Wireless monitoring method and system for blockage of filter screen of dust remover

Abstract

The invention provides a method and a system for wirelessly monitoring blockage of a filter screen of a dust remover. The method comprises the following steps: irradiating the surface of a filter screen by multi-wavelength laser to collect optical scattering data of particle accumulation, and extracting scattering angle distribution and intensity change characteristics under different wavelengths; collaborative analysis is carried out on a structured sequential sequence of particle accumulation distribution formed by multi-source scattering, a dynamic correlation mode is captured through scattering mode correlation processing, and an abnormal spectrogram fused with multi-wavelength collaboration is constructed; and mapping the data to a filter screen three-dimensional space coordinate to generate particle stacking density three-dimensional distribution data, generating a blockage position and severity level monitoring result according to a density over-threshold value, and pushing the blockage position and severity level monitoring result to a mobile terminal for display. According to the invention, three-dimensional space positioning and real-time grading alarm of the blockage degree of the filter screen are realized, and the maintenance accuracy of the purifier and the user response timeliness are improved.

Description

Technical Field

[0001] The present application relates to the field of wireless monitoring technology, and in particular to a method and system for wirelessly monitoring dust collector filter blockage. Background Art

[0002] Over long-term operation, the dust filter of a household air purifier will gradually become clogged due to the continuous accumulation of dust particles, resulting in reduced purification efficiency, increased energy consumption, and even a shortened device lifespan. Traditional manual judgment or regular filter replacement methods make it difficult to accurately reflect the actual filter blockage status in real time, especially in scenarios where the concentration of particulate matter in the air changes dynamically, making it impossible to accurately optimize the filter maintenance cycle. Therefore, there is an urgent need for a technical solution that can monitor the degree of filter blockage in real time and enable remote monitoring through wireless data transmission, thereby improving the intelligence level and efficiency of the purifier.

[0003] Currently, a targeted solution is a clogging monitoring system based on differential pressure sensors. This system installs high-precision micro-differential pressure sensors on both sides of the filter, indirectly assessing the degree of clogging by measuring the pressure difference between the front and rear of the filter in real time. When particulate matter accumulates on the filter, airflow resistance increases, and the pressure difference rises accordingly. The system uses an algorithm to convert the pressure difference signal into a clogging level and transmits the data to a user terminal or cloud platform via a wireless module, enabling remote monitoring and early warning. This solution avoids manual intervention and can dynamically respond to changes in filter status. However, due to limitations in sensor accuracy and the influence of ambient temperature and humidity, further optimization of its anti-interference capabilities is still needed. Summary of the Invention

[0004] The present application provides a dust collector filter blockage wireless monitoring method and system to solve the problems in the prior art of fuzzy spatial positioning of filter blockage, delayed real-time classification of pollution levels, and low efficiency of manual disassembly and inspection.

[0005] In a first aspect, the present application provides a dust collector filter blockage wireless monitoring method, comprising:

[0006] The system collects optical scattering data of particle accumulation changes caused by filter clogging by synchronously irradiating the filter screen with a multi-wavelength laser installed on the surface of the air purifier, and extracts the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data;

[0007] Performing multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation;

[0008] Performing scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and constructing an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns;

[0009] Mapping the abnormal spectrum to the preset three-dimensional spatial coordinates of the filter to generate three-dimensional distribution data reflecting the particle accumulation density at different positions of the filter;

[0010] Obtain the particle accumulation density in the three-dimensional distribution data, and calculate the blockage degree based on the particle accumulation density. When the particle accumulation density exceeds a preset threshold, generate monitoring results of the blockage location and severity level, and send the monitoring results to the mobile terminal for direct display.

[0011] Optionally, a multi-wavelength laser installed on the surface of the air purifier filter is used to synchronously irradiate to collect optical scattering data of particle accumulation changes caused by filter clogging, and the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data are extracted, including:

[0012] Deploy multi-wavelength laser transmitters at multiple points on the surface of the air purifier filter, and synchronously output directional laser beams of different wavelengths to illuminate the surface area of ​​the air purifier filter;

[0013] Arranging optical receiving sensors at symmetrical angles of the directional laser beam to collect optical scattering data of the particle accumulation area, and synchronously recording the receiving time of the optical scattering data;

[0014] extracting a spatial propagation angle offset of the optical scattering data, and generating a scattering angle distribution according to the spatial propagation angle offset and a receiving time;

[0015] The peak-to-valley difference of the optical scattering data is measured, and the reception time is combined with the peak-to-valley difference to generate an intensity variation feature.

[0016] Optionally, performing multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation includes:

[0017] Obtaining characteristic points of the scattering angle distribution and fluctuation nodes of the intensity change feature, and recording a detection timestamp of each characteristic point;

[0018] Match the characteristic points of the scattering angle distribution of different wavelengths at the same timestamp with the corresponding fluctuation nodes of the intensity change characteristics to establish a multi-wavelength characteristic point association table;

[0019] Comparing the synchronization of the offset direction of the scattering angle distribution of each wavelength in the multi-wavelength feature point association table with the fluctuation trend of the intensity change feature, and marking the feature points with consistent offset directions and synchronous increase and decrease of the intensity change features as coordinated change points according to the synchronization;

[0020] The number change rate of the coordinated change points within the time window is counted to generate a particle accumulation dynamic index, and the detection timestamp of each time window and the particle accumulation dynamic index are combined into a structured time series.

[0021] Optionally, comparing the synchronization of the offset direction of the scattering angle distribution of each wavelength in the multi-wavelength feature point association table with the fluctuation trend of the intensity change feature, and marking the feature points with consistent offset directions and synchronous increase and decrease of the intensity change features as collaborative change points according to the synchronization, including:

[0022] Extracting the fluctuation trend of the offset direction and intensity change characteristics of the scattering angle distribution of each wavelength marker in the multi-wavelength feature point association table, and marking the offset direction of each wavelength marker at the same time stamp as an increase or decrease type;

[0023] Compare the change state type of intensity increase or decrease in the fluctuation trend of the intensity change feature. If the characteristic point has a wavelength identification with an offset direction that is increasing and a fluctuation trend that is increasing at the same time, or a wavelength identification with an offset direction that is decreasing and a fluctuation trend that is decreasing at the same time under the same timestamp, then the characteristic point and the corresponding timestamp are marked as a collaborative change point.

[0024] Optionally, performing scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and constructing an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns, including:

[0025] performing scattering pattern correlation synchronization processing on the structured time series, wherein the scattering pattern correlation synchronization processing separates time segments of scattering features corresponding to different wavelengths from the structured time series, and extracts the scattering angle and scattering intensity of the time segment of each wavelength;

[0026] Comparing the fluctuation direction of the scattering angle and the increase and decrease trend of the scattering intensity of each wavelength in the same time window, if the fluctuation direction of the wavelength is the same and the scattering intensity increases and decreases synchronously, then marking the time window as a synchronous correlation window;

[0027] Counting the numerical deviation amplitudes of the scattering angle and scattering intensity changes of each wavelength in the synchronous correlation window, and calculating the sum of the numerical deviation amplitudes as the dynamic correlation strength value of the dynamic correlation mode;

[0028] Comparing the dynamic correlation strength value with a preset baseline, and when the dynamic correlation strength value deviates from the preset baseline by more than a preset tolerance, marking the wavelength combination area as an abnormal cooperation area;

[0029] The time window, corresponding wavelength and numerical deviation amplitude of the abnormal cooperative region of the time window are integrated to construct an abnormal spectrum map integrating multi-wavelength cooperation.

[0030] Optionally, mapping the abnormal spectrum to preset three-dimensional spatial coordinates of the filter screen to generate three-dimensional distribution data reflecting the particle accumulation density at different positions of the filter screen includes:

[0031] Analyze the abnormal collaborative events marked in the abnormal spectrum, and extract the wavelength identifier, timestamp and numerical deviation amplitude corresponding to each abnormal collaborative event;

[0032] According to the preset filter three-dimensional space coordinate mapping table, the wavelength identifier is converted into the three-dimensional coordinate value of the corresponding filter space point;

[0033] Combining the numerical deviation amplitude of the same spatial point with the corresponding time stamp to generate a spatiotemporal correlation data unit of the filter spatial point;

[0034] Accumulate the incremental values ​​of the numerical deviation amplitudes of the same filter space point within a continuous time window, and calculate the particle packing density index of the filter space point according to the changing trend of the incremental values;

[0035] The three-dimensional coordinate values ​​of all filter space points and the particle packing density index are integrated to generate three-dimensional distribution data covering all positions of the filter for blockage status assessment.

