Real-time monitoring method and system for dust deposition of plasma air purifier

Abstract

The invention provides a real-time monitoring method and system for dust deposition of a plasma air purifier. The light gathering device is installed on the windward side of the filter screen to compress dust density and enhance light scattering, and the superposition photoelectric converter converts and amplifies current signals; in the plasma ionization process, charge distribution is controlled, and a current signal and dust movement data are synchronously collected to form a composite signal; separating the composite signal into a surface dust light component and a deep dust charge component based on a gradient fiber layer structure; performing depth weighting calculation on the components through airflow path characteristics to generate a dust retention distribution diagram containing position information; and establishing a compensation mechanism according to the dynamic trend of the distribution diagram, fusing surface and deep dust changes to output deposition coefficients of all layers, and realizing layered real-time quantitative evaluation of the blockage state. The real-time dynamic accurate quantification of the dust deposition position and the blocking degree from the surface layer to the deep layer of the plasma purifier filter screen is realized.

Description

Technical Field

[0001] The present application relates to the technical field of air purification equipment monitoring, and in particular to a method and system for real-time monitoring of dust deposition in a plasma air purifier. Background Art

[0002] When a plasma air purifier is operating, the charge generated by the ionization process causes dust particles to form a layered deposit structure in the filter, with both surface adhesion and deeper embedment. Traditional monitoring methods struggle to distinguish between these two layers, making it impossible to accurately quantify the extent of deep blockage (deep blockage exceeding 30% affects purification efficiency). A technology that can identify dust deposition locations and the extent of layered blockage in real time is urgently needed to address the secondary pollution problem caused by delayed maintenance.

[0003] The current mainstream solution uses a dual-path scattering contrast method combined with airflow pressure differential sensing: a laser transmitter is set on the windward side of the filter, and a photoelectric receiver is set on the leeward side. The total dust deposition amount is calculated by detecting the intensity difference between the incident light and the transmitted light; at the same time, a pressure differential sensor is installed to monitor the air pressure changes on both sides of the filter, and the total dust amount and pressure differential data are input into a preset linear fitting model to estimate the distribution of the filter blockage area; when the model output value exceeds the set threshold, a cleaning alarm signal is triggered.

[0004] Existing solutions have essential limitations: position differentiation fails because the optical signal of deeply embedded dust is blocked by surface dust and has a high attenuation rate. Only surface-attached dust is effectively detected, and the rate of missed detection of deep deposition is high; dynamic monitoring is distorted, and the charge disturbance generated by plasma ionization triggers dust migration, resulting in nonlinear deviations between the pressure difference change and the total amount of dust, and the real-time monitoring error fluctuates widely; early warning delays are out of control, and the threshold model relies on static historical data, which cannot perceive the dynamic diffusion process of deep deposition into the fiber structure. Maintenance delays cause the purification efficiency to decay faster. Summary of the Invention

[0005] The present application provides a real-time monitoring method and system for dust deposition in a plasma air purifier, which is used to solve the problems of layered deposition positioning failure, dynamic monitoring distortion and maintenance response lag in the prior art.

[0006] In a first aspect, the present application provides a method for real-time monitoring of dust deposition in a plasma air purifier, comprising: A spatial compression light concentrator is installed on the windward side of the filter of the plasma air purifier, which physically compresses the distribution density of dust particles to enhance the intensity of the light scattering signal, and at the same time, a photoelectric converter is superimposed to convert the scattered light signal into an amplified current signal; Performing spatial charge distribution control during the continuous ionization process of the plasma, synchronously collecting the amplified current signal and the dust movement data generated by the charge disturbance, to form a composite signal with spatiotemporal coupling characteristics; Based on the gradient fiber layer structure of the filter, the composite signal is separated into an optical signal component corresponding to the distribution of surface retained dust and a charge disturbance component corresponding to the distribution of deeply embedded dust; Inputting the optical signal component and the charge disturbance component into a preset hierarchical monitoring model, performing a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter, and generating a dust retention distribution map containing dust location information; An adaptive attenuation compensation mechanism is established based on the dynamic diffusion trend of the dust retention distribution diagram. By fusing the changes in the current surface retained dust distribution and the current deeply embedded dust distribution, the deposition quantification coefficients of different layers of the filter blockage structure are output. Based on the deposition quantification coefficients, a hierarchical real-time quantitative evaluation of the filter blockage status during operation is performed.

[0007] Optionally, based on the gradient fiber layer structure of the filter, the composite signal is separated into an optical signal component corresponding to the surface retained dust distribution and a charge disturbance component corresponding to the deep embedded dust distribution, including: Based on the gradient fiber layer structure of the filter, the surface layer region is defined as the windward side of the filter with dense fiber distribution, and the deep layer region is defined as the leeward side with loose fiber distribution, and the density reference list is pre-calibrated; Perform signal response feature segmentation on the composite signal and extract the intensity value and duration of each data point; Based on the density reference list and the preset threshold standard, classifying as a light signal component when the intensity value exceeds the threshold and the duration is less than the specified value, and classifying as a charge disturbance component when the intensity value is lower than the threshold but the duration exceeds the specified value; The optical signal component is bound to the distribution position of retained dust in the surface layer area to generate an optical signal component data string; the charge disturbance component is bound to the distribution position of embedded dust in the deep layer area to generate a charge disturbance component data string.

[0008] Optionally, spatial charge distribution control is performed during the continuous ionization process of the plasma, and the amplified current signal and dust movement data generated by the charge disturbance are synchronously collected to form a composite signal with spatiotemporal coupling characteristics, including: During the continuous ionization process of the plasma, the voltage intensity and time interval of the electrode array are adjusted to divide the low charge density area and the high charge density area, and the area boundaries are set based on a preset control parameter list; When the charge distribution control is activated, the amplified current signal and dust movement data are collected simultaneously, and each data point is marked with the same time stamp and regional location label; Performing time and space data superposition processing on the collected data to extract the intensity value of the amplified current signal and the displacement value of the dust movement data; The difference ratio between the intensity value and the displacement value is calculated, and a weight ratio is set according to the charge density area. The intensity value and the displacement value are combined to output a composite signal data block with a time mark and a position tag.

[0009] Optionally, performing a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter to generate a dust retention distribution map containing dust position information includes: According to the direction of the airflow path of the filter from the windward side to the leeward side, the shallow area and deep area are divided to form a depth level reference map; Obtaining the optical signal component corresponding to the shallow area position and the charge disturbance component corresponding to the deep area position and the corresponding position label; Setting a weight ratio between shallow and deep areas based on the depth level reference map, and applying the weight ratio to the values ​​of the light signal component and the charge disturbance component to calculate a weighted dust value; A two-dimensional grid map is constructed, where grid rows represent time points and grid columns represent location labels. The weighted dust value is filled in each grid cell. A distribution map is drawn by color coding in combination with the two-dimensional grid map, and a dust retention distribution map is output.

