding points are extracted to calculate temperature and humidity compensation coefficients (such as compensating for phase lag effects in high-temperature environments). The compensation coefficients are then superimposed with the initial map to generate a compensated impedance phase difference map.

[0089] Subsequently, the system extracts the phase difference values ​​at different frequency points in the compensated impedance phase difference spectrum and compares them with the impedance characteristic values ​​at the same frequency points in the particulate matter impedance spectrum data. If the trends of the two are consistent and the difference in fluctuation amplitude is less than the preset error threshold (e.g., the phase difference and impedance value at a 10kHz frequency point both increase with increasing frequency), then the frequency point is marked as a verified pass point. The percentage of verified pass points across the entire frequency band is counted as the spectrum consistency verification result. For different particle size dust classification intervals (0-5μm fine particles / 5-10μm coarse particles), the average value of the impedance characteristic values ​​of each interval is extracted. Based on the magnitude of the average value, the discrimination boundary threshold between insulating dust and conductive dust is defined (e.g., if the average impedance of the fine particle interval is >1000Ω, it is judged as insulating dust).

[0090] Based on conductivity parameters, the system deploys multiple parallel metal plate arrays within the flue, with ceramic-based insulating dielectric layers filling the spaces between the plates to form a capacitor structure. The system records the position coordinates and initial spacing parameters of each plate. As the plate spacing is reduced using a lifting device (hydraulic drive), the reduction process is monitored in real time. When the spacing reaches a preset safety threshold (e.g., 5mm), a deformation detection process for the insulating dielectric layer is initiated: multi-point telescopic sensors collect data on dielectric layer thickness changes. If the thickness difference at any given time exceeds the allowable fluctuation range (e.g., ±0.2mm), an uneven coverage area (e.g., the top of the flue) is marked, spacing reduction is paused, and the tilt angle parameters of the corresponding metal plates are fine-tuned (e.g., the height difference of the hydraulic rod can be ±0.5°). Once the thickness difference meets the standard, the spacing is reduced to the target value (e.g., 3mm) to ensure uniform dielectric layer coverage. After adjustment, the electric field intensity distribution at different spacings is measured, generating a correlation graph between spacing parameters and electric field intensity distribution (e.g., a 3mm spacing corresponds to a 15kV / m electric field). Based on this, the required spacing parameters for the target electric field are matched in reverse, and the plate spacing is dynamically adjusted to stabilize dust adsorption.

[0091] The system further extracts the indicators for insulating dust and conductive dust from the conductivity discrimination parameters at different locations, merging them to generate spatial distribution maps of the insulating region (fly ash enrichment area) and the conductive region (carbon powder accumulation area). It calculates the resistivity difference at each point within the insulating region, marks the boundary line where the difference abruptly exceeds a preset threshold (such as the resistance jump boundary at the rear of the flue), and generates a resistance gradient change trajectory. Simultaneously, it identifies continuous paths in the conductive path where conductivity continuously increases (such as the carbon powder migration path towards the outlet), generating a conductive dust migration path trajectory. After superimposing the two trajectories, when the resistance gradient and conductivity of the intersection area (such as the middle of the flue) rise synchronously, the system outputs this intersection area as the key adjustment area, and adjusts the spacing of the parallel metal plates proportionally according to the magnitude of the resistance gradient change, synchronously matching the voltage output value.

[0092] Finally, the system collects the spacing adjustment range of the lifting device and the rate of change of the voltage output value to generate a feedback dataset. It extracts abnormal time periods where the adjustment range exceeds the threshold (e.g., a sudden 40% drop in spacing between 14:00 and 14:30 on a certain day) and continuous intervals where the voltage change rate continuously increases during these periods. The system locates the corresponding spatial position in the working environment during these periods, obtains the impedance phase difference spectrum data of the original key measurement points, and compares it with the fluctuation pattern of the voltage change rate. If the spectrum fluctuation lags behind the voltage rate change, it determines that there is an error in the measurement at that point. For the erroneous points, the system adds adjacent auxiliary measurement points (e.g., adding high-frequency monitoring points at the rear of the flue), updates the key measurement point selection strategy (including the adjacent areas of the erroneous points in the key set), and re-collects the compensated impedance phase difference spectrum to achieve closed-loop optimization of multimodal dust adaptive adsorption control.