[0036] Optionally, obtaining the particle packing density in the three-dimensional distribution data, calculating the degree of blockage based on the particle packing density, generating monitoring results of the blockage location and severity level when the particle packing density exceeds a preset threshold, and sending the monitoring results to a mobile terminal for direct display, including:

[0037] Extracting the particle packing density of each filter space point in the three-dimensional distribution data, and binding the three-dimensional coordinate value of the filter space point with the particle packing density and the acquisition time stamp to generate a spatial density data set;

[0038] Comparing the particle accumulation density of each filter space point in the spatial density data set with a preset threshold interval, when the particle accumulation density exceeds the lower limit of the preset threshold, it is marked as a slightly blocked point; when the particle accumulation density exceeds the upper limit of the threshold interval, it is marked as a severely blocked point;

[0039] Scan the blockage status of adjacent filter space points. If consecutive adjacent points are both slightly blocked, they will be merged into a slightly blocked area. If consecutive adjacent points are both heavily blocked, they will be merged into a heavily blocked area.

[0040] The monitoring results including the coordinate set of the lightly congested area and the coordinate set of the heavily congested area are output, and the monitoring results are sent to the mobile terminal for direct display via a wireless transmission protocol.

[0041] In a second aspect, the present application provides a dust collector filter blockage wireless monitoring system, comprising:

[0042] An acquisition module is configured to collect optical scattering data of changes in particle accumulation caused by filter clogging by synchronously irradiating the filter with a multi-wavelength laser installed on the surface of the air purifier filter, and to extract the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data;

[0043] An analysis module, configured to perform multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation;

[0044] a processing module, configured to perform scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and construct an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns;

[0045] A generating module, configured to map the abnormal spectrum to preset three-dimensional spatial coordinates of the filter screen to generate three-dimensional distribution data reflecting the particle packing density at different positions of the filter screen;

[0046] The sending module is used to obtain the particle accumulation density in the three-dimensional distribution data, calculate the blockage degree based on the particle accumulation density, generate monitoring results of the blockage location and severity level when the particle accumulation density exceeds a preset threshold, and send the monitoring results to the mobile terminal for direct display.

[0047] In a third aspect, an embodiment of 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 wireless monitoring method for dust collector filter blockage as described in the first aspect above.

[0048] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, the method for wirelessly monitoring dust collector filter blockage as described in the first aspect is implemented.

[0049] In the technical solution of this application, the optical scattering characteristics of particle accumulation on the filter surface are obtained through multi-wavelength laser synchronous irradiation technology to achieve non-contact high-precision blockage monitoring. The technical effects include: constructing a structured time series based on multi-source scattering collaborative analysis to accurately capture the dynamic changes of particle accumulation; constructing anomaly spectra through scattering pattern correlation processing to enhance the collaborative recognition ability of scattering characteristics of different wavelengths; three-dimensional spatial mapping technology to achieve visual positioning of the blockage position, combined with density threshold judgment to generate graded warnings. This method breaks through the limitations of traditional pressure monitoring, realizes full-area real-time quantitative assessment of the filter blockage degree, significantly improves the intelligent operation and maintenance level of the air purification system, and provides a scientific basis for precise maintenance.

[0050] Furthermore, through the coordinated deployment of multi-wavelength laser emitters and optical receiving sensors, high-precision optical detection of particle accumulation on the filter surface can be achieved. The technical effects include: directional laser beam irradiation combined with symmetrical angle reception design significantly improves the signal-to-noise ratio of the scattered signal; the scattering angle distribution constructed based on the spatial propagation angle offset can accurately reflect the physical characteristics of particle accumulation; and the time series analysis of intensity change characteristics enhances the dynamic monitoring sensitivity. This method breaks through the limitations of traditional single-point detection and realizes full-area real-time monitoring of the filter blockage status, providing air purification systems with a non-contact, highly reliable intelligent diagnostic method, effectively extending the service life of the filter and reducing maintenance costs.

[0051] 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

[0052] 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.

[0053] Figure 1 A flow chart of a wireless monitoring method for dust collector filter blockage provided by the present application is shown;

[0054] Figure 2 A scenario diagram of a wireless monitoring system for filter blockage of a dust collector provided by the present application is shown;

[0055] Figure 3 The present invention provides a schematic structural diagram of a wireless monitoring system for filter blockage in a dust collector;

[0056] Figure 4 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0057] 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.

[0058] 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.

[0059] Researchers have discovered fundamental limitations in existing air purifier filter clogging monitoring technology: While solutions based on differential pressure sensors can indirectly reflect changes in overall airflow resistance, they cannot accurately perceive the distribution characteristics of particle accumulation within the filter's microstructure. Specifically, the differential pressure signal is easily disturbed by ambient temperature and humidity, leading to misjudgment of the clogging level; and a single air pressure parameter can only assess the overall filter clogging level and cannot locate localized areas of high-density accumulation. These shortcomings stem from traditional technologies' blind spots in detecting the physical morphology of filter clogging, making it difficult to support accurate maintenance decisions and energy optimization.

[0060] In response to the above challenges, the present invention proposes a wireless monitoring method for dust collector filter blockage, the innovation of which lies in the realization of microscopic visual diagnosis of the blockage state through collaborative analysis of multi-source optical features and spatial coordinate mapping. Specifically, multi-wavelength laser irradiation technology is used to capture the scattering angle distribution and intensity change characteristics at different wavelengths, and a time-series evolution model of particle accumulation is constructed through collaborative analysis of multi-source scattering; the scattering dynamic correlation pattern is further converted into an abnormal spectrum graph, and mapped to the three-dimensional spatial coordinates of the filter to generate particle density distribution data. This method subverts the traditional indirect measurement mode: multi-wavelength laser scattering technology eliminates the influence of environmental interference on the measurement and accurately distinguishes the accumulation morphology of particles of different particle sizes; the three-dimensional spatial mapping mechanism realizes the positioning and density grading of local blockage areas of the filter for the first time, solving the problem of traditional solutions' lack of perception of microstructural changes; finally, through real-time visual feedback on the mobile terminal, users are provided with a visual decision-making basis for the blockage location and level, significantly improving the maintenance accuracy of the purifier and the intelligent level of energy consumption control.

[0061] 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.

[0062] Figure 1 A flowchart of a wireless monitoring method for dust collector filter blockage is provided for an embodiment of the present application. Figure 1 As shown, the method includes:

[0063] 101. Synchronously irradiate the filter surface of an air purifier with a multi-wavelength laser to collect optical scattering data of particle accumulation changes caused by filter clogging, and extract the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data.

[0064] Optionally, step 101 may specifically include the following steps:

[0065] 1011. Multi-wavelength laser emitters are deployed at multiple points on the surface of the air purifier filter to synchronously output directional laser beams of different wavelengths to illuminate the surface area of ​​the air purifier filter.

[0066] 1012. Arrange optical receiving sensors at symmetrical angle positions of the directional laser beam to collect optical scattering data of the particle accumulation area, and synchronously record the receiving time of the optical scattering data.

[0067] 1013. Extract a spatial propagation angle offset of the optical scattering data, and generate a scattering angle distribution according to the spatial propagation angle offset and a receiving time.

[0068] 1014. Measure the difference between the peak value and the valley value of the fluctuation of the optical scattering data, and combine the receiving time with the difference between the peak value and the valley value of the fluctuation to generate an intensity change feature.

[0069] In the above scheme, a multi-wavelength laser is a light source device that simultaneously emits lasers of multiple wavelengths. Optical scattering data is the collection of light signals scattered by the interaction between the laser and particles. The scattering angle distribution is the intensity distribution characteristics of the scattered light at different spatial angles. The intensity variation characteristic is the temporal fluctuation pattern of the scattered light intensity. The air purifier filter is a filter medium that removes particulate matter from the air. Particle accumulation is the deposited layer formed by trapped particles on the filter surface. The laser emitter is an optical device that generates a directional laser beam. A directional laser beam is a laser beam propagating in a specific direction. The optical receiving sensor is a sensor device that detects the scattered light signal. The symmetrical angular position is the receiving point position symmetrical to the laser beam propagation axis. The spatial propagation angle offset is the angular difference of the scattered light from its original propagation direction. The reception time is the time stamp of the optical sensor data acquisition. The peak value is the maximum value of the scattered light intensity. The valley difference is the intensity difference between adjacent peaks and valleys. The time stamp is the time code of the data acquisition moment. The propagation direction is the initial path of the laser beam. The sensor device is a device that converts the optical signal into an electrical signal. The deposit layer is a layer of material formed by particles accumulating on the filter surface. The filter medium is a porous material used to separate particles. The optical signal is the optical response of the laser light upon interaction with the particles. The intensity distribution is the energy distribution of the scattered light at different angles. The variation pattern is the characteristic pattern of the intensity fluctuation over time. The maximum value is the highest point in the intensity curve. The intensity difference is the difference between adjacent extreme values. The time code is a digital identifier marking the moment of data acquisition. The travel path is the spatial propagation trajectory of the laser beam. The optical response is a manifestation of the scattering characteristics of the material to the laser light. The energy distribution is the ratio of the scattered light intensity in different directions. The pattern is the regularity of the intensity variation. The highest point is the location of the maximum value in the intensity curve. The numerical difference is the arithmetic difference between two data points. The digital identifier is an encoded representation of the time information. The scattering characteristics are the characteristics of the particle's scattering behavior to the laser light. The regularity is a recognizable pattern in the data variation.

