Air filter element internet of things intelligent chip monitoring method and system

Abstract

The invention provides an internet of things intelligent chip monitoring method and system for an air filter element, and relates to the technical field of intelligent management.The method comprises the steps that the PM2.5 or PM10 concentration and the dust holding capacity of the filter element are collected through a laser particulate matter sensor, and the front-back pressure difference of the filter element is monitored through a pressure difference sensor; a temperature and humidity compensation algorithm is adopted for correcting the pressure difference data, the air inlet concentration is detected through a non-dispersive infrared carbon dioxide sensor, and the air-fuel ratio abnormity with the abrupt change of the carbon dioxide concentration larger than 15% is recognized; based on the dust holding capacity and laser scattering intensity matrix feature values, feature values are extracted, a preset dust feature library is matched, a dust type label and a wear coefficient are output, and according to the dust holding capacity, the corrected pressure difference data, the environment temperature and humidity and the engine working condition, the air permeability time sequence, the environment temperature and humidity, the dust loading capacity and the engine working condition are input; the prediction error is less than + / -8%. The full-life-cycle intelligent management of the filter element is realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management and processing technology, and in particular to a method and system for monitoring an air filter element using an Internet of Things (IoT) smart chip. Background Art

[0002] Traditional air filter monitoring technology has numerous drawbacks. It relies on mechanical differential pressure sensors or fixed-cycle replacement strategies, making it susceptible to interference from dust type, temperature, and humidity. In high-humidity environments, dust agglomeration reduces the sensitivity of the differential pressure sensor, making it inaccurately reflecting the filter's clogged state. Furthermore, traditional equipment lacks connectivity with the vehicle's ECU or cloud platforms, providing only local alarms. Drivers must manually record and contact the service station, delaying maintenance responses. Furthermore, the repeated operations of resetting sensors and updating system data during filter replacements further increase the risk of human error.

[0003] In terms of the full life cycle management of filter elements, most existing filter elements are disposable designs and lack chip-level identification and life tracking functions. In actual use, filter elements usually need to undergo multiple maintenance before they are scrapped. As the number of maintenance increases, their air permeability continues to decline and the material gradually deteriorates until they reach the replacement and scrap standards. However, traditional technology cannot accurately track this dynamic change process, resulting in 30% of the remaining life of scrapped filter elements in a certain mining area not being effectively utilized, causing waste of resources.

[0004] At the user interaction level, drivers need to check the vehicle dashboard, mobile phone APP and paper maintenance manual at the same time. The information from multiple platforms is not integrated, which causes some drivers to ignore warnings due to complex operations, ultimately increasing the vehicle's maintenance costs. In terms of technical functions, traditional solutions also lack intelligent functions such as dust composition analysis, dynamic prediction of remaining life and visualization of carbon emission savings. They are generally limited to single physical signal collection and have not formed a "perception-analysis-decision-making-service" closed loop, resulting in high user maintenance costs, serious waste of resources, and the failure to effectively release the value of data. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an air filter element Internet of Things smart chip monitoring method and system to realize intelligent management of the filter element throughout its life cycle.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows: In a first aspect, a method for monitoring an air filter element with an IoT smart chip is provided, the method comprising: Step 1: A laser particulate matter sensor collects the PM2.5 or PM10 concentration and dust holding capacity of the filter element at 10-second intervals. A differential pressure sensor monitors the pressure differential before and after the filter element. This differential pressure data is corrected using a temperature and humidity compensation algorithm. A non-dispersive infrared carbon dioxide sensor detects intake air concentration and identifies air-fuel ratio abnormalities where the carbon dioxide concentration changes by more than 15%. Step 2: Based on the dust holding capacity and laser scattering intensity matrix eigenvalues, extract the eigenvalues and match them with the preset dust feature library, output the dust type label and wear coefficient, and input the air permeability time series, ambient temperature and humidity, dust load and engine operating conditions according to the dust holding capacity, corrected pressure difference data, ambient temperature and humidity, and engine operating conditions to obtain the remaining life percentage of the filter element, with a prediction error of less than ±8%; Step 3: Trigger a three-level warning based on the output remaining life percentage and air permeability threshold: When the air permeability is ≤50%, a pop-up notification will be displayed; When the air permeability is ≤40%, a pop-up window and SMS notification will be displayed; When the air permeability is ≤20%, a pop-up window will pop up on the screen to remind you and calculate the fuel consumption increment; Step 4: When the remaining life of the output is less than 5%, the filter element positioning data is sent to the cloud, and a reverse logistics work order is generated on the cloud. The company arranges after-sales personnel to revisit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the return and remanufacturing process is initiated.