[0093] For points with errors, adjacent auxiliary measurement points are added, and the screening strategy for key measurement points is updated. The screening strategy involves including the adjacent areas of the error points into the set of key points and re-acquiring the compensated impedance phase difference spectrum to achieve iterative optimization of acquisition accuracy, thereby realizing multimodal dust adaptive adsorption control.

[0094] Figure 3 This application provides a schematic diagram of the structure of an intelligent dust collector control system based on electrostatic adsorption, as shown in the embodiment. Figure 3 As shown, the system includes: The acquisition module 31 is used to acquire dust concentration, temperature and humidity and particulate matter impedance spectrum data generated by multi-frequency electrical signal excitation in the working environment, and to screen key measurement points according to the distribution of dust concentration, apply an alternating electric field to the key measurement points to obtain impedance phase difference spectrum, and compensate the impedance phase difference spectrum based on the temperature and humidity. The calculation module 32 is used to obtain the spectral consistency verification result between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and to calculate the conductivity discrimination parameters of dust with different particle sizes based on the spectral consistency verification result. Deployment module 33 is used to deploy multiple sets of parallel metal plates to form an adsorption array. An insulating dielectric layer is embedded between the parallel metal plates to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted by a lifting device to change the electric field intensity distribution for dust adsorption control. The generation module 34 is used to identify the mixed distribution state of insulating dust and conductive dust according to the conductivity discrimination parameters to generate a resistivity spatial distribution heat map, and based on the resistance gradient change of the insulating dust accumulation area and the migration path of conductive dust in the obtained resistivity spatial distribution heat map, it drives the lifting device to adjust the spacing of the parallel metal plates and match the voltage output value. The optimization module 35 is used to obtain feedback data on the adjustment range of the spacing between the parallel metal plates and the rate of change of the voltage output value, and to update the screening strategy of key measurement points based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control.

[0095] Figure 3 The aforementioned intelligent dust collector control system based on electrostatic adsorption can perform... Figure 1 The implementation principle and technical effects of the intelligent dust collector control method based on electrostatic adsorption described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the intelligent dust collector control system based on electrostatic adsorption in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0096] In one possible design, Figure 3 The intelligent dust collector control system based on electrostatic adsorption in the illustrated embodiment can be implemented as a computing device, such as... Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42; The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 42.

[0097] The processing component 42 is used for the above Figure 1 The embodiment describes a control method for an intelligent dust collector based on electrostatic adsorption.

[0098] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0099] Storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0100] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0101] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0102] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0103] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0104] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a control method for an intelligent dust collector based on electrostatic adsorption.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A control method for an intelligent dust collector based on electrostatic adsorption, characterized in that, include: The system collects dust concentration, temperature and humidity data, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment. Key measurement points are selected based on the dust concentration distribution. An alternating electric field is applied to the key measurement points to obtain impedance phase difference spectrum. The impedance phase difference spectrum is then compensated based on the temperature and humidity. Obtain the spectral consistency verification results between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and calculate the conductivity discrimination parameters of dust particles of different sizes based on the spectral consistency verification results; Multiple sets of parallel metal plates are deployed to form an adsorption array. An insulating dielectric layer is embedded between the parallel metal plates to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted by a lifting device to change the electric field intensity distribution for dust adsorption control. The mixed distribution state of insulating dust and conductive dust is identified based on the conductivity discrimination parameters to generate a resistivity spatial distribution heat map. Based on the resistance gradient change of the insulating dust accumulation area and the migration path of conductive dust in the obtained resistivity spatial distribution heat map, the lifting device is coordinated to adjust the spacing of the parallel metal plates and match the voltage output value. Feedback data on the adjustment range of the spacing between parallel metal plates and the rate of change of the voltage output value are obtained, and the screening strategy of key measurement points is updated based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control.

2. The method according to claim 1, characterized in that, The system collects dust concentration, temperature, humidity, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment. Based on the dust concentration distribution, key measurement points are selected, and an alternating electric field is applied to these key measurement points to obtain impedance phase difference spectra. The impedance phase difference spectra are then compensated based on the temperature and humidity data, including: Dust concentration acquisition equipment is deployed in the working environment to obtain real-time dust concentration at each point to generate a dust concentration distribution map. Simultaneously, temperature and humidity sensors are used to acquire temperature and humidity at each point to generate a temperature and humidity distribution map. Particulate matter impedance spectrum data is generated by multi-frequency electrical signal excitation. Obtain the outline of a continuous region in the dust concentration distribution map where the dust concentration exceeds a preset threshold, and mark the location coordinates of the continuous region outline as key measurement points. An alternating electric field is applied at the marked key measurement points, and the phase change of the current under the excitation of the alternating electric field is measured to generate an initial impedance phase difference spectrum. The temperature and humidity of each key measurement point in the temperature and humidity distribution map are extracted to calculate the temperature and humidity compensation coefficient of the corresponding key measurement point. The compensation coefficient is then superimposed on the initial impedance phase difference map to compensate the impedance phase difference map.