[0070] In this embodiment, first, in step 1011, a multi-wavelength laser emitter array is deployed at multiple predetermined locations on the air purifier filter surface. A synchronous trigger module drives all emitters to output directional laser beams of varying wavelengths, allowing multiple beams to precisely illuminate target areas on the filter surface. This process utilizes laser positioning and calibration technology to ensure that each wavelength beam is focused on the particle accumulation-sensitive area, establishing the fundamental light field environment for multi-dimensional optical analysis.

[0071] Subsequently, in step 1012, an array of optical receiving sensors is arranged at symmetrical angles within the directional laser beam illumination area. This array captures real-time optical scattering data from the particle accumulation area on the filter surface. The sensors simultaneously timestamp each piece of data received, forming a temporally and spatially correlated raw signal dataset. This angularly symmetrical arrangement eliminates ambient light interference and ensures data purity.

[0072] Next, in step 1013, path analysis is performed on the optical scattering data: the spatial propagation angle offset between the incident light and the scattered light is calculated using a vector analysis algorithm, the angle offsets of different sensors at the same receiving timestamp are integrated according to spatial coordinates, and a scattering angle distribution map that evolves over time is dynamically generated to quantify the influence of particle distribution on optical path deflection.

[0073] Finally, through step 1014, the waveform characteristics of the optical scattering data are scanned: the fluctuation peaks and valleys of each data are identified and the intensity difference is calculated, the receiving time is dynamically associated with the corresponding intensity difference, and an intensity change feature data set is constructed through time series encapsulation to reveal the spatiotemporal evolution law of light intensity attenuation caused by particle accumulation.

[0074] In practical applications, in the filter operation monitoring of the dust removal system, the system first deploys multi-wavelength laser emitters at four multiple points on the surface of the dust collector filter (such as the edge and center area of ​​the filter), and triggers these lasers through a synchronous controller to synchronously output directional laser beams of different wavelengths so that they evenly illuminate the surface area of ​​the air purifier filter (step 1011). At the symmetrical angle position of the backlight side of the filter and the directional laser beam (such as the symmetrical points of the laser incident angle ±30° and ±60°), the system arranges a high-sensitivity optical receiving sensor array (such as a silicon-based photodiode array) to collect optical scattering data formed by the dust particle accumulation area in real time, and synchronously record the reception time of all scattered signals (step 1012). Based on the original scattering signal, the system extracts the spatial propagation angle offset of the optical scattering data, and associates the offset with the reception time to generate a time-series scattering angle distribution map (step 1013). At the same time, the system measures the difference between the peak and valley values ​​of the optical scattering data, dynamically binds the reception time to the peak-valley difference, and forms a time-series intensity change feature (step 1014). By integrating the scattering angle distribution and intensity change characteristics, the system constructs an optical fingerprint model of filter blockage. When the dust accumulation becomes thicker, the scattering angle distribution of the short-wavelength laser will shift toward larger angles, while the peak-to-valley difference of the intensity change characteristics of the long-wavelength laser will be significantly reduced. The two work together to indicate the degree of blockage and provide non-contact judgment criteria for the wireless early warning system.

[0075] The overall solution in step 101 above enables real-time capture of the multi-dimensional optical characteristics of the filter clogging process. By utilizing simultaneous multi-wavelength laser illumination and dual-parameter acquisition of scattering angle and intensity, a dynamic monitoring system for the optical response of particle accumulation is constructed. This innovative separation of scattering angle distribution and intensity variation overcomes the limitations of traditional single-wavelength detection and provides a high-resolution data foundation for collaborative analysis of multi-source scattering. This technology significantly improves the spatial resolution and temporal sensitivity of particle accumulation on the filter surface.

[0076] 102. Perform multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation.

[0077] Optionally, step 102 may specifically include the following steps:

[0078] 1021. Obtain characteristic points of the scattering angle distribution and fluctuation nodes of the intensity change feature, and record a detection timestamp of each characteristic point.

[0079] 1022. Match the characteristic points of the scattering angle distribution of different wavelengths at the same time stamp with the corresponding fluctuation nodes of the intensity change characteristics to establish a multi-wavelength characteristic point association table.

[0080] 1023. Compare the synchronization of the offset direction of the scattering angle distribution of each wavelength in the multi-wavelength feature point association table with the fluctuation trend of the intensity change characteristic, and mark the feature points with consistent offset directions and synchronous increase and decrease of intensity change characteristics as coordinated change points based on the synchronization.

[0081] Among them, step 1023 may specifically include the following process: extracting the offset direction of the scattering angle distribution of each wavelength identifier in the multi-wavelength feature point association table and the fluctuation trend of the intensity change characteristic, and marking the offset direction of each wavelength identifier at the same timestamp as an increase or decrease type; comparing the change state type of intensity increase or decrease in the fluctuation trend of the intensity change characteristic. If the feature point has a wavelength identifier at the same timestamp where the offset direction is both increasing and the fluctuation trend is both increasing, or the offset direction is both decreasing and the fluctuation trend is both decreasing, then the feature point and the corresponding timestamp are marked as a collaborative change point.

[0082] 1024. Count the number change rate of the coordinated change points within the time window to generate a particle accumulation dynamic index, and combine the detection timestamp of each time window and the particle accumulation dynamic index into a structured time series.

[0083] In the above scheme, multi-source scattering collaborative analysis is a correlation analysis method that integrates multi-wavelength scattering features. A structured time series is an ordered data set reflecting particle accumulation dynamics. Feature points are key measurement locations in the scattering angle distribution. Fluctuation nodes are extreme points in the intensity variation feature. The detection timestamp is the time stamp of the data acquisition moment. The multi-wavelength feature point association table is a correspondence table between scattering features at different wavelengths. The shift direction is the changing trend of the scattering angle distribution. The fluctuation trend is the direction of increase or decrease in the intensity variation feature. Synchronicity is the temporal consistency of the changes in the features at different wavelengths. Coordinated change points are data points where the multi-wavelength features change synchronously. An increase type is a change state in which the scattering angle or intensity increases. A decrease type is a change state in which the scattering angle or intensity decreases. An intensity increase is an increase in value within a fluctuation trend. An intensity decrease is a decrease in value within a fluctuation trend. Calibration is the process of adding status tags to data points. A time window is an analysis period divided by a fixed duration. The number change rate is the ratio of increase or decrease in the number of coordinated change points. The particle accumulation dynamic index is a comprehensive indicator that quantifies accumulation dynamics. An ordered data set is a time-ordered sequence of characteristic data. Key measurement locations are important sampling points in the scattering angle distribution. Extreme points are peaks or troughs in the intensity curve. Time stamps are coded information about the moment of data collection. A correspondence table is a mapping table of characteristic points at different wavelengths. A change trend is the direction of change of a characteristic parameter over time. An increase or decrease direction is the tendency of a numerical value to increase or decrease. Temporal consistency is the state of synchronous change between different characteristics. A data point is a characteristic measurement value at a specific time. A change state distinguishes the type of increase or decrease in a parameter. An analysis period is a fixed time period for statistical calculations. An increase or decrease ratio is the percentage difference in the change in quantity. A comprehensive indicator is a quantitative parameter that reflects overall change. Key sampling points are key measurement locations in the characteristic distribution. A peak is a maximum point in the intensity curve. A trough is a minimum point in the intensity curve. Coded information is a digital representation of temporal data. A mapping table lists the correspondence between different characteristics. A percentage difference is the relative ratio of the change in quantity. A quantitative parameter is an indicator that numerically represents the change.

[0084] In this embodiment of the present application, first, through step 1021, the system extracts characteristic points from the scattering angle distribution (such as the coordinate positions of sudden changes in angle offset), simultaneously identifies fluctuation nodes (such as key moments of sudden increases or decreases in intensity) from the intensity change characteristics, and records a precise detection timestamp for each characteristic point. This process strictly binds the characteristic points to the timestamps through a spatiotemporal coordinate resolution algorithm, ensuring that the timing foundation for subsequent collaborative analysis is accurately established.