[0007] Furthermore, the laser particle sensor collects the PM2.5 or PM10 concentration and dust holding capacity of the filter element at 10-second intervals, including: Step 1.1: Use a MEMS laser scattering sensor to synchronously collect PM2.5 concentration values, PM10 concentration values, and dust holding capacity data at a fixed period of 10 seconds to obtain the collected data; Step 1.2: Based on the collected data, eliminate outliers caused by transient environmental interference, convert the dust holding capacity into dust load, and encrypt and upload the verified PM2.5 or PM10 concentration and dust load to the cloud via a low-power communication module.

[0008] Furthermore, a differential pressure sensor is used to monitor the pressure difference before and after the filter element, and a temperature and humidity compensation algorithm is used to correct the differential pressure data. A non-dispersive infrared carbon dioxide sensor is used to detect the intake air concentration and identify air-fuel ratio abnormalities with a sudden change in carbon dioxide concentration greater than 15%, including: In step 1.3, the temperature and relative humidity of the filter element's environment are acquired in real time using the temperature and humidity sensor integrated on the chip. The raw output of the differential pressure sensor is input into a linear compensation formula, correlated with the dust load data, and areas with high clogging risk are marked. In step 1.4, based on the high congestion risk area, when a sudden change in the carbon dioxide concentration of a single vehicle is detected, which is greater than 15%, the carbon dioxide data of other vehicles within 10 kilometers of the same area are retrieved. If the proportion of vehicles with sudden changes in the area is greater than 30%, it is determined to be an environmental factor. If only a single vehicle has a sudden change, the engine operating condition data of the vehicle is correlated to determine a fuel injection fault.

[0009] Furthermore, based on the dust holding capacity and laser scattering intensity matrix eigenvalues, the eigenvalues are extracted and matched with the preset dust feature library to output the dust type label and wear coefficient, including: Step 2.1: Based on the corrected pressure differential data and dust load, extract the eigenvalues of the laser scattering intensity matrix, including calculating the mean, variance, and peak value of the scattering intensity as eigenvectors. The eigenvectors are then matched against a pre-set dust signature library for similarity, and a dust type label is output. Simultaneously, the pressure differential-dust coupled thermal map data is integrated to annotate high-wear dust distribution areas. When more than 50 vehicles in the same geographic area identify a new, unpredicted dust type, the dust signature library is automatically expanded and sent to the edge computing unit for update. In step 2.2, based on the dust type tag, connect to the car manufacturer's ECU historical data platform to retrieve the engine cylinder pressure fluctuation value during the filter element's service life; calculate the wear coefficient based on the cylinder pressure fluctuation value, specifically by calculating the standard deviation of the cylinder pressure fluctuation to quantify the degree of wear, and associate the wear coefficient with the dust type tag for output.

[0010] Furthermore, based on the dust holding capacity, corrected differential pressure data, ambient temperature and humidity, and engine operating conditions, the air permeability time series, ambient temperature and humidity, dust load, and engine operating conditions are input to obtain the remaining filter life percentage with a prediction error of less than ±8%, including: Step 2.3: Match the preset air permeability attenuation baseline curve based on the dust type label; superimpose the wear coefficient to correct the slope of the attenuation curve; fuse the corrected pressure difference data with the dust load to calculate the real-time air permeability attenuation value to obtain the initial value of remaining life; perform a weighted fusion calculation on the initial value of remaining life with the regional operating condition feature library and historical filter element scrapping data, where the regional operating condition feature weight accounts for 60% and the historical data weight accounts for 40%, and output the optimized value of the remaining life percentage with a prediction error of less than ±8%; Step 2.4: When the filter element is actually scrapped, dynamic compensation is performed based on the optimized value of the remaining life percentage output in step 5.1: Calculate the ratio of the predicted lifespan days to the actual usage days as the error compensation factor; If the error compensation factor is greater than 1.1, add the attenuation rate compensation value for high dust conditions to the air permeability attenuation reference curve; If the error compensation factor is less than 0.9, a dust environment correction factor of 0.15 is added to the dust load.