3. The method according to claim 1, characterized in that, Obtain the spectral consistency verification results between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and calculate the conductivity discrimination parameters for dust particles of different sizes based on the spectral consistency verification results, including: The phase difference values ​​at different frequency points in the compensated impedance phase difference spectrum are extracted, and the impedance characteristic values ​​at the same frequency points in the particulate matter impedance spectrum data are extracted, and the phase difference at each frequency point is recorded. By comparing the changing trend of the phase difference value with the changing trend of the impedance characteristic value at the same frequency point, if the changing trends are in the same direction and the difference in the fluctuation amplitude of the phase difference value and the impedance characteristic value is less than the preset error threshold, the frequency point is marked as a verification point. The percentage of verified points across all frequency points is statistically analyzed, and the proportion of the number of verified points to the total number of frequency points is calculated as the spectrum consistency verification result. For different particle size dust classification intervals, the average value of impedance characteristic values ​​at frequency points within each classification interval is extracted, and the threshold value of the discrimination boundary between insulating dust and conductive dust is determined based on the magnitude of the average value as the discrimination parameter for the conductivity performance of dust of different particle sizes.

4. The method according to claim 1, characterized in that, Multiple sets of parallel metal plates are deployed to form an adsorption array. Insulating dielectric layers are embedded between the parallel metal plates to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted using a lifting device to change the electric field intensity distribution for dust adsorption control. This includes: Multiple sets of parallel metal plate arrays are arranged in the dust removal area. Each set of parallel metal plates is filled with an insulating dielectric layer to form a capacitor structure. The position coordinates and spacing parameters of each metal plate are recorded. The spacing parameters between the parallel metal plates are adjusted by the lifting device. When the spacing parameters are reduced to a preset threshold, the deformation detection of the insulating dielectric layer is triggered to ensure that the insulating dielectric layer uniformly covers the gaps between the parallel metal plates. The electric field intensity distribution between parallel metal plates under different spacing parameters is measured, and a correspondence spectrum between the spacing parameters and the electric field intensity distribution is generated based on the electric field intensity distribution data. Based on the corresponding relationship diagram, the spacing parameters are matched in reverse to the electric field intensity distribution, and the position of the parallel metal plates is dynamically adjusted to the target spacing value by the lifting device to stabilize the electric field intensity distribution for dust adsorption control.

5. The method according to claim 4, characterized in that, The spacing parameters between the parallel metal plates are adjusted by a lifting device. When the spacing parameters decrease to a preset threshold, deformation detection of the insulating dielectric layer is triggered to ensure that the insulating dielectric layer uniformly covers the gaps between the parallel metal plates, including: The displacement mechanism of the parallel metal plates is driven by the lifting device to reduce the distance between adjacent parallel metal plates, and the reduction process of the distance parameter is monitored in real time. When the spacing is reduced to a preset safety threshold, the deformation detection process of the insulating dielectric layer embedded between the metal plates is initiated. The deformation detection process collects the thickness change data of the dielectric layer on the telescopic sensors deployed at multiple points. Calculate the difference in thickness variation data at different points at the same time point. If the difference exceeds the allowable fluctuation range, mark it as an uneven coverage area and record its location. The spacing reduction operation is paused for the uneven coverage area, and the lifting device is controlled to fine-tune the tilt angle parameter of the corresponding metal plate in the area until the thickness change difference detected again is lower than the allowable fluctuation range; After the thickness variation difference of the entire area reaches the target, a confirmation signal is generated to ensure that the insulating dielectric layer is uniformly covered. The lifting device is then driven to continue to reduce the spacing to the target value to ensure that the insulating dielectric layer uniformly covers the gap between the parallel metal plates.