[0010] Optionally, an adaptive attenuation compensation mechanism is established based on the dynamic diffusion trend of the dust retention distribution map, and by fusing the changes in the current surface retained dust distribution and the current deep embedded dust distribution, the deposition quantification coefficients of different layers of the filter blockage structure are output, including: Extracting a shallow region sequence corresponding to the surface retained dust distribution and a deep region sequence corresponding to the deep embedded dust distribution from the dust retention distribution map, and calculating the change rate of adjacent time points as the diffusion trend of the dust retention distribution map; Setting a change rate threshold and a compensation factor table, and adjusting a corresponding compensation factor value in the compensation factor table based on the change rate threshold; Applying the compensation factor to the shallow region sequence and the deep region sequence, and outputting a compensated shallow region value and a compensated deep region value; The compensated shallow area values ​​and the compensated deep area values ​​are fused, and the average value of the compensated shallow area values ​​is output as the shallow area deposition coefficient, and the average value of the compensated deep area values ​​is output as the deep area deposition coefficient.

[0011] Optionally, physically compressing the dust particle distribution density by the light concentrator to enhance the intensity of the light scattering signal, and simultaneously superimposing a photoelectric converter to convert the scattered light signal into an amplified current signal, comprises: A light concentrator device including a lens group and a reflector is installed on the windward side of the filter; Adjusting the distance between the lens groups in the light concentrator to compress the distribution density of dust particles, thereby enhancing the intensity of the scattered light signal and outputting an enhanced scattered light signal; At the same time, the scattered light signal is converted into an initial current value through a photoelectric converter, and the initial current value is amplified by a preset amplification factor to output an amplified current signal.

[0012] Optionally, calculating the difference ratio between the intensity value and the displacement value, setting a weight ratio according to the charge density area, combining the intensity value and the displacement value, and outputting a composite signal data block with a time stamp and a position tag includes: Extracting the corresponding intensity value, displacement value, time stamp and position tag for each data point, and calculating the absolute difference between the intensity value and the displacement value; Taking the larger value of the intensity value and the displacement value, and when the larger value is greater than zero, selecting the corresponding preset calculation rule to calculate the difference ratio; Selecting a preset weight value according to the charge density area corresponding to the position tag, calculating the intensity value, the weight value, and the displacement value according to a corresponding preset calculation rule, and outputting a combined value; The combined value, the time mark, the position tag and the difference ratio are combined into a data unit, and all the data units are integrated and sorted in time to form a composite signal data block.

[0013] In a second aspect, the present application provides a real-time monitoring system for dust deposition in a plasma air purifier, comprising: A conversion module is used to install a spatial compression light concentrator on the windward side of the filter of the plasma air purifier, physically compress the distribution density of dust particles through the light concentrator to enhance the intensity of the light scattering signal, and simultaneously superimpose a photoelectric converter to convert the scattered light signal into an amplified current signal; a generation module for performing spatial charge distribution control during the continuous ionization process of the plasma, synchronously collecting the amplified current signal and the dust movement data generated by the charge disturbance, and forming a composite signal with spatiotemporal coupling characteristics; a separation module for separating the composite signal into an optical signal component corresponding to the surface retained dust distribution and a charge disturbance component corresponding to the deep embedded dust distribution based on the gradient fiber layer structure of the filter; a calculation module, configured to input the optical signal component and the charge disturbance component into a preset hierarchical monitoring model, perform a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter, and generate a dust retention distribution map including dust location information; A fusion module is used to establish an adaptive attenuation compensation mechanism based on the dynamic diffusion trend of the dust retention distribution map. By fusing the changes in the current surface retained dust distribution and the current deep embedded dust distribution, the deposition quantification coefficients of different layers of the filter blockage structure are output, and a hierarchical real-time quantitative evaluation of the filter blockage status during operation is performed based on the deposition quantification coefficients.

[0014] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a real-time monitoring method for dust deposition in a plasma air purifier as described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements the real-time monitoring method for dust deposition of a plasma air purifier as described in the first aspect.

[0016] This application enhances the light scattering intensity by compressing the dust density and converting it into an electrical signal, significantly improving the ability to capture micro-dust signals; synchronously controls the charge distribution and collects multi-source data during the ionization process, effectively overcoming dynamic disturbance interference; separates the surface transient light signal and the deep long-term charge signal based on the filter fiber density gradient, completely solving the problem of deep dust identification; performs depth-weighted calculation of the stratified signal in combination with the airflow path to accurately map the spatial position of dust deposition; establishes a compensation mechanism based on the dynamic trend of the retention distribution and the deep changes in the table, predicts the blockage diffusion in advance and outputs the stratification quantification coefficient, and finally realizes the real-time quantitative evaluation of the blockage status of the plasma purifier filter from the surface to the deep layer.

[0017] Furthermore, by identifying the high-density fiber area on the windward side of the filter as the surface layer and the low-density fiber area on the leeward side as the deep layer, a density reference list is pre-constructed; the intensity value and duration characteristics of each data point in the composite signal are extracted, and the high-intensity short-term signal is classified as the optical signal component and bound to the surface layer position according to the preset threshold standard, and the low-intensity long-term signal is classified as the charge disturbance component and bound to the deep layer position; finally, a data string of the optical signal component corresponding to the dust retained on the surface and a data string of the charge disturbance component corresponding to the dust embedded in the deep layer are generated. This breaks through the limitation of existing technologies that cannot distinguish between dust deposition layers: based on the fiber density gradient, the surface transient signal and the deep long-term disturbance are accurately separated. By strictly binding the signal component to the physical position, the problem of missed detection caused by attenuation of the deep signal is eliminated. For the first time, the dust deposition at different depths is independently quantified, significantly improving the accuracy of deposition positioning and the reliability of layer identification.

[0018] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A flow chart showing a method for real-time monitoring of dust deposition in a plasma air purifier provided by the present application is shown; Figure 2 A scene diagram showing a method for real-time monitoring of dust deposition in a plasma air purifier provided by the present application is shown; Figure 3 A schematic diagram of the structure of a real-time monitoring system for dust deposition in a plasma air purifier provided by the present application is shown; Figure 4 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0022] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0023] Current dust deposition monitoring technology for plasma air purifiers has fundamental limitations. The mainstream solution relies on a combination of dual-path scattering contrast and differential pressure sensing. Its detection mechanism can only obtain information on the total amount of dust and cannot distinguish between surface attachment and deeply embedded layered deposition structures. The light scattering signal of deep dust is severely obscured by surface deposition, resulting in long-term missed detection of dust embedded in the fiber. The charge disturbance generated by the ionization process causes continuous migration of dust, resulting in a dynamic mismatch between pressure difference changes and the total amount of deposition, and serious distortion of real-time monitoring data. More importantly, the static threshold alarm model cannot capture the diffusion trend of deep deposition into the interior of the fiber structure, and maintenance operations are seriously delayed, which accelerates the decline of purification efficiency and triggers the risk of secondary pollution.

[0024] In order to overcome the above limitations, this application proposes a layered dynamic monitoring method based on light-charge synergy. By deploying a spatial compression light concentrator on the windward side of the filter, the dust scattering signal intensity is enhanced and converted into an amplified current signal; spatial charge distribution control is performed synchronously during the plasma ionization process to construct a spatiotemporal coupled composite signal of the current signal and the dust movement data; based on the structural characteristics of the gradient fiber layer of the filter, the composite signal is accurately separated into the surface retained dust light component and the deep embedded dust charge component; the components are weighted by the deposition depth in combination with the airflow path characteristics to generate a dust retention distribution map containing position information; finally, an adaptive attenuation compensation mechanism driven by dynamic diffusion trend is established, and the surface and deep deposition changes are integrated to output the layered deposition quantification coefficient to realize the layered real-time assessment of the blockage state. This solution fundamentally solves the problems of unrecognizable deposition layers, dynamic interference distortion and rigid early warning mechanism, and for the first time forms a real-time dynamic assessment system for dust deposition covering the surface and inner structures of the filter.