[0011] Furthermore, based on the outputted remaining life percentage and air permeability threshold, a three-level warning is triggered: when the air permeability is ≤50%, a pop-up notification is executed; when the air permeability is ≤40%, a pop-up and SMS notification are executed; when the air permeability is ≤20%, a screen pop-up reminder is executed and the fuel consumption increment is calculated, including: Step 3.1: Read the air permeability attenuation value corresponding to the outputted optimized value of the remaining life percentage in real time. When the air permeability attenuation value reaches the initial threshold of 50%, activate the IoT communication module to send an APP pop-up window and SMS notification to the user terminal; Step 3.2: When the air permeability attenuation value continues to drop to 40%, the vehicle voice module is called to broadcast the warning content. At the same time, the current pressure difference increase ratio is calculated based on the corrected pressure difference data. If the pressure difference increase ratio is greater than 30%, the warning level is simultaneously raised to level 2. In step 3.3, when the air permeability attenuation value reaches the critical threshold of 20%, a mandatory pop-up reminder will be displayed on the screen, and the fuel consumption increment calculation will be performed at the same time: Retrieve dust load data and output wear coefficient to calculate dust wear equivalent value; Based on the marked high-clogging risk area data, extract the actual pressure difference before and after the current filter element; Combined with the engine operating condition data, the actual pressure difference value is converted into the engine intake resistance coefficient; The absolute value of the fuel consumption increase per 100 kilometers is output through the intake resistance coefficient and the fuel consumption mapping table calibrated by the car manufacturer's ECU.

[0012] Furthermore, when the remaining life output is less than 5%, the filter element location data is sent to the cloud, which generates a reverse logistics work order. The company arranges after-sales personnel to revisit the driver and recommend that the driver perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the remanufacturing process is initiated, including: Step 4.1: When the outputted optimized value of the remaining life percentage falls below 5% for the first time, the real-time location data of the vehicle's GPS module is retrieved, and a unique identification code is generated in combination with the filter element serial number. The identification code and location data are uploaded to the cloud server via the Internet of Things communication module; In step 4.2, based on the received filter serial number, the company arranges after-sales personnel to visit the driver and recommend the driver to perform filter maintenance. The company also retrieves the output wear coefficient and recorded dynamic compensation historical data to perform residual value evaluation calculations: The total accumulated dust load of the filter element is extracted and divided by the preset maximum dust holding threshold of the filter material to obtain the physical loss rate. The wear coefficient and the physical loss rate are weighted and combined, with weights of 70% and 30% respectively, to output the residual value percentage. If the residual value percentage is greater than 30%, a reverse logistics work order containing the filter element location information is automatically generated, and a remanufacturing feasibility assessment is performed simultaneously: The calculated dust wear equivalent value is retrieved. If the wear equivalent value is ≤0.3 and the error compensation factor is in the range of [0.95, 1.05], a filter element disassembly instruction is sent to the remanufacturing factory. If the wear equivalent value is greater than 0.3, the filter element scrapping process is triggered and the user is notified.

[0013] In the second aspect, an air filter element IoT smart chip monitoring system includes: The acquisition module uses a laser particulate matter sensor to collect the PM2.5 or PM10 concentration and dust holding capacity of the filter element at 10-second intervals. A differential pressure sensor monitors the pressure differential before and after the filter element and corrects the pressure differential data using a temperature and humidity compensation algorithm. A non-dispersive infrared carbon dioxide sensor detects intake air concentration and identifies air-fuel ratio abnormalities where the carbon dioxide concentration changes by more than 15%. The extraction module is used to extract eigenvalues based on the dust holding capacity and laser scattering intensity matrix eigenvalues, match them with the preset dust feature library, output the dust type label and wear coefficient, and input the air permeability time series, ambient temperature and humidity, dust load and engine operating conditions based on the dust holding capacity, corrected pressure difference data, ambient temperature and humidity, and engine operating conditions to obtain the remaining life percentage of the filter element, with a prediction error of less than ±8%; The calculation module is used to trigger three levels of warning based on the output remaining life percentage and air permeability threshold: when the air permeability is ≤50%, a pop-up notification is executed; when the air permeability is ≤40%, a pop-up window and SMS notification are executed; when the air permeability is ≤20%, a screen pop-up reminder is executed and the fuel consumption increment is calculated; The processing module is used to send the filter element positioning data to the cloud when the output remaining life is less than 5%. The cloud generates a reverse logistics work order. The company arranges after-sales personnel to revisit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the return and remanufacturing process is initiated.

[0014] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0015] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

[0016] The above solution of the present invention includes at least the following beneficial effects: Through multi-sensor fusion and edge computing technology, real-time and accurate monitoring of the air filter status and intelligent management of the entire life cycle are achieved. At the same time, through dynamic early warning mechanisms and circular economy design, excessive replacement and engine damage are avoided, reducing user maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention provides a flow chart of an air filter element IoT smart chip monitoring method.