[0085] Then, in step 1022, the scattering angle distribution feature points corresponding to different wavelengths at the same timestamp are matched with the intensity change feature fluctuation nodes. Based on the dual index of wavelength identifier and spatial position, the angle offset feature points at the same time point are dynamically associated with the intensity fluctuation nodes to generate a structured multi-wavelength feature point association table. This table uses the timestamp as the primary key and integrates the scattering angle offset status and intensity fluctuation trend at each wavelength.

[0086] Next, in step 1023, multi-wavelength synergy verification is performed within the multi-wavelength feature point association table: the scattering angle distribution offset direction (marked as "increasing" or "decreasing") and the intensity change characteristic fluctuation trend (marked as "rising" or "decreasing") for each wavelength identifier are extracted. If, at the same feature point at the same timestamp, the offset direction for all wavelength identifiers is increasing and the fluctuation trend is rising, or the offset direction is decreasing and the fluctuation trend is falling, the full-wavelength consistency determination module demarcates the feature point and its timestamp as a synergistic change point, and records its spatial coordinates and timestamp.

[0087] Finally, in step 1024, the rate of change in the number of coordinated change points within each preset time window (e.g., 5-minute intervals) is counted. The percentage increase in the number of coordinated change points between adjacent time windows is calculated (e.g., window 2 increases by 20% compared to window 1), generating a particle accumulation dynamic index. The starting detection timestamp and corresponding index value of each time window are encapsulated as a key-value pair to construct a structured time series (e.g., a timestamp sequence [09:00, 09:05...] corresponds to an index sequence [102, 120...]), forming a quantitative expression model for the dynamic evolution of particle accumulation.

[0088] In actual application, during the operation of the filter of a household air purifier, the system first obtains the characteristic points of the scattering angle distribution and the fluctuation nodes of the intensity change characteristics, and records the detection timestamp corresponding to each characteristic point (step 1021). Based on the synchronization of timestamps, the system matches the characteristic points of the scattering angle distribution of different wavelengths at the same timestamp with the corresponding fluctuation nodes of the intensity change characteristics. For example, the scattering offset of the 785nm wavelength at an angle of +60° at 2:00 PM is associated with the peak-to-valley difference of the intensity of the wavelength at the same time, and a multi-wavelength characteristic point association table is generated (step 1022). Subsequently, the system compares the synchronization of the offset direction of the scattering angle distribution of each wavelength in the association table with the fluctuation trend of the intensity change characteristics (such as the increase or decrease of the signal intensity in the same period): If the offset direction of all wavelength identifiers at a certain timestamp is increasing and the fluctuation trend is increasing at the same time (such as dust accumulation causing large-angle scattering enhancement and signal intensity to rise synchronously), or the offset direction is decreasing and the fluctuation trend is decreasing at the same time (such as the scattered signal drops synchronously after the filter is cleaned), then the feature point and the corresponding timestamp are marked as a coordinated change point (step 1023). The system further uses a 10-minute time window to calculate the change rate of the number of coordinated change points in the window (such as the number of new coordinated points per unit time), generates a particle accumulation dynamic index reflecting the dust accumulation rate, and combines the detection timestamps of each time window with the particle accumulation dynamic index into a structured time series sequence (step 1024). The sequence is transmitted wirelessly to the user terminal, displaying the filter clogging trend in real time. When the particle accumulation dynamic index continues to rise, it indicates that dust has formed a uniform accumulation layer on the filter surface, triggering a cleaning warning. If the index fluctuates violently, it indicates a local clogging risk and requires targeted maintenance.

[0089] The overall approach in step 102 above achieves spatiotemporal correlation modeling and dynamic index generation for multi-wavelength scattering characteristics. By analyzing the spatiotemporal matching of feature points and the synchronization of offset direction / intensity trends, a mechanism for identifying coordinated changes in multi-wavelength scattering parameters is established. The particle accumulation dynamic index is calculated based on the rate of change in the number of coordinated change points within a time window, forming a structured time series that quantitatively characterizes the evolution of filter clogging. This technology overcomes the one-sidedness of single-wavelength scattering analysis and enables dynamic assessment of clogging by integrating multispectral features.

[0090] 103. Perform scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and construct an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns.

[0091] Optionally, step 103 may specifically include the following steps:

[0092] 1031. Perform scattering pattern correlation synchronization processing on the structured time series. The scattering pattern correlation synchronization processing separates time segments of scattering features corresponding to different wavelengths from the structured time series, and extracts scattering angles and scattering intensities of the time segments of each wavelength.

[0093] 1032. Compare the fluctuation direction of the scattering angle and the increase and decrease trend of the scattering intensity of each wavelength in the same time window. If the fluctuation direction of the wavelength is the same and the scattering intensity increases and decreases synchronously, mark the time window as a synchronous correlation window.

[0094] 1033. Count the numerical deviation amplitudes of the scattering angle and scattering intensity changes of each wavelength in the synchronous correlation window, and calculate the sum of the numerical deviation amplitudes as the dynamic correlation strength value of the dynamic correlation mode.

[0095] 1034. Compare the dynamic correlation strength value with a preset baseline. When the dynamic correlation strength value deviates from the preset baseline by more than a preset tolerance, mark the wavelength combination area as an abnormal cooperation area.

[0096] 1035. The time window, corresponding wavelength and numerical deviation amplitude of the abnormal cooperative region of the time window are integrated to construct an abnormal spectrum graph integrating multi-wavelength cooperation.

[0097] In the above scheme, scattering pattern correlation synchronization is a method for analyzing the temporal correlation of multi-wavelength scattering features. The dynamic correlation pattern is the pattern of coordinated changes in scattering features at different wavelengths. The anomaly spectrum is an optical signature map that identifies the region of anomalous coordination. A time segment is a continuous time segment within a structured time series. The scattering angle is the angle at which the laser scattering deviates from its original direction. The scattering intensity is the strength of the scattered light signal. The fluctuation direction is the temporal trend of the scattering angle. The increasing or decreasing trend is the direction of increase or decrease in the scattering intensity. The synchronous correlation window is the time period during which the features at different wavelengths change synchronously. The numerical deviation amplitude is the deviation of the feature parameter from the baseline. The dynamic correlation intensity value is an indicator that quantifies the coordinated changes in the features. The preset baseline is the reference value for the correlation intensity under normal conditions. The preset tolerance is the acceptable deviation range for determining anomalies. The anomalous coordination region is the spatial range of anomalous synchronization of features. The combined region is the spatial location of multi-wavelength anomalous coordination. The time window is a fixed-length analysis period. The corresponding wavelength is the laser wavelength involved in the anomalous coordination. The numerical deviation amplitude is the deviation of the feature parameter from the baseline. The continuous time segment is a data interval that is seamless in time. The angle value represents the spatial deflection of scattered laser light. The intensity is a quantitative representation of the scattered light energy. The trend of change is the temporal evolution of the parameter. The direction of change is the tendency of the value to increase or decrease. The time period is the time unit for analysis. The difference is the numerical difference between the parameter and the baseline. The quantitative index is a numerical expression of the characteristic association. The reference value is the standard parameter under normal conditions. The acceptable range is the allowable range of parameter fluctuation. The spatial range is the boundary of the region where the anomaly occurs. The laser wavelength is the specific wavelength property of the laser beam. The data interval is the continuous data segment of the time series. The spatial deflection is the angular offset of the scattered light. The energy representation is the physical description of the scattered light intensity. The temporal evolution is the process of parameter change over time. The standard parameter is the baseline value under normal conditions. The region boundary is the spatial boundary of the anomaly. The specific wavelength is a single wavelength component of the laser.

[0098] In this embodiment, first, in step 1031, the system performs scattering pattern correlation synchronization processing on the structured time series: the time series data is separated by wavelength identifier to generate independent time segment data sets (e.g., the time series of wavelengths λ1 and λ2 are separated). The scattering angle fluctuation sequence and scattering intensity change sequence are extracted from the time segment of each wavelength. A time window alignment algorithm is used to ensure that the feature data of different wavelengths remain strictly synchronized within the same time interval, thus establishing the spatiotemporal correlation foundation of multi-wavelength features.

[0099] Then, in step 1032, the scattering angle fluctuation direction (e.g., continuous increase / decrease in angle value) and the scattering intensity increase / decrease trend (e.g., increase / decrease in intensity value) of all wavelengths in the same time window are compared. If it is detected that the angle fluctuation direction of all wavelengths in a certain time window is the same (e.g., all increase) and the scattering intensity change trend is synchronized (e.g., both increase), the system automatically marks the time window as a synchronous correlation window, indicating the synergistic characteristics of multi-wavelength scattering behavior.