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0026] Figure 1 The present invention provides a flow chart of a method for real-time monitoring of dust deposition in a plasma air purifier, as shown in FIG. Figure 1 As shown, the method includes: 101. Install a spatial compression light concentrator on the windward side of the filter of the plasma air purifier, physically compress the distribution density of dust particles to enhance the intensity of the light scattering signal, and simultaneously superimpose a photoelectric converter to convert the scattered light signal into an amplified current signal;

[0027] Optionally, step 101 may specifically include the following steps: 1011. Install a light concentrator device including a lens group and a reflector on the windward side of the filter; 1012. Adjust the distance between the lens groups in the light concentrator to compress the distribution density of dust particles, enhance the intensity of the scattered light signal, and output an enhanced scattered light signal; 1013. Simultaneously, convert the scattered light signal into an initial current value through a photoelectric converter, amplify the initial current value by a preset amplification factor, and output an amplified current signal.

[0028] In the above scheme, the windward side of the filter is the filter surface in the air purifier that is in direct contact with the polluted airflow; the light concentrator is composed of a lens group and a reflector, the lens group is a magnifying glass array composed of multiple convex lenses, and the reflector is a mirror metal sheet with reflective function; the dust particle distribution density compression refers to increasing the visual concentration of dust in a unit observation area by reducing the spacing between the lens groups, similar to the focusing effect of a telescope; the scattered light signal is the visible light reflected by the dust particles to the surrounding areas after being illuminated, and its intensity increases with the increase of dust density; the photoelectric converter refers to a photosensitive element that can convert the intensity of light into the size of the current, and its working principle is similar to that of a solar panel; the initial current value is the weak current signal directly output by the photoelectric converter, and the typical value does not exceed 0.1 mA; the preset amplification factor refers to the signal amplification degree pre-set in the circuit, and the current is multiplied by the operational amplifier chip.

[0029] In an embodiment of the present application, first, through 1011, a light concentrator device is installed on the side of the front air flow of the filter of the plasma air purifier. The device is composed of a lens group composed of a plurality of lenses and a reflective sheet. The lens group is used to collect light, and the reflective sheet is used to reflect light onto the lens to improve the light collection effect. During installation, this device is attached to the inlet of the filter through a fixed bracket to ensure that the lens faces the direction of air inflow, thereby effectively capturing the light scattered by dust particles in the air. For example, in a standard air purifier model, a device consisting of two convex lenses and a flat reflective sheet is installed. The convex lenses are 5 cm in diameter and arranged in parallel on the bracket. The reflective sheet is placed at a 45-degree angle at the rear, so that when air flows through, the light reflected by the dust particles is concentrated and collected by the lens group. Secondly, through 1012, the lens group installed in 1011 is used to adjust the distance between the lenses to change the light focusing effect. The adjustment process involves manually or automatically narrowing or widening the lens spacing by rotating the adjustment knob on the bracket or using a micromotor. As the distance decreases, the light becomes more concentrated and the focal area narrows. This increases the number and density of dust particles within the focal area, resulting in a more densely distributed, more intense scattered light signal. For example, under test conditions, with an initial lens spacing of 20 mm and a scattered light signal intensity of 100 units, reducing the spacing to 10 mm using the adjustment knob increases the perceived density of dust particles at the focal point, causing the scattered light signal intensity to rise to 200 units. This increased intensity is measured using a light intensity meter. Then, through 1013, a photoelectric conversion element such as a photosensitive diode is used to convert the enhanced scattered light signal output by 102 into an initial current value. When the photosensitive diode is exposed to light, a small current is generated. Then, a preset amplification factor, such as 10 times, is applied to this initial current through an amplification circuit such as an operational amplifier to amplify the output amplified current signal. The amplification process uses the formula I_out=G×I_in, where I_out represents the output current unit in milliamperes, G represents the amplification factor, and I_in represents the input current unit in milliamperes. For example, when the enhanced light signal intensity is 200 units, the corresponding photosensitive diode output current I_in=0.2 mA. According to the amplification factor G=10, I_out=10×0.2=2.0 mA is calculated, and the output is 2.0 mA. The amplified current signal is used for subsequent processing.

[0030] In actual applications, during the testing of the plasma air purifier developed by Company A, technician C first installed a light concentrator device consisting of a lens group and a reflector on the windward side of the filter to ensure that the components were precisely aligned in the high-dust simulation environment of facility B. The distance between the lens groups was then dynamically adjusted according to the real-time dust concentration, significantly enhancing the intensity of the scattered light signal by compressing the distribution density of dust particles. Finally, the enhanced light signal was connected to a photoelectric converter, where it was converted into an initial current value by a photodiode. After being processed with a preset amplification factor, a high-sensitivity amplified current signal was output. The entire process was stably executed within Company A's purifier control system.

[0031] Through the installation of a light concentrator and dynamic adjustment of the lens spacing, this solution physically compresses the distribution density of dust particles, significantly enhancing the intensity of the scattered light signal. By superimposing the photoelectric conversion and signal amplification functions, the scattered light is efficiently converted into a highly sensitive amplified current signal, comprehensively improving the accuracy of dust detection, the system response speed, and the operational reliability of the purifier in complex environments.

[0032] 102. Perform spatial charge distribution control during the continuous ionization process of the plasma, synchronously collect the amplified current signal and the dust movement data generated by the charge disturbance, and form a composite signal with spatiotemporal coupling characteristics; Optionally, step 102 may specifically include the following steps: 1021. During the continuous ionization process of the plasma, adjust the voltage intensity and time interval of the electrode array to divide the low charge density area and the high charge density area, and set the area boundaries based on the preset control parameter list; 1022. When the charge distribution control is started, the amplified current signal and dust movement data are collected simultaneously, and each data point is marked with the same time stamp and regional location label; 1023. Perform time and space data superposition processing on the collected data to extract the intensity value of the amplified current signal and the displacement value of the dust movement data; 1024. Calculate the difference ratio between the intensity value and the displacement value, set a weight ratio according to the charge density area, combine the intensity value and the displacement value, and output a composite signal data block with a time stamp and a position tag.

[0033] In the above scheme, continuous plasma ionization refers to the process of converting air molecules into charged ions through continuous discharge of electrodes. Spatial charge distribution control refers to the process of dividing the ionization zone into a low charge density region with sparse charged particles and a high charge density region with dense charged particles by adjusting the voltage intensity and time interval of the electrode array. Dust motion data is information on the movement trajectory of charged dust particles under the action of charge forces. Composite signals are combined data that contain both time correlation and spatial position correlation. The preset control parameter list is a configuration table of predefined voltage values, time interval parameters, and region boundary coordinates. Time tags are timestamps corresponding to the data collection points. Region location tags indicate the location of the ionization zone where the data originated. Temporal data overlay is the alignment of data at different time points along the timeline. Spatial data overlay is the alignment of data at different spatial locations along the coordinate grid. The intensity value reflects the strength of the amplified current signal. The displacement value indicates the amount of positional movement of dust particles per unit time. The difference ratio is the numerical ratio between the intensity value and the displacement value. The weight ratio is the calculation coefficient assigned to different charge density regions. Composite signal data blocks are structured data units that integrate time tags, position tags, intensity values, and displacement values.