6. The method according to claim 1, characterized in that, The mixture distribution of insulating and conductive dust is identified based on the conductivity discrimination parameters to generate a resistivity spatial distribution heat map. Based on the resistance gradient change in the insulating dust accumulation area and the migration path of the conductive dust in the obtained resistivity spatial distribution heat map, the lifting device is coordinated to adjust the spacing of the parallel metal plates and match the voltage output value, including: Extract the markers for insulating dust and conductive dust from the conductivity discrimination parameters at different locations, and merge adjacent areas with the same markers to generate a spatial distribution map of insulating and conductive areas; The resistivity difference at each point within the insulation region is calculated based on the spatial distribution map, and the boundary line where the change in resistivity difference exceeds a preset threshold is marked to generate the resistance gradient change trajectory of the insulation region. Identify continuous paths where conductivity increases continuously in a conductive region, and trace the extension direction of the continuous paths to generate conductive dust migration path trajectories. The resistance gradient change trajectory and the conductive dust migration path trajectory are superimposed on the same spatial coordinates. When the resistance gradient and conductivity of the intersection region of the resistance gradient change trajectory and the conductive dust migration path trajectory increase synchronously, the intersection region is output as the key adjustment region. The magnitude of the resistance gradient change is obtained, and the spacing of the parallel metal plates is adjusted proportionally according to the magnitude. At the same time, the voltage output value is synchronously matched according to the spacing of the parallel metal plates.

7. The method according to claim 1, characterized in that, Feedback data on the adjustment range of the spacing between parallel metal plates and the rate of change of the voltage output value are obtained. Based on the feedback data, the selection strategy for key measurement points is updated to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control, including: The adjustment range of the parallel metal plate spacing performed by the lifting device is collected, and the rate of change of the voltage output value is recorded synchronously to generate a feedback dataset of adjustment range and rate of change. Extract the abnormal time periods in the feedback dataset where the adjustment amplitude exceeds a preset threshold, and mark the continuous intervals in which the rate of change of the voltage output value continuously increases within the abnormal time periods. Locate the spatial position of the work environment corresponding to the abnormal time period, and obtain the impedance phase difference spectrum data collected from the original key measurement points of the work environment spatial position; By comparing the fluctuation pattern of the impedance phase difference spectrum with the fluctuation pattern of the rate of change during the abnormal time period, if the difference in fluctuation pattern exceeds the allowable range, it is determined that there is an error in the point measurement. For points with errors, adjacent auxiliary measurement points are added, and the screening strategy for key measurement points is updated. The screening strategy involves including the adjacent areas of the error points into the set of key points and re-acquiring the compensated impedance phase difference spectrum to achieve iterative optimization of acquisition accuracy, thereby realizing multimodal dust adaptive adsorption control.

8. A smart dust collector control system based on electrostatic adsorption, characterized in that, include: The acquisition module is used to acquire dust concentration, temperature and humidity, and particulate matter impedance spectrum data generated by multi-band electrical signal excitation in the working environment. It also selects key measurement points based on the dust concentration distribution, applies an alternating electric field to the key measurement points to obtain impedance phase difference spectrum, and compensates the impedance phase difference spectrum based on the temperature and humidity. The calculation module is used to obtain the spectral consistency verification results between the compensated impedance phase difference spectrum and the particulate matter impedance spectrum data, and to calculate the conductivity discrimination parameters of dust with different particle sizes based on the spectral consistency verification results. The deployment module is used to deploy multiple sets of parallel metal plates to form an adsorption array. The parallel metal plates are embedded with an insulating dielectric layer to form a capacitor structure. The spacing between adjacent metal plates is dynamically adjusted by a lifting device to change the electric field intensity distribution for dust adsorption control. The generation module is used to identify the mixed distribution state of insulating dust and conductive dust according to the conductivity discrimination parameters to generate a resistivity spatial distribution heat map, and based on the resistance gradient change of the insulating dust accumulation area and the migration path of conductive dust in the obtained resistivity spatial distribution heat map, it drives the lifting device to adjust the spacing of the parallel metal plates and match the voltage output value. The optimization module is used to obtain feedback data on the adjustment range of the spacing between parallel metal plates and the rate of change of the voltage output value, and to update the screening strategy of key measurement points based on the feedback data to iteratively optimize the acquisition accuracy of the impedance phase difference spectrum, thereby realizing multimodal dust adaptive adsorption control.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the intelligent dust collector control method based on electrostatic adsorption as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a smart dust collector control method based on electrostatic adsorption as described in any one of claims 1 to 7.

Images