[0100] Next, through step 1033, the scattering characteristic deviation of each wavelength is calculated in the marked synchronous correlation window: the absolute deviation of the scattering angle value of each wavelength and the angle mean of the window is calculated respectively, and then the absolute deviation of the scattering intensity value of each wavelength and the intensity mean of the window is calculated respectively. Subsequently, the above two deviation values ​​of the same wavelength are summed to obtain the numerical deviation amplitude of the wavelength. The dynamic correlation intensity value of the dynamic correlation pattern is generated by accumulating all the wavelength deviation amplitude values ​​to quantify the degree of multi-wavelength collaborative fluctuation.

[0101] Then, in step 1034, the dynamic correlation strength value is compared with a preset baseline (e.g., a baseline range of [80, 120]) constructed from historical normal data. When the correlation strength value deviates from the preset baseline by more than a preset tolerance (e.g., <60 or >140), the system demarcates the wavelength combination region involved in the time window as an abnormal coordination region and records the abnormal wavelength identifier and deviation value.

[0102] Finally, step 1035 integrates the data for the abnormal coordination regions across all time windows. A three-dimensional data fusion (time × wavelength × deviation value) is performed on the timestamps of the abnormal windows, their corresponding wavelength combinations, and their numerical deviation amplitudes. This spectral matrix encoding generates an abnormal spectrum map that integrates multi-wavelength coordination. This map maps deviation amplitudes to thermal intensity, visually demonstrating the wavelength coordination and spatiotemporal distribution characteristics of the abnormal scattering pattern.

[0103] In actual application, during the operation of the household air purifier, the system performs scattering pattern correlation synchronization processing on the structured time series sequence (including the particle accumulation dynamic index and timestamp) generated in step 102: first, the time segments of the scattering characteristics corresponding to different wavelengths are separated from the sequence, and the scattering angle and scattering intensity in each wavelength time segment are extracted (step 1031). The system compares the fluctuation direction of the scattering angle of each wavelength in the same time window (angle increases or decreases) and the increase or decrease trend of the scattering intensity (intensity increases or decreases). If all wavelengths in a certain window show the same scattering angle fluctuation direction (such as increasing at the same time) and the scattering intensity increases or decreases synchronously (such as the intensity increases), then the time window is marked as a synchronous correlation window (step 1032). Based on the marked synchronous correlation window, the system counts the numerical deviation amplitude of the scattering angle and scattering intensity changes of each wavelength in the window, and calculates the sum of the deviation amplitudes of all wavelengths as the dynamic correlation strength value of the dynamic correlation pattern (step 1033). Compare the dynamic correlation strength value with the preset baseline (initial clean filter calibration value): If the dynamic correlation strength value deviates from the preset baseline by more than the preset tolerance (such as the total deviation exceeds the baseline by 30%), the wavelength combination area is calibrated as an abnormal coordination area (step 1034). Finally, the system integrates the time windows, corresponding wavelengths and numerical deviation amplitudes of the abnormal coordination areas of all time windows to construct a three-dimensional (time-wavelength-deviation amplitude) fusion multi-wavelength coordination abnormal spectrum (step 1035). The spectrum is transmitted wirelessly to the user's mobile phone app to intuitively display the multi-wavelength scattering coordination anomaly caused by the mixed accumulation of pet dander and pollen on the filter surface - when the wavelengths continue to shift synchronously and the intensity increases at the same time, the spectrum marks the red warning area, prompting the user to clean the blocked area at the edge of the filter in a targeted manner.

[0104] The overall solution in step 103 above enables intelligent diagnosis of dynamic correlations in scattering patterns and the construction of anomaly spectra. By detecting the synchronization of scattering angle / intensity trends across wavelengths, an innovative "synchronous correlation window" determination mechanism is designed. Dynamic correlation strength values ​​are calculated based on the sum of numerical deviation amplitudes, and combined with baseline tolerance thresholds, anomaly coordination areas are precisely located. This technology, for the first time, establishes a coordinated anomaly map of multi-wavelength scattering characteristics, overcoming the limitations of traditional single-parameter threshold alarms and enabling cross-spectral diagnosis of coordinated anomalies in the early stages of filter blockage.

[0105] 104. Map the abnormal spectrum to the preset three-dimensional spatial coordinates of the filter to generate three-dimensional distribution data reflecting the particle accumulation density at different positions of the filter.

[0106] Optionally, step 104 may specifically include the following steps:

[0107] 1041. Analyze the abnormal collaborative events marked in the abnormal spectrum, and extract the wavelength identifier, timestamp, and numerical deviation amplitude corresponding to each abnormal collaborative event.

[0108] 1042. According to a preset filter three-dimensional space coordinate mapping table, convert the wavelength identifier into a three-dimensional coordinate value of a corresponding filter space point.

[0109] 1043. Combine the numerical deviation amplitude of the same spatial point with the corresponding time stamp to generate a spatiotemporal correlation data unit of the filter spatial point.

[0110] 1044. Accumulate the incremental values ​​of the numerical deviation amplitudes of the same filter space point within the continuous time window, and calculate the particle packing density index of the filter space point based on the changing trend of the incremental values.

[0111] 1045. Integrate the three-dimensional coordinate values ​​of all filter space points and the particle packing density index to generate three-dimensional distribution data covering all positions of the filter for blockage status assessment.

[0112] In the above scheme, an abnormal coordinated event is a phenomenon in which multi-wavelength scattering characteristics exhibit abnormal synchronization. The three-dimensional spatial coordinates of the filter are a coordinate system that describes the filter's three-dimensional structure. The three-dimensional distribution data is a digital model that reflects the spatial distribution of particle accumulation. The wavelength identifier is a characteristic marker that distinguishes different laser wavelengths. The timestamp identifies the moment when the abnormal event occurred. The numerical deviation amplitude is the degree to which the characteristic parameter deviates from the baseline. The spatial point is the specific location of the filter in three-dimensional space. The three-dimensional coordinate value is the position parameter of the point in the spatial coordinate system. The spatiotemporal correlation data unit is a data structure that combines spatial position and time. The incremental value is the change in the deviation amplitude within a continuous time window. The trend of change is the pattern of the incremental value's evolution over time. The particle accumulation density index is a comprehensive indicator that quantifies the degree of accumulation. The clogging status assessment is an analysis and judgment of the degree of filter clogging. The three-dimensional structure is the three-dimensional geometric shape of the filter. The digital model is a digital representation of the spatial distribution. The characteristic marker is an identifier that distinguishes different wavelengths. The time stamp is a precise record of the time when the event occurred. The deviation from the baseline is the degree to which the parameter differs from the standard value. The specific location is a precise spatial point on the filter. Position parameters are numerical groups that describe spatial coordinates. Data structure is the format for organizing and storing data. Change is the difference between parameters in adjacent time windows. Evolution pattern is the pattern of parameter change over time. Comprehensive indicators are composite parameters used for quantitative evaluation. Analysis and judgment are assessment conclusions based on data. Geometric form is the spatial shape characteristic of an object. Digital representation is a computer-processable form of data. Precise time records are the exact moment an event occurs. Standard values ​​are reference parameters under normal conditions. Precise spatial points are specific coordinates for three-dimensional positioning. Numeric groups are multidimensional values ​​that describe location. Storage format is the way data is organized. Difference is the arithmetic difference between two values. Pattern of change is the regularity characteristic of parameter evolution. Composite parameters are multi-factor evaluations. Assessment conclusions are based on data analysis. Shape characteristics are the external features of an object. Processable format is a computer-readable data format. Precise time is the specific point in time when an event occurs. Reference parameters are baseline values ​​under normal conditions. Specific coordinates are numerical representations of spatial location. Multidimensional values ​​are the location descriptions of multiple parameters. Organization is the arrangement structure of data. Arithmetic difference is the result of subtracting numbers. Regularity is a recurring pattern. Multivariate analysis is the integrated evaluation of multiple parameters. Data analysis is the processing and interpretation of data. Form is the external shape of an object. Recognizable format is the form of data that can be read by a computer.

[0113] In this embodiment, the system first analyzes the abnormal coordinated events marked in the abnormal spectrum graph in step 1041, specifically extracting the wavelength identifier, precise timestamp, and quantified numerical deviation amplitude corresponding to each event. This process locates the abnormal peak area in the spectrum graph through a graphic feature recognition algorithm, automatically captures event parameters to form a structured event list, and provides the input data source for spatial mapping.