[0034] In this embodiment of the present application, first, at step 1021, while the air purifier continuously performs a discharge process (plasma ionization), the space is divided into two regions of different charge intensities: a high charge intensity region and a low charge intensity region by adjusting the voltage level and discharge interval of the metal electrode group. Specifically, the region boundaries are automatically set according to a preset control parameter table, which contains voltage range values, discharge intervals, and specific region coordinate location information. For example, within a 30 cm x 30 cm square space, the parameter table sets the lower left corner as a high charge region, with a voltage of 15,000 volts and a discharge interval of 0.1 seconds, and the upper right corner as a low charge region, with a voltage of 8,000 volts and a discharge interval of 0.5 seconds. The boundary locations are determined by coordinates 15,15 to 30,30. Secondly, at step 1022, upon initiating charge distribution control, two sets of data are immediately and simultaneously collected: an amplified current signal from the current signal output in step 1013, and dust movement data obtained by capturing dust particle movement images with a high-speed camera and analyzing their trajectories. Each collected data point is annotated with the same time information accurate to the millisecond level and the charge intensity region location at that point. For example, at 10:00:01:500, a current value of 1.8 mA was detected in the high-charge region, and the camera captured a dust particle moving 120 microns. Both sets of data are labeled as the high-charge region at time 10000-1500. Then, at step 1023, the labeled data collected in step 1022 are aligned according to the same time stamp and regional features are matched based on the position tags. Two key values ​​are extracted from the matched data: the intensity value of the amplified current signal in milliamperes and the displacement value of the dust movement data in microns. The displacement value is calculated by comparing the change in the position coordinates of the dust particle at adjacent time points. For example, the data block with the time stamp 10000-1500 contains a current intensity of 1.8 mA in the high-charge region and a displacement of 120 microns, and a current intensity of 0.9 mA in the low-charge region and a displacement of 250 microns. These paired data are directly fed into the next processing step. Finally, through step 1024, the difference ratio between the current intensity value and the displacement value is calculated using the formula: the ratio is equal to |intensity value minus displacement value| divided by (intensity value plus displacement value), where the symbol || represents absolute value and the symbol "division" represents division. Weights are then assigned based on the charge density region type: 1.8 for high-charge regions and 1.2 for low-charge regions, determined by the partition information from step 1021. Finally, the composite signal is output using the formula: the composite signal value is equal to the displacement value multiplied by 1 minus the ratio, plus the intensity value multiplied by the weight ratio. The output data block contains a time stamp, position label, and composite signal value.For example, the data intensity of the high charge area is 1.8 mA and the displacement is 120 microns: the difference ratio calculation is: |1.8-120| divided by (1.8+120) equals 118.2 divided by 121.8 equals approximately 0.97; weight coefficient: 1.8 high charge area; composite signal value: 120 multiplied by 1-0.97 plus 1.8 multiplied by 1.8 equals 120 multiplied by 0.03 plus 3.24 equals 3.6 plus 3.24 equals 6.84; output data block example: the high charge area value 6.84 at time 100001500 is used for subsequent processing.

[0035] In actual application, during the operation of Company A's plasma purification system, technician C first adjusted the voltage intensity and time interval of the electrode array according to a preset parameter list, divided the ionization zone into low / high charge density areas and stabilized the boundaries; then, when starting charge control, the amplified current signal and dust movement data were synchronously collected and amplified, and a unified timestamp and partition location label were annotated for each data point; the labeled data was then processed in time and space through the analysis system D, and the current intensity value and dust displacement value were extracted; finally, the difference ratio between the intensity and displacement was calculated, and different weight ratios were assigned according to the charge density area. The composite signal data block with time and space labels was generated by fusion, and the entire process was verified in the test facility B.

[0036] This solution accurately establishes the coupling relationship between dust movement and current signals by dynamically dividing the charge density zone and synchronously collecting spatiotemporal data. The spatiotemporal label and weight fusion processing significantly enhances the composite signal's ability to analyze particle trajectories and electric field strength, providing a highly reliable comprehensive basis for the purifier's real-time control decisions in complex dust environments, effectively improving system adaptability.

[0037] 103. Based on the gradient fiber layer structure of the filter, the composite signal is separated into an optical signal component corresponding to the surface retained dust distribution and a charge disturbance component corresponding to the deep embedded dust distribution; Optionally, step 103 may specifically include the following steps: 1031. Based on the gradient fiber layer structure of the filter, identify the surface layer region where the windward side has dense fiber distribution and the deep layer region where the leeward side has loose fiber distribution, and pre-calibrate to form a density reference list; 1032. Perform signal response feature segmentation on the composite signal to extract the intensity value and duration of each data point; 1033. Based on the density reference list and the preset threshold standard, classify as a light signal component when the intensity value exceeds the threshold and the duration is less than the specified value; classify as a charge disturbance component when the intensity value is lower than the threshold but the duration exceeds the specified value; 1034. Bind the optical signal component to the retained dust distribution position in the surface layer area to generate an optical signal component data string; bind the charge disturbance component to the embedded dust distribution position in the deep layer area to generate a charge disturbance component data string.

[0038] Among them, step 1034 may specifically include the following processes: extracting the corresponding intensity value, displacement value, time stamp and position label for each data point, and calculating the absolute difference between the intensity value and the displacement value; taking the larger value of the intensity value and the displacement value, when the larger value is greater than zero, selecting the corresponding preset calculation rule to calculate the difference ratio; selecting a preset weight value according to the charge density area corresponding to the position label, calculating the intensity value, the weight value and the displacement value according to the corresponding preset calculation rule, and outputting a combined value; combining the combined value, the time stamp, the position label and the difference ratio into a data unit, integrating all data units and sorting them in time to form a composite signal data block.

[0039] In the above scheme, the gradient fiber layer structure refers to a layered design in which the fiber density in the filter gradually decreases from the windward side to the leeward side; the surface layer area is the dust retention area formed by the dense arrangement of fibers on the windward side of the filter; the deep area is the dust embedding area formed by the loose fibers on the leeward side of the filter; the density reference list is a pre-established correspondence table between the fiber layer density and the spatial position; the signal response feature division refers to the operation of classification according to the numerical characteristics of the signal; the intensity value represents the value of the current or light intensity in the composite signal; the duration refers to the length of time that the signal intensity remains higher than the baseline value; the preset threshold standard is an artificially set signal classification critical value; the optical signal component is a signal type that meets the high-intensity short-duration characteristics, corresponding to the surface dust Dust distribution; the charge disturbance component is a signal type that conforms to the low-intensity and long-duration characteristics, corresponding to deep dust distribution; the optical signal component data string is an optical signal data set bound to the surface layer position; the charge disturbance component data string is a charge signal data set bound to the deep position; the absolute difference is the absolute value after subtracting the intensity value from the displacement value; the difference ratio is the calculation result of the ratio of the intensity value to the displacement value; the preset weight value is the weighting coefficient assigned according to the importance of the charge density area; the combined value is the composite data after the intensity value and the displacement value are weighted; the data unit is an independent data packet containing the combined value, time stamp, position tag and difference ratio; the composite signal data block is a complete signal set that integrates all data units in chronological order.