[0018] Figure 2 This is a schematic diagram of an air filter element Internet of Things smart chip monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0020] like Figure 1 As shown, an embodiment of the present invention provides an air filter element IoT smart chip monitoring method, the method comprising the following steps: Step 1: A laser particulate matter sensor collects the PM2.5 or PM10 concentration and dust holding capacity of the filter element at 10-second intervals. A differential pressure sensor monitors the pressure differential before and after the filter element, and a temperature and humidity compensation algorithm is used to correct the pressure differential data. A non-dispersive infrared carbon dioxide sensor detects the intake air concentration and identifies air-fuel ratio abnormalities with sudden changes in carbon dioxide concentration greater than 15%. Step 2: Based on the dust holding capacity and laser scattering intensity matrix eigenvalues, extract the eigenvalues and match them with the preset dust feature library, output the dust type label and wear coefficient, and input the air permeability time series, ambient temperature and humidity, dust load and engine operating conditions according to the dust holding capacity, corrected pressure difference data, ambient temperature and humidity, and engine operating conditions to obtain the remaining life percentage of the filter element, with a prediction error of less than ±8%; Step 3: Trigger a three-level warning based on the outputted remaining life percentage and air permeability threshold: When the air permeability is ≤50%, a pop-up notification will be displayed; when the air permeability is ≤40%, a pop-up and SMS notification will be displayed; when the air permeability is ≤20%, a pop-up screen reminder will be displayed and the fuel consumption increment will be calculated. Step 4: When the output remaining life is less than 5%, the filter element positioning data is sent to the cloud, and a reverse logistics work order is generated on the cloud. The company arranges after-sales personnel to revisit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the return to factory remanufacturing process is initiated.

[0021] In an embodiment of the present invention, through multi-sensor collaborative collection and data correction, the filter element status parameters are accurately acquired. Combined with dust type identification and LSTM life prediction model, the prediction error of the remaining life of the filter element is controlled within ±8%. Then, through a three-level early warning mechanism, intelligent triggering of maintenance is achieved. Finally, the reverse logistics and remanufacturing process is started when the filter element is scrapped, which effectively improves the accuracy and reliability of filter element status monitoring, reduces manual intervention, and reduces the risk of engine loss and resource waste.

[0022] In a preferred embodiment of the present invention, the above step 1 may include: Step 1.1: Use a MEMS laser scattering sensor to synchronously collect PM2.5 concentration values, PM10 concentration values, and dust holding capacity data at a fixed period of 10 seconds to obtain the collected data. In step 1.2, based on the collected data, outliers caused by transient environmental interference are eliminated, the dust holding capacity is converted into dust load, and the verified PM2.5 or PM10 concentration and dust load are encrypted and uploaded to the cloud via a low-power communication module.

[0023] In an embodiment of the present invention, a MEMS laser scattering sensor is used to synchronously collect PM2.5 or PM10 concentration and dust holding capacity data at a fixed period of 10 seconds. Combined with the elimination of abnormal values of environmental instantaneous interference and the conversion of dust holding capacity to dust load, the verified data is transmitted through a low-power communication module. This can achieve high-frequency and accurate collection of filter element dust concentration and load, effectively avoid data distortion caused by environmental interference, and provide a reliable data basis for subsequent dust type identification, life prediction and early warning. At the same time, the low-power design ensures the chip endurance and improves the practicality and stability of the system.

[0024] In an embodiment of the present invention, the specific steps include: Step 1.1: Use the MEMS laser scattering sensor to synchronously collect PM2.5 concentration values, PM10 concentration values, and dust holding capacity data with a fixed period of 10 seconds to obtain the collected data.

[0025] In step 1.2, perform sliding window statistics (5 cycles) on the buffer data, calculate the mean (μ) and standard deviation (σ) of PM2.5 / PM10 concentrations, and remove outliers that meet the following conditions: Concentration value > μ + 3σ or < μ - 3σ; The sudden change of dust holding capacity is greater than 50mg / cycle; Dust holding capacity - dust loading conversion.

[0026] The dust holding capacity data after cleaning is converted into the dust load on the filter surface (unit: g). The calculation formula is as follows ,in, is the dust holding capacity (mg / m 3 ), take the sum of PM2.5 and PM10 concentrations, is the sampling flow rate, is the cumulative sampling time (h), It is the effective filtration area of the filter element.

[0027] Perform CRC-16 check on PM2.5 / PM10 concentration and dust load to generate a check code. Use the AES-128 algorithm to encrypt the data, and the key is dynamically issued by the cloud.

[0028] In a preferred embodiment of the present invention, the above step 1 may include: In step 1.3, the temperature and relative humidity of the filter element's environment are acquired in real