[0114] Subsequently, in step 1042, each wavelength identifier is converted into a specific 3D coordinate value on the filter surface (e.g., wavelength λA corresponds to coordinates (x1, y1, z1)) based on a preset 3D filter coordinate mapping table (which records the correspondence between wavelength identifiers and the filter's physical location). This table lookup conversion algorithm ensures a precise mapping of optical parameters to physical space, establishing a direct correlation between abnormal events and filter locations.

[0115] Next, in step 1043, the numerical deviation amplitude and the corresponding timestamp of the same spatial point are merged: the deviation amplitude value and the timestamp are bound to a spatiotemporal correlation data unit (such as the coordinates (x1, y1, z1)) through the spatiotemporal data encapsulator to form a spatial point attribute dataset with a time dimension, preserving the dynamic evolution characteristics of particle accumulation.

[0116] Then, in step 1044, the numerical deviation amplitudes at the same spatial point on the filter within consecutive time windows are incrementally accumulated: the difference in deviation amplitudes between adjacent time windows is calculated (e.g., window 2 can increase by 15% compared to window 1), and a time series analysis algorithm is used to fit an incremental change trend curve. Based on the slope of the curve and the cumulative increase, the particle packing density index for that point is calculated (e.g., index = base value + slope × cumulative increase), quantifying the spatiotemporal characteristics of the particle packing rate at that location.

[0117] Finally, in step 1045, the 3D coordinate values ​​of all filter spatial points and their corresponding particle packing density indices are integrated. A spatial interpolation algorithm is used to reconstruct the discrete point data into a continuous 3D grid, generating 3D distribution data covering all filter locations (e.g., a point cloud model mapping density values ​​to X / Y / Z coordinates). This data is rendered using a visualization engine, visually displaying the distribution of blockage status at different filter locations and providing an evaluation basis for cleaning decisions.

[0118] In actual application, during the operation of the household air purifier, the system first analyzes the abnormal collaborative events marked in the abnormal spectrum graph, and extracts the wavelength identifier, timestamp (such as the 14:00-14:10 period) and numerical deviation amplitude corresponding to each abnormal collaborative event (step 1041). Based on the preset filter three-dimensional spatial coordinate mapping table (which defines the installation position coordinates of laser emitters of different wavelengths on the filter surface, such as the 660nm laser corresponding to the coordinates of the upper left corner of the filter [X1, Y1, Z1]), the system converts the wavelength identifier into the three-dimensional coordinate value of the corresponding filter space point (such as the 660nm identifier is mapped to the coordinates [X1, Y1, Z1]) (step 1042). Subsequently, the system merges the numerical deviation amplitude of the same spatial point with the corresponding timestamp, for example, integrating the multiple angle offsets of the coordinates [X1, Y1, Z1] from 14:00 to 14:10 into a spatiotemporal correlation data unit (step 1043). The system further accumulates the incremental values ​​of the numerical deviation amplitude of the same filter space point within a continuous time window on a 24-hour basis, and calculates the particle packing density index of the filter space point based on the changing trend of the incremental value (continuous increase or step-by-step jump) (if the incremental slope exceeds the threshold, it is marked as high density) (step 1044). Finally, the system integrates the three-dimensional coordinate values ​​and particle packing density index of all filter space points to generate three-dimensional distribution data covering all positions of the filter: for example, the edge area of ​​the filter shows a high density index (pet dander accumulation), and the center area shows a low density index (fine particles are uniformly adsorbed). This data is projected to the user's mobile phone app via a three-dimensional heat map, intuitively displaying severely clogged areas (red high-density areas) and clean areas (blue low-density areas), achieving accurate assessment of the clog status (step 1045).

[0119] The overall solution in step 104 above achieves intelligent spatial mapping of abnormal spectra to three-dimensional blockage density. Using wavelength identification-spatial coordinate conversion technology, abstract spectral anomalies are converted into the three-dimensional coordinates of the filter's physical location. An innovative design of the spatiotemporal correlation data unit structure calculates the particle packing density index based on the incremental deviation amplitude. This technology transcends the dimensional limitations of two-dimensional planar monitoring and constructs a three-dimensional blockage distribution model covering the entire filter area, providing spatial data support for precisely locating blocked areas.

[0120] 105. Obtain the particle accumulation density in the three-dimensional distribution data, and calculate the degree of blockage based on the particle accumulation density. When the particle accumulation density exceeds a preset threshold, generate monitoring results of the blockage location and severity level, and send the monitoring results to the mobile terminal for direct display.

[0121] Optionally, step 105 may specifically include the following steps:

[0122] 1051. Extract the particle packing density of each filter space point in the three-dimensional distribution data, and bind the three-dimensional coordinate value of the filter space point with the particle packing density and the acquisition timestamp to generate a spatial density data set.

[0123] 1052. Compare the particle accumulation density of each filter space point in the spatial density data set with the preset threshold range. When the particle accumulation density exceeds the lower limit of the preset threshold, it is marked as a slightly blocked point. When the particle accumulation density exceeds the upper limit of the threshold range, it is marked as a severely blocked point.

[0124] 1053. Scan the blockage status of adjacent filter space points. If consecutive adjacent points are both slightly blocked, they will be merged into a slightly blocked area. If consecutive adjacent points are both heavily blocked, they will be merged into a heavily blocked area.

[0125] 1054. Output monitoring results including a coordinate set of a lightly congested area and a coordinate set of a heavily congested area, and send the monitoring results to a mobile processing terminal via a wireless transmission protocol for direct display.

[0126] In the above scheme, an abnormal coordinated event is a phenomenon in which multi-wavelength scattering characteristics exhibit abnormal synchronization. The three-dimensional spatial coordinates of the filter are a coordinate system that describes the filter's three-dimensional structure. The three-dimensional distribution data is a digital model that reflects the spatial distribution of particle accumulation. The wavelength identifier is a characteristic marker that distinguishes different laser wavelengths. The timestamp identifies the moment when the abnormal event occurred. The numerical deviation amplitude is the degree to which the characteristic parameter deviates from the baseline. The spatial point is the specific location of the filter in three-dimensional space. The three-dimensional coordinate value is the position parameter of the point in the spatial coordinate system. The spatiotemporal correlation data unit is a data structure that combines spatial position and time. The incremental value is the change in the deviation amplitude within a continuous time window. The trend of change is the pattern of the incremental value's evolution over time. The particle accumulation density index is a comprehensive indicator that quantifies the degree of accumulation. The clogging status assessment is an analysis and judgment of the degree of filter clogging. The three-dimensional structure is the three-dimensional geometric shape of the filter. The digital model is a digital representation of the spatial distribution. The characteristic marker is an identifier that distinguishes different wavelengths. The time stamp is a precise record of the time when the event occurred. The deviation from the baseline is the degree to which the parameter differs from the standard value. The specific location is a precise spatial point on the filter. Position parameters are numerical groups that describe spatial coordinates. Data structure is the format for organizing and storing data. Change is the difference between parameters in adjacent time windows. Evolution pattern is the pattern of parameter change over time. Comprehensive indicators are composite parameters used for quantitative evaluation. Analysis and judgment are assessment conclusions based on data. Geometric form is the spatial shape characteristic of an object. Digital representation is a computer-processable form of data. Precise time records are the exact moment an event occurs. Standard values ​​are reference parameters under normal conditions. Precise spatial points are specific coordinates for three-dimensional positioning. Numeric groups are multidimensional values ​​that describe location. Storage format is the way data is organized. Difference is the arithmetic difference between two values. Pattern of change is the regularity characteristic of parameter evolution. Composite parameters are multi-factor evaluations. Assessment conclusions are based on data analysis. Shape characteristics are the external features of an object. Processable format is a computer-readable data format. Precise time is the specific point in time when an event occurs. Reference parameters are baseline values ​​under normal conditions. Specific coordinates are numerical representations of spatial location. Multidimensional values ​​are the location descriptions of multiple parameters. Organization is the arrangement structure of data. Arithmetic difference is the result of subtracting numbers. Regularity is a recurring pattern. Multivariate analysis is the integrated evaluation of multiple parameters. Data analysis is the processing and interpretation of data. Form is the external shape of an object. Recognizable format is the form of data that can be read by a computer.

[0127] In this embodiment, the system first analyzes the particle packing density values ​​at each filter spatial point in the three-dimensional distribution data in step 1051. Using a data binding algorithm, the three-dimensional coordinate values ​​(X / Y / Z axis position), density value, and precise acquisition timestamp of the point are packaged into a structured spatial density dataset. This process ensures a strict correspondence between spatial position, congestion density, and timestamp, building a spatiotemporal database for congestion status analysis.