[0040] In an embodiment of the present application, first, through 1031, according to the structural characteristics of the gradient fiber layer of the filter, the dense fiber part close to the air inflow direction is marked as the surface layer area, and the loose fiber part away from the air inflow direction is marked as the deep layer area. In the specific implementation, the fiber number density at different thickness positions is measured by a microscope, and a regional division reference table is made and the coordinate range is marked. For example, in a rectangular filter with a total thickness of 3 mm, the fiber density at the first 1 mm reaches 450 fibers per square millimeter, which is divided into the surface layer area, and the fiber density at the last 2 mm is only 180 fibers per square millimeter, which is divided into the deep layer area. The reference table clearly records the coordinate position 0-1 mm corresponding to the surface layer area and 1-3 mm corresponding to the deep layer area. Secondly, through 1032, the composite signal data transmitted in step 102 are feature extracted one by one, the composite signal value is directly used as the intensity value, and the signal duration is determined by calculating the time mark difference of consecutive data points at the same position. For example, the two high-charge area data points at time marks 100001500 and 100001700 have intensities of 6.84 and 6.82 respectively. The average intensity during the 200-millisecond time interval is 6.83. This value and the duration together constitute the characteristic parameters of the signal point. Then, signal classification is performed through 1033, combined with the density reference list and the preset threshold standard. The intensity threshold of 4.0 and the duration threshold of 150 milliseconds are set as the judgment criteria: when the signal intensity is ≥4.0 and the duration is <150 milliseconds, it is classified as an optical signal component; when the signal intensity is <4.0 but the duration is ≥150 milliseconds, it is classified as a charge disturbance component. For example, a signal point with an actual intensity of 6.83 and a duration of 200 milliseconds exceeds the duration threshold, but is still classified as an optical signal component because the intensity significantly meets the standard; while another set of data with an intensity of 3.5 and a duration of 300 milliseconds is classified as a charge disturbance component because it meets the long-term low-intensity feature. Finally, through 1034, the light signal component is bound to the distribution position of the retained dust in the surface layer area: the original intensity value, displacement value, time mark and position label information are extracted from the data point; the absolute difference between the intensity value and the displacement value is calculated; the larger value of the two is selected as the benchmark; when the benchmark value is greater than 0, the difference ratio is calculated to be equal to the absolute difference divided by the sum of the two values; the weight coefficient is selected according to the position label, taking 1.8 for the high charge area and 1.2 for the low charge area; the output value is calculated by the formula combination value = displacement value × (1-ratio) + intensity value × weight; finally, the calculated value, time mark, position label and ratio value are integrated as data units, and connected in chronological order to form a signal component data string. Specifically, when processing the data intensity of a light signal component in a high-charge area of ​​1.8 mA and a displacement of 120 microns: first calculate the absolute difference 118.2, select the reference value 120, and obtain the difference ratio 0.97. Apply the weight 1.8 to calculate the combination value 120×0.03+1.8×1.8=6.84, and generate a data unit containing the time mark 100001500, the surface layer position label, the combination value 6.84 and the ratio value 0.97. The unit is automatically sorted in the data string according to the timestamp.

[0041] In actual applications, during the test of Company A's filtration system, Technician C first identified the dense areas of the surface layer and the loose areas in the deep layer based on the gradient fiber layer structure of the filter mesh and generated a density reference list; then, the intensity value and duration of each data point of the composite signal were extracted, and when the intensity exceeded the preset threshold and the duration was short, it was classified as an optical signal component; when the intensity was low but the duration was long, it was classified as a charge disturbance component; finally, the optical signal component was bound to the surface retained dust distribution to generate a data string, and the charge disturbance component was bound to the deeply embedded dust to generate a data string. At the same time, the absolute difference between the intensity and displacement was calculated and the larger value was taken, and the data units with time tags, position tags and difference ratios were output according to the partition weights. The entire process was completed in real time on the verification platform B.

[0042] This solution uses a gradient fiber layer structure to accurately distinguish the physical properties of surface-retained dust and deeply embedded dust, and combines it with a dual-threshold judgment mechanism of intensity and duration to achieve reliable separation of optical signal components and charge disturbance components; spatiotemporal label binding and weight fusion calculations synchronously associate the dust distribution level and movement state, and the output data comprehensively characterizes the pollution characteristics of different areas of the filter, providing a hierarchical decision-making basis for the dynamic adjustment of the purifier, and systematically improving the filtration accuracy and deep cleaning efficiency.

[0043] 104. Input the optical signal component and the charge disturbance component into a preset hierarchical monitoring model, perform a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter, and generate a dust retention distribution map including dust location information; Optionally, step 104 may specifically include the following steps: 1041. Based on the airflow path of the filter from the windward side to the leeward side, shallow area positions and deep area positions are divided to form a depth level reference map; 1042. Obtain an optical signal component corresponding to a shallow position, a charge disturbance component corresponding to a deep position, and corresponding position labels; 1043. Setting a weight ratio of shallow area positions and deep area positions based on the depth level reference map, and applying the weight ratio to the values ​​of the optical signal component and the charge disturbance component to calculate a weighted dust value; 1044. Construct a two-dimensional grid map, where grid rows represent time points and grid columns represent location labels. Fill each grid cell with the weighted dust value. Draw a distribution map using color coding in combination with the two-dimensional grid map, and output a dust retention distribution map.

[0044] In the above scheme, the airflow path characteristics refer to the directional flow pattern of air flowing from the windward side of the filter to the leeward side; the shallow area position is the fiber layer area close to the windward side; the deep area position is the fiber layer area close to the leeward side; the depth level reference map is a depth level mapping map of the filter position divided according to the airflow path; the position label is the spatial coordinate identifier of the signal source area; the weight ratio is the calculation coefficient assigned to different depth areas, the shallow area position has a lower weight and the deep area position has a higher weight; the weighted dust value is the result of multiplying the original value of the light signal component or the charge disturbance component by the corresponding area weight; the two-dimensional grid map is a matrix coordinate map formed by time points as rows and position labels as columns; the grid unit is an independent square formed by the intersection of rows and columns in the two-dimensional grid map; the color coding method is a visual rule that uses different shades of color to represent the size of the dust value; the dust retention distribution map is a color space distribution map reflecting the degree of dust deposition at different positions of the filter.

[0045] In this embodiment of the present application, first, through 1041, based on the directional characteristics of air flow in the plasma air purifier filter, the area close to the air inlet surface is defined as a shallow area, and the area away from the air inlet surface is defined as a deep area. In specific implementation, the areas are divided by measuring the thickness ratio of the filter. For example, in a filter with a total thickness of 10 cm, the first 3 cm range is marked as the shallow area and the last 7 cm range is marked as the deep area. A depth level reference map is formed and the coordinate scale is marked. The map clearly marks, for example, the position coordinates 0-3 cm are shallow areas and 3-10 cm are deep areas. Secondly, through 1042, two types of signal component data processed in the previous steps are obtained: optical signal component data belonging to the shallow area and charge disturbance component data belonging to the deep area, and the position tags and time tags carried by these data are extracted. For example, from the data string output by step 1034, the optical signal component value 6.84 at the shallow area position tag at time 100001500 and the charge disturbance component value 2.83 at the deep area position tag at time 100001505 are captured. Then, at step 1043, based on the regional division of the depth level reference map, weights are set: shallow areas receive a weight of 0.8, while deep areas receive a weight of 1.5. A dual-weight calculation process is performed on the two signal component values, including position-area weighting and component-type weighting. The optical signal component receives an additional weighting factor of 0.8, and the charge disturbance component receives an additional weighting factor of 1.2. The final weighted dust value is calculated as follows: Weighted Value = Original Signal Value × Position-Area Weight × Component-Type Weight. For example, for a shallow optical signal component value of 6.84, first multiply it by the position weight of 0.8 to obtain 5.47, then multiply it by the component weight of 0.8 to obtain a final weight of 4.38. For a deep charge disturbance component value of 2.83, first multiply it by the position weight of 1.5 to obtain 4.25, then multiply it by the component weight of 1.2 to obtain a final weight of 5.10. Finally, at step 1044, a two-dimensional grid structure is constructed: the horizontal grid represents the time axis, with each 5-millisecond time point, and the vertical grid represents the location labels, divided into 10 equal columns according to the filter coordinates. Each grid cell is filled with a weighted dust value corresponding to the time and location. The values ​​are color-coded, for example, values ​​0-3 are light blue, 4-6 are yellow, and 7-10 are red. This ultimately generates a dust retention distribution map. For example, at time 10000-1500, the first row of grid cells in the shallow area is filled with 4.38, resulting in a yellow block. At time 10000-1505, the fifth row of grid cells in the deep area is filled with 5.10, resulting in a yellow block. The distribution of color blocks formed at consecutive time points intuitively illustrates dust accumulation.

[0046] In actual applications, during the test of Company A's filtration system, technician C first divided the filter into shallow and deep areas from the windward side to the leeward side according to the airflow path and generated a depth level reference map; then extracted the light signal component corresponding to the shallow area position label and the charge disturbance component corresponding to the deep area position label; based on the depth level, the ratio of low weight for shallow areas and high weight for deep areas was set, and weighted calculation was performed on the two types of signal values ​​to obtain the dust value; finally, a time-position two-dimensional grid map was constructed on the D analysis platform, the weighted values ​​were filled into the grid cells, and a dynamic dust retention distribution map was generated through color coding, which was output in real time on the B verification platform.

[0047] This solution accurately quantifies the dust retention intensity in different areas through a depth-weighted mechanism based on airflow path division. The time-position grid fuses weighted values ​​and uses color coding to intuitively display the instantaneous distribution of dust on the filter surface and its accumulation trend deep inside. This provides the purifier with a dynamic decision-making map that can locate polluted areas and optimize the allocation of cleaning resources.

[0048] 105. An adaptive attenuation compensation mechanism is established based on the dynamic diffusion trend of the dust retention distribution diagram. By fusing the changes in the current surface retained dust distribution and the current deeply embedded dust distribution, the deposition quantification coefficients of different layers of the filter blockage structure are output. Based on the deposition quantification coefficients, a hierarchical real-time quantitative evaluation of the filter blockage status during operation is performed.

[0049] Optionally, step 105 may specifically include the following steps: 1051. Extracting a shallow region sequence corresponding to the surface retained dust distribution and a deep region sequence corresponding to the deep embedded dust distribution from the dust retention distribution map, and calculating the change rate of adjacent time points as the diffusion trend of the dust retention distribution map; 1052. Set a change rate threshold and a compensation factor table, and adjust a corresponding compensation factor value in the compensation factor table based on the change rate threshold; 1053. Apply the compensation factor to the shallow region sequence and the deep region sequence, and output a compensated shallow region value and a compensated deep region value; 1054. The compensated shallow area values ​​and the compensated deep area values ​​are integrated, and the average value of the compensated shallow area values ​​is output as the shallow area deposition coefficient, and the average value of the compensated deep area values ​​is output as the deep area deposition coefficient.

[0050] In the above scheme, the dynamic diffusion trend refers to the directional characteristics of the expansion of the dust distribution range or the change of concentration over time in the dust retention distribution diagram; the adaptive attenuation compensation mechanism refers to the calculation rule that automatically adjusts the compensation strength according to the current dust distribution change rate; the deposition quantification coefficient is a numerical indicator that characterizes the degree of dust deposition in different fiber layers of the filter; the surface retained dust distribution refers to the dust position data accumulated in the shallow area on the windward side of the filter; the deeply embedded dust distribution refers to the dust position data embedded in the deep area on the leeward side of the filter; the shallow area sequence is the time-varying data string representing the surface retained dust distribution in the dust retention distribution diagram; the deep area sequence is the time-varying data string representing the deep embedded dust distribution in the dust retention distribution diagram; the change rate is the adjacent The ratio of increase or decrease in the dust distribution value at a point in time; the change rate threshold is the critical change rate value that triggers the adjustment of the compensation mechanism; the compensation factor table is a comparison table of compensation coefficients corresponding to different pre-stored change rate intervals; the compensation factor is a calculation multiplier used to correct the dust value attenuation error; the compensated shallow area value is the surface retained dust data corrected by the compensation factor; the compensated deep area value is the deep embedded dust data corrected by the compensation factor; the shallow area deposition coefficient is the average calculation result of the compensated shallow area value, which reflects the degree of surface dust deposition; the deep area deposition coefficient is the average calculation result of the compensated deep area value, which reflects the degree of deep dust deposition; layered real-time quantitative evaluation refers to the process of synchronously outputting shallow and deep deposition coefficients to dynamically monitor the blockage status of the filter.

[0051] In the embodiment of the present application, first, two data sequences are extracted from the dust retention distribution map through 1051: the surface dust accumulation data represented by the light-colored area is called the shallow area sequence, and the deep dust accumulation data represented by the dark-colored area is called the deep area sequence. The specific operation is to read the grid unit values ​​in the distribution map in chronological order, and calculate the amplitude of the numerical change between two adjacent time points as the diffusion trend value. The formula is change rate = current value minus previous value divided by previous value multiplied by 100 percent. For example, the shallow area sequence has a value of 4.38 at time 100001500 and a value of 4.45 at time 100001505, then the change rate is equal to 4.45 minus 4.38 divided by 4.38 multiplied by 100 percent, which is equal to 1.6 percent; the deep area sequence changes from 5.10 to 5.18 at the same time point, and the change rate is equal to 5.18 minus 5.10 divided by 5.10 multiplied by 100 percent, which is equal to 1.57 percent. Next, at step 1052, the change rate criterion is set to 0.5 percent, and a compensation factor table is prepared containing adjustment coefficients for different types of change rates: when the change rate is less than or equal to 0.5 percent, a compensation factor of 1.0 is used; when the change rate is greater than 0.5 percent, a compensation factor of 1.2 is used. For example, if the shallow zone change rate of 1.6 percent exceeds 0.5 percent, the shallow zone compensation factor is adjusted to 1.2; a deep zone change rate of 1.57 percent also triggers a compensation factor of 1.2. Then, at step 1053, the compensation factor is applied to the sequence values: the calculation formula is: compensated value = original value multiplied by the compensation factor. The shallow zone and deep zone sequences are processed separately: for example, the original value of 4.45 at time 100001505 in the shallow zone is multiplied by the compensation factor of 1.2 to obtain 5.34; the original value of 5.18 at the same time in the deep zone is multiplied by the compensation factor of 1.2 to obtain 6.22. After compensation, the values ​​at all time points form two new sequences: the compensated shallow zone sequence and the compensated deep zone sequence. Finally, through 1054, the two compensation sequences are combined: the shallow-zone sedimentation coefficient is calculated as the average of all values ​​in the compensated shallow-zone sequence, and the deep-zone sedimentation coefficient is calculated as the average of all values ​​in the compensated deep-zone sequence. For example, if the compensated shallow-zone values ​​at five time points are 5.34, 5.41, 5.28, 5.37, and 5.31, the average value is 5.34 + 5.41 + 5.28 + 5.37 + 5.31 divided by 5, which equals 5.342. The deep-zone sedimentation coefficient is 6.22, 6.30, 6.18, 6.25, and 6.20, and the average value is 6.22 + 6.30 + 6.18 + 6.25 + 6.20 divided by 5, which equals 6.23. The final output is a shallow-zone sedimentation coefficient of 5.34 and a deep-zone sedimentation coefficient of 6.23, which are used to assess the degree of filter blockage at different depths.