[0128] Then, in step 1052, the particle accumulation density at each point in the spatial density dataset is automatically compared with a preset threshold range. If the density value exceeds the lower threshold (e.g., 50%) but does not reach the upper threshold (70%), the system marks the point as slightly congested. If the density value exceeds the upper threshold, it is marked as severely congested. This marking process is completed by a real-time threshold determination engine, providing hierarchical label input for spatial region clustering.

[0129] Next, in step 1053, a 3D spatial connectivity scan is performed: A neighborhood traversal is performed for lightly congested points (in six directions: front, back, left, right, up, and down). Spatially contiguous points of the same type are aggregated into lightly congested areas. Heavy congested points are then merged into heavy congested areas using the same neighborhood rules. A boundary extraction algorithm is used to calculate the outline coordinates of each area, generating a coordinate set for the lightly congested area and a coordinate set for the heavy congested area, thus achieving a spatially aggregated representation of the congestion state.

[0130] Finally, in step 1054, the monitoring results are integrated and output: a set of coordinates of lightly and heavily congested areas and congestion level identifiers. This data is pushed to a mobile processing terminal via a wireless transmission protocol (such as MQTT). The terminal uses a 3D rendering engine to map the coordinates to a filter model: lightly congested areas are rendered as yellow semi-transparent blocks, while heavily congested areas are rendered as red warning blocks. Users can interact with the system to view congestion details through touch, completing a closed-loop, real-time visual monitoring of the congestion status.

[0131] In practical applications, during the operation of a household dust collector, the system first extracts the particle accumulation density of each spatial point on the filter from the three-dimensional distribution data (e.g., the density value at the coordinates [X1, Y1, Z1] of the filter's upper left corner). It then dynamically binds the three-dimensional coordinate values ​​(X / Y / Z axis positions) of each point, the particle accumulation density, and the acquisition timestamp (e.g., 14:00) to generate a time-series spatial density dataset (step 1051). Based on a preset blockage threshold range (the lower limit is the light blockage threshold, and the upper limit is the heavy blockage threshold), the system compares the particle accumulation density of each point in the spatial density dataset with the threshold range: when the density of a point exceeds the lower limit (e.g., kitchen fumes causing accumulation in the edge area), it is marked as a lightly blocked point; when the density exceeds the upper limit (e.g., pet dander forming a compaction in the corner of the filter), it is marked as a heavily blocked point (step 1052). The system further scans the blockage status of adjacent filter space points: if more than three consecutive points are marked as lightly blocked points (such as a long strip along the edge of the filter), they are merged into a lightly blocked area; if the consecutive points are all heavily blocked points (such as the circular area in the center of the filter), they are merged into a heavily blocked area (step 1053). Finally, the system outputs the monitoring results, which include a coordinate set of lightly blocked areas (such as an edge coordinate chain) and a coordinate set of heavily blocked areas (such as a center coordinate block). The results are pushed to the user's mobile phone processing terminal via Wi-Fi or Bluetooth wireless transmission protocol and displayed directly in the form of a three-dimensional heat map: red marks heavily blocked areas that need to be cleaned immediately, and yellow marks lightly blocked areas that are recommended for observation (step 1054).

[0132] The overall solution in step 105 above enables visual, graded warnings of congestion risks and mobile decision support. By determining density threshold intervals and scanning the status of adjacent points, an intelligent merging algorithm for lightly and heavily congested areas is constructed. This innovative approach outputs a congestion level distribution map with spatial coordinates, and enables real-time mobile visualization via wireless transmission. This technology transcends the interactive limitations of traditional local alarms, establishing a complete warning chain of "three-dimensional positioning - classification - remote notification," providing precise spatial navigation for filter maintenance.

[0133] The following is a specific example for steps 101 to 105. Figure 2 As shown:

[0134] In a home environment (especially with pets or those who are allergic to pollen), the air purifier filter is prone to accumulate mixed particles such as pet hair, dander, and pollen. The system first deploys multi-wavelength laser emitters at multiple points on the filter surface, synchronously outputting directional laser beams of different wavelengths to illuminate the filter surface area. An optical receiving sensor array is arranged at symmetrical angles of the laser beam to collect optical scattering data in the particle accumulation area in real time and synchronously record the reception time. For example, a 660nm laser scatters at a large angle when irradiated on pet hair, while a 785nm laser is more sensitive to intensity changes in pollen.

[0135] The system extracts the spatial propagation angle offset of the optical scattering data and generates a time-series scattering angle distribution based on the reception time. At the same time, the difference between the peak and valley values ​​of the fluctuation is measured to generate the intensity change characteristics. By comparing the synchronization of the scattering angle distribution offset direction (increase / decrease) and the intensity change characteristic fluctuation trend (rise / fall) of different wavelengths at the same time stamp, if at a certain moment all wavelengths show an increase in the offset direction and an increase in the fluctuation trend (such as the synchronous enhancement of scattering due to the mixed accumulation of hair and pollen), the feature point is marked as a coordinated change point. Using a 10-minute time window, the number change rate of the coordinated change points is statistically calculated to generate a particle accumulation dynamic index, and the timestamps are integrated to form a structured time series.

[0136] Perform synchronization processing on the correlation of scattering patterns of structured time series: separate the time segments corresponding to each wavelength, and extract the scattering angle and scattering intensity. If the fluctuation direction of all wavelengths in a window is the same and the scattering intensity increases and decreases synchronously (such as the scattering angles of the three wavelengths continue to increase), it is marked as a synchronous correlation window. The numerical deviation amplitude of the scattering angle and intensity change of each wavelength in the window is counted, and the sum is calculated as the dynamic correlation intensity value. When the value deviates from the preset baseline (initial clean filter calibration value) and exceeds the preset tolerance, the wavelength combination area is calibrated as an abnormal collaborative area. Integrate the time windows, wavelengths and numerical deviation amplitudes of all abnormal collaborative areas to construct a three-dimensional abnormal spectrum.

[0137] The system analyzes the abnormal coordinated events in the abnormal spectra, extracts wavelength identifiers, timestamps, and numerical deviation amplitudes, and converts them into three-dimensional coordinate values ​​based on the filter's three-dimensional spatial coordinate mapping table. Multiple deviation amplitudes and timestamps for the same coordinate point are combined into a spatiotemporal correlation data unit. The incremental values ​​of the numerical deviation amplitudes within 24 hours are accumulated, and the particle packing density index is calculated based on the incremental trend (continuously increasing values ​​are marked as high density). The three-dimensional coordinate values ​​and particle packing density index of all coordinate points are integrated to generate three-dimensional distribution data (for example, a high density index is displayed in the upper right corner of the filter).

[0138] Finally, the system extracts the particle accumulation density of each spatial point, binds the three-dimensional coordinates and time stamps to generate a spatial density data set. When the density of a point exceeds the lower limit of the threshold, it is marked as a lightly blocked point, and when it exceeds the upper limit, it is marked as a severely blocked point (such as the corner of the filter is severely blocked due to pollen compaction). Scan the blockage status of adjacent points, merge continuous light points into lightly blocked areas (such as edge strip areas), and merge continuous heavy points into heavily blocked areas (such as central block areas). The monitoring results (including regional coordinate sets and severity levels) are sent to the user's mobile phone via Wi-Fi and directly displayed in a three-dimensional heat map: yellow areas indicate light blockages that need to be observed, and red areas warn that they need to be cleaned immediately.

[0139] Figure 3 The present invention provides a schematic diagram of a dust collector filter blockage wireless monitoring system. Figure 2 As shown, the system includes:

[0140] An acquisition module 31 is configured to collect optical scattering data of changes in particle accumulation caused by filter clogging by synchronously irradiating a multi-wavelength laser mounted on the surface of the air purifier filter, and extract the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data;

[0141] An analysis module 32 is configured to perform a multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation;

[0142] a processing module 33 for performing scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and constructing an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns;

[0143] A generating module 34 is used to map the abnormal spectrum to a preset three-dimensional spatial coordinate of the filter screen to generate three-dimensional distribution data reflecting the particle accumulation density at different positions of the filter screen;

[0144] The sending module 35 is used to obtain the particle accumulation density in the three-dimensional distribution data, and calculate the degree of blockage based on the particle accumulation density. When the particle accumulation density exceeds a preset threshold, a monitoring result of the blockage location and severity level is generated, and the monitoring result is sent to the mobile terminal for direct display.

[0145] Figure 3 The wireless monitoring system for dust collector filter blockage can be implemented Figure 1The implementation principle and technical effects of the wireless dust collector filter blockage monitoring method described in the illustrated embodiment are not further described. The specific manner in which each module and unit performs operations in the wireless dust collector filter blockage monitoring system in the above embodiment has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0146] In one possible design, Figure 3 A dust collector filter blockage wireless monitoring system 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;

[0147] 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 .