[0052] In actual application, during the operation monitoring phase of Company A's filtration system, technician C extracted shallow and deep area sequences from the dust retention distribution map, calculated the rate of change at adjacent time points as the diffusion trend; dynamically adjusted the compensation factor values ​​in the compensation factor table according to the preset change rate threshold; applied the adjusted compensation factors to the shallow and deep area sequences, and output the compensated corrected values; finally, fused the compensation values ​​and calculated the shallow and deep area average values ​​respectively, and output them as deposition quantification coefficients. The entire process was completed in real time through the D analysis platform in the B test environment.

[0053] This solution effectively offsets the impact of signal attenuation on quantitative results by adaptively adjusting the compensation factor driven by dynamic diffusion trends. The layered compensation mechanism precisely separates and corrects surface and deep dust deposition data, enabling multi-level real-time quantitative assessment of the filter clogging status. This provides cleaning decision support for the purifier based on actual deposition trends, comprehensively improving the accuracy of clogging warnings and system maintenance efficiency.

[0054] Figure 2 A scene diagram of a real-time monitoring method for dust deposition in a plasma air purifier is provided in an embodiment of the present application, such as Figure 2 As shown, a complete embodiment of steps 101 to 105 includes: In actual application, during the full-process test of the plasma air purification system of Company A, technician C first installed a light concentrator device containing a lens group and a reflector on the windward side of the filter. By adjusting the lens spacing, the dust particle density was compressed and the scattered light signal was enhanced. At the same time, the output current signal was amplified through photoelectric conversion. Then, during the continuous ionization process of the plasma, the voltage intensity and time interval of the electrode array were dynamically adjusted to divide the boundaries of the high and low charge density areas. The amplified current signal and dust movement data were synchronously collected and labeled with time and space labels. The composite signal was output through weighted fusion of the difference ratio. Then, based on the gradient fiber of the filter, the composite signal was output. The layer structure identifies surface and deep regions, separating the composite signal into a surface-retained dust optical signal component and a deeply embedded dust charge disturbance component using a dual intensity-duration threshold. The system then divides the shallow and deep regions based on the airflow path, performs a depth-weighted calculation on the two signal components, and generates a two-dimensional time-position grid map. A dynamic dust retention distribution map is then output through color coding. Finally, based on the diffusion trend of this distribution map, the rate of change is calculated, and a compensation factor is adaptively adjusted to correct the surface and deep sequence data. The shallow and deep deposition coefficients are output, enabling real-time quantitative assessment of the filter's stratified blockage status. The entire process runs continuously on experimental platform B, with closed-loop verification completed by analysis system D.

[0055] This solution achieves highly sensitive acquisition of dust signals through enhanced light focusing and charge control, and combines gradient fiber structure with spatiotemporal label separation technology to accurately analyze surface / deep dust distribution. Depth weighting and color coding intuitively present the dynamic trend of pollution, and an adaptive compensation mechanism eliminates attenuation errors. The real-time output of the layered deposition coefficient provides a quantitative assessment basis for the purifier's blockage status, significantly improving maintenance accuracy and the system's sustainable operation capability.

[0056] Figure 3 The present invention provides a schematic diagram of a real-time monitoring system for dust deposition in a plasma air purifier. Figure 3 As shown, the system includes: A conversion module 31 is configured to install a spatial compression light concentrator on the windward side of the filter of the plasma air purifier, physically compress the distribution density of dust particles through the light concentrator to enhance the intensity of the light scattering signal, and simultaneously superimpose a photoelectric converter to convert the scattered light signal into an amplified current signal; A generation module 32 is used to perform spatial charge distribution control during the continuous ionization process of the plasma, synchronously collect the amplified current signal and the dust movement data generated by the charge disturbance, and form a composite signal with spatiotemporal coupling characteristics; A separation module 33 is configured to separate the composite signal into an optical signal component corresponding to the surface retained dust distribution and a charge disturbance component corresponding to the deep embedded dust distribution based on the gradient fiber layer structure of the filter; a calculation module 34 configured to input the optical signal component and the charge disturbance component into a preset hierarchical monitoring model, perform a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter, and generate a dust retention distribution map including dust location information; The fusion module 35 is used to establish an adaptive attenuation compensation mechanism based on the dynamic diffusion trend of the dust retention distribution diagram, and output the deposition quantification coefficients of different layers of the filter blockage structure by fusing the current surface retained dust distribution and the current deep embedded dust distribution changes. The layered real-time quantitative evaluation of the filter blockage status during operation is performed based on the deposition quantification coefficients.

[0057] Figure 3 The dust deposition real-time monitoring system of a plasma air purifier can be performed Figure 1 The implementation principles and technical effects of the method for real-time monitoring of dust deposition in a plasma air purifier described in the illustrated embodiment are not further elaborated. The specific manner in which the various modules and units perform their operations in the real-time monitoring system for dust deposition in a plasma air purifier in the above-mentioned embodiment have been described in detail in the embodiments of the method and will not be further elaborated here.

[0058] In one possible design, Figure 3 The dust deposition real-time monitoring system of a plasma air purifier of the embodiment shown 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 called and executed by the processing component 42 .

[0059] The processing component 42 is used for the above Figure 1 The embodiment provides a real-time monitoring method for dust deposition in a plasma air purifier.

[0060] 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 method. Of course, the processing component may also 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 method.

[0061] The 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 memory 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 memory, flash memory, magnetic disk, or optical disk.

[0062] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0063] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0064] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0065] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0066] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An XX method of the illustrated embodiment.

[0067] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0069] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer or server) to execute the methods described in each embodiment or certain portions of the embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for real-time monitoring of dust deposition in a plasma air purifier, characterized in that: include: A spatial compression light concentrator is installed on the windward side of the filter of the plasma air purifier, which physically compresses the distribution density of dust particles to enhance the intensity of the light scattering signal, and at the same time, a photoelectric converter is superimposed to convert the scattered light signal into an amplified current signal; Performing spatial charge distribution control during the continuous ionization process of the plasma, synchronously collecting the amplified current signal and the dust movement data generated by the charge disturbance, to form a composite signal with spatiotemporal coupling characteristics; Based on the gradient fiber layer structure of the filter, the composite signal is separated into an optical signal component corresponding to the distribution of surface retained dust and a charge disturbance component corresponding to the distribution of deeply embedded dust; Inputting the optical signal component and the charge disturbance component into a preset hierarchical monitoring model, performing a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter, and generating a dust retention distribution map containing dust location information; An adaptive attenuation compensation mechanism is established based on the dynamic diffusion trend of the dust retention distribution diagram. By fusing the changes in the current surface retained dust distribution and the current deeply embedded dust distribution, the deposition quantification coefficients of different layers of the filter blockage structure are output. Based on the deposition quantification coefficients, a hierarchical real-time quantitative evaluation of the filter blockage status during operation is performed.