[0148] The processing component 42 is used for the above Figure 1 The embodiment provides a wireless monitoring method for dust collector filter blockage.

[0149] The processing component 42 may include one or more processors to execute computer instructions to perform 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.

[0150] 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.

[0151] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0152] 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.

[0153] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0154] 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.

[0155] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a wireless monitoring method for filter blockage in a dust collector.

[0156] 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.

[0157] 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.

[0158] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0159] 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 wireless monitoring method for dust collector filter blockage, characterized in that: include: The system collects optical scattering data of particle accumulation changes caused by filter clogging by synchronously irradiating the filter screen with a multi-wavelength laser installed on the surface of the air purifier, and extracts the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data; Performing multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation; Performing scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and constructing an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns; Mapping the abnormal spectrum to the preset three-dimensional spatial coordinates of the filter to generate three-dimensional distribution data reflecting the particle accumulation density at different positions of the filter; Obtain the particle accumulation density in the three-dimensional distribution data, and calculate the blockage degree based on the particle accumulation density. When the particle accumulation density exceeds a preset threshold, generate monitoring results of the blockage location and severity level, and send the monitoring results to the mobile terminal for direct display.

2. The method according to claim 1, characterized in that The optical scattering data of particle accumulation changes caused by filter clogging is collected by synchronously irradiating a multi-wavelength laser installed on the surface of the air purifier filter, and the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data are extracted, including: Deploy multi-wavelength laser transmitters at multiple points on the surface of the air purifier filter, and synchronously output directional laser beams of different wavelengths to illuminate the surface area of ​​the air purifier filter; Arranging optical receiving sensors at symmetrical angles of the directional laser beam to collect optical scattering data of the particle accumulation area, and synchronously recording the receiving time of the optical scattering data; extracting a spatial propagation angle offset of the optical scattering data, and generating a scattering angle distribution according to the spatial propagation angle offset and a receiving time; The peak-to-valley difference of the optical scattering data is measured, and the reception time is combined with the peak-to-valley difference to generate an intensity variation feature.

3. The method according to claim 1, characterized in that The scattering angle distribution and intensity variation characteristics are subjected to multi-source scattering collaborative analysis to form a structured time series reflecting the particle accumulation distribution variation, including: Obtaining characteristic points of the scattering angle distribution and fluctuation nodes of the intensity change feature, and recording a detection timestamp of each characteristic point; Match the characteristic points of the scattering angle distribution of different wavelengths at the same timestamp with the corresponding fluctuation nodes of the intensity change characteristics to establish a multi-wavelength characteristic point association table; Comparing the synchronization of the offset direction of the scattering angle distribution of each wavelength in the multi-wavelength feature point association table with the fluctuation trend of the intensity change feature, and marking the feature points with consistent offset directions and synchronous increase and decrease of the intensity change features as coordinated change points according to the synchronization; The number change rate of the coordinated change points within the time window is counted to generate a particle accumulation dynamic index, and the detection timestamp of each time window and the particle accumulation dynamic index are combined into a structured time series.

4. The method according to claim 3, wherein Comparing the synchronization of the offset direction of the scattering angle distribution of each wavelength in the multi-wavelength feature point association table with the fluctuation trend of the intensity change feature, and marking the feature points with consistent offset directions and synchronous increase and decrease of the intensity change features as collaborative change points according to the synchronization, including: Extracting the fluctuation trend of the offset direction and intensity change characteristics of the scattering angle distribution of each wavelength marker in the multi-wavelength feature point association table, and marking the offset direction of each wavelength marker at the same time stamp as an increase or decrease type; Compare the change state type of intensity increase or decrease in the fluctuation trend of the intensity change feature. If the characteristic point has a wavelength identification with an offset direction that is increasing and a fluctuation trend that is increasing at the same time, or a wavelength identification with an offset direction that is decreasing and a fluctuation trend that is decreasing at the same time under the same timestamp, then the characteristic point and the corresponding timestamp are marked as a collaborative change point.

5. The method according to claim 1, wherein Performing scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and constructing an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns, including: performing scattering pattern correlation synchronization processing on the structured time series, wherein the scattering pattern correlation synchronization processing separates time segments of scattering features corresponding to different wavelengths from the structured time series, and extracts the scattering angle and scattering intensity of the time segment of each wavelength; Comparing the fluctuation direction of the scattering angle and the increase and decrease trend of the scattering intensity of each wavelength in the same time window, if the fluctuation direction of the wavelength is the same and the scattering intensity increases and decreases synchronously, then marking the time window as a synchronous correlation window; Counting the numerical deviation amplitudes of the scattering angle and scattering intensity changes of each wavelength in the synchronous correlation window, and calculating the sum of the numerical deviation amplitudes as the dynamic correlation strength value of the dynamic correlation mode; Comparing the dynamic correlation strength value with a preset baseline, and when the dynamic correlation strength value deviates from the preset baseline by more than a preset tolerance, marking the wavelength combination area as an abnormal cooperation area; The time window, corresponding wavelength and numerical deviation amplitude of the abnormal cooperative region of the time window are integrated to construct an abnormal spectrum map integrating multi-wavelength cooperation.

6. The method according to claim 1, wherein Mapping the abnormal spectrum to the preset three-dimensional spatial coordinates of the filter to generate three-dimensional distribution data reflecting the particle accumulation density at different positions of the filter, including: Analyze the abnormal collaborative events marked in the abnormal spectrum, and extract the wavelength identifier, timestamp and numerical deviation amplitude corresponding to each abnormal collaborative event; According to the preset filter three-dimensional space coordinate mapping table, the wavelength identifier is converted into the three-dimensional coordinate value of the corresponding filter space point; Combining the numerical deviation amplitude of the same spatial point with the corresponding time stamp to generate a spatiotemporal correlation data unit of the filter spatial point; Accumulate the incremental values ​​of the numerical deviation amplitudes of the same filter space point within a continuous time window, and calculate the particle packing density index of the filter space point according to the changing trend of the incremental values; The three-dimensional coordinate values ​​of all filter space points and the particle packing density index are integrated to generate three-dimensional distribution data covering all positions of the filter for blockage status assessment.

7. The method according to claim 1, wherein Obtaining the particle packing density in the three-dimensional distribution data, calculating the degree of blockage based on the particle packing density, generating monitoring results of the blockage location and severity level when the particle packing density exceeds a preset threshold, and sending the monitoring results to a mobile terminal for direct display, including: Extracting the particle packing density of each filter space point in the three-dimensional distribution data, and binding the three-dimensional coordinate value of the filter space point with the particle packing density and the acquisition time stamp to generate a spatial density data set; Comparing the particle accumulation density of each filter space point in the spatial density data set with a preset threshold interval, when the particle accumulation density exceeds the lower limit of the preset threshold, it is marked as a slightly blocked point; when the particle accumulation density exceeds the upper limit of the threshold interval, it is marked as a severely blocked point; Scan the blockage status of adjacent filter space points. If consecutive adjacent points are both slightly blocked, they will be merged into a slightly blocked area. If consecutive adjacent points are both heavily blocked, they will be merged into a heavily blocked area. The monitoring results including the coordinate set of the lightly congested area and the coordinate set of the heavily congested area are output, and the monitoring results are sent to the mobile terminal for direct display via a wireless transmission protocol.

8. A wireless monitoring system for dust collector filter blockage, characterized in that: include: An acquisition module is configured to collect optical scattering data of changes in particle accumulation caused by filter clogging by synchronously irradiating the filter with a multi-wavelength laser installed on the surface of the air purifier filter, and to extract the scattering angle distribution and intensity change characteristics corresponding to different wavelengths in the optical scattering data; An analysis module, configured to perform multi-source scattering collaborative analysis on the scattering angle distribution and intensity variation characteristics to form a structured time series reflecting the particle accumulation distribution variation; a processing module, configured to perform scattering pattern correlation synchronization processing on the structured time series to capture dynamic correlation patterns between scattering features of different wavelengths, and construct an abnormal spectrum graph integrating multi-wavelength synergy according to the dynamic correlation patterns; A generating module, configured to map the abnormal spectrum to preset three-dimensional spatial coordinates of the filter screen to generate three-dimensional distribution data reflecting the particle packing density at different positions of the filter screen; The sending module is used to obtain the particle accumulation density in the three-dimensional distribution data, calculate the blockage degree based on the particle accumulation density, generate monitoring results of the blockage location and severity level when the particle accumulation density exceeds a preset threshold, and send the monitoring results to the mobile terminal for direct display.

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 dust collector filter blockage wireless monitoring method 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, a wireless monitoring method for dust collector filter blockage according to any one of claims 1 to 7 is implemented.

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