2. The method according to claim 1, characterized in that Based on the gradient fiber layer structure of the filter, the composite signal is separated into an optical signal component corresponding to the surface retained dust distribution and a charge disturbance component corresponding to the deep embedded dust distribution, including: Based on the gradient fiber layer structure of the filter, the surface layer region is defined as the windward side of the filter with dense fiber distribution, and the deep layer region is defined as the leeward side with loose fiber distribution, and the density reference list is pre-calibrated; Perform signal response feature segmentation on the composite signal and extract the intensity value and duration of each data point; Based on the density reference list and the preset threshold standard, classifying as a light signal component when the intensity value exceeds the threshold and the duration is less than the specified value, and classifying as a charge disturbance component when the intensity value is lower than the threshold but the duration exceeds the specified value; The optical signal component is bound to the distribution position of retained dust in the surface layer area to generate an optical signal component data string; the charge disturbance component is bound to the distribution position of embedded dust in the deep layer area to generate a charge disturbance component data string.

3. The method according to claim 1, characterized in that During the continuous ionization process of the plasma, spatial charge distribution control is performed, and the amplified current signal and the dust movement data generated by the charge disturbance are synchronously collected to form a composite signal with spatiotemporal coupling characteristics, including: During the continuous ionization process of the plasma, the voltage intensity and time interval of the electrode array are adjusted to divide the low charge density area and the high charge density area, and the area boundaries are set based on a preset control parameter list; When the charge distribution control is activated, the amplified current signal and dust movement data are collected simultaneously, and each data point is marked with the same time stamp and regional location label; Performing time and space data superposition processing on the collected data to extract the intensity value of the amplified current signal and the displacement value of the dust movement data; The difference ratio between the intensity value and the displacement value is calculated, and a weight ratio is set according to the charge density area. The intensity value and the displacement value are combined to output a composite signal data block with a time mark and a position tag.

4. The method according to claim 1, wherein Performing a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter to generate a dust retention distribution map containing dust position information, including: According to the direction of the airflow path of the filter from the windward side to the leeward side, the shallow area and deep area are divided to form a depth level reference map; Obtaining the optical signal component corresponding to the shallow area position and the charge disturbance component corresponding to the deep area position and the corresponding position label; Setting a weight ratio between shallow and deep areas based on the depth level reference map, and applying the weight ratio to the values ​​of the light signal component and the charge disturbance component to calculate a weighted dust value; A two-dimensional grid map is constructed, where grid rows represent time points and grid columns represent location labels. The weighted dust value is filled in each grid cell. A distribution map is drawn by color coding in combination with the two-dimensional grid map, and a dust retention distribution map is output.

5. The method according to claim 1, wherein An adaptive attenuation compensation mechanism is established based on the dynamic diffusion trend of the dust retention distribution map. By integrating the current surface retained dust distribution with the current changes in the deep embedded dust distribution, the deposition quantitative coefficients of different layers of the filter blockage structure are output, including: Extracting a shallow region sequence corresponding to the surface retained dust distribution and a deep region sequence corresponding to the deep embedded dust distribution from the dust retention distribution map, and calculating the change rate of adjacent time points as the diffusion trend of the dust retention distribution map; Setting a change rate threshold and a compensation factor table, and adjusting a corresponding compensation factor value in the compensation factor table based on the change rate threshold; Applying the compensation factor to the shallow region sequence and the deep region sequence, and outputting a compensated shallow region value and a compensated deep region value; The compensated shallow area values ​​and the compensated deep area values ​​are fused, and the average value of the compensated shallow area values ​​is output as the shallow area deposition coefficient, and the average value of the compensated deep area values ​​is output as the deep area deposition coefficient.

6. The method according to claim 1, wherein The light concentrator is used to physically compress the distribution density of dust particles to enhance the intensity of the light scattering signal, and a photoelectric converter is superimposed to convert the scattered light signal into an amplified current signal, including: A light concentrator device including a lens group and a reflector is installed on the windward side of the filter; Adjusting the distance between the lens groups in the light concentrator to compress the distribution density of dust particles, thereby enhancing the intensity of the scattered light signal and outputting an enhanced scattered light signal; At the same time, the scattered light signal is converted into an initial current value through a photoelectric converter, and the initial current value is amplified by a preset amplification factor to output an amplified current signal.

7. The method according to claim 3, characterized in that Calculating the difference ratio between the intensity value and the displacement value, setting a weight ratio according to the charge density area, combining the intensity value and the displacement value, and outputting a composite signal data block with a time stamp and a position tag, including: Extracting the corresponding intensity value, displacement value, time stamp and position tag for each data point, and calculating the absolute difference between the intensity value and the displacement value; Taking the larger value of the intensity value and the displacement value, and when the larger value is greater than zero, selecting the corresponding preset calculation rule to calculate the difference ratio; Selecting a preset weight value according to the charge density area corresponding to the position tag, calculating the intensity value, the weight value, and the displacement value according to a corresponding preset calculation rule, and outputting a combined value; The combined value, the time mark, the position tag and the difference ratio are combined into a data unit, and all the data units are integrated and sorted in time to form a composite signal data block.

8. A real-time monitoring system for dust deposition in a plasma air purifier, characterized in that: include: A spatial compression light concentrator is installed on the windward side of the filter of the plasma air purifier, which physically compresses the distribution density of dust particles to enhance the intensity of the light scattering signal, and at the same time, a photoelectric converter is superimposed to convert the scattered light signal into an amplified current signal; Performing spatial charge distribution control during the continuous ionization process of the plasma, synchronously collecting the amplified current signal and the dust movement data generated by the charge disturbance, to form a composite signal with spatiotemporal coupling characteristics; Based on the gradient fiber layer structure of the filter, the composite signal is separated into an optical signal component corresponding to the distribution of surface retained dust and a charge disturbance component corresponding to the distribution of deeply embedded dust; Inputting the optical signal component and the charge disturbance component into a preset hierarchical monitoring model, performing a weighted calculation of the deposition depth on the optical signal component and the charge disturbance component based on the airflow path characteristics of the filter, and generating a dust retention distribution map containing dust location information; An adaptive attenuation compensation mechanism is established based on the dynamic diffusion trend of the dust retention distribution diagram. By fusing the changes in the current surface retained dust distribution and the current deeply embedded dust distribution, the deposition quantification coefficients of different layers of the filter blockage structure are output. Based on the deposition quantification coefficients, a hierarchical real-time quantitative evaluation of the filter blockage status during operation is performed.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a real-time monitoring method for dust deposition in a plasma air purifier as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the real-time monitoring method for dust deposition of a plasma air purifier according to any one of claims 1 to 7 is implemented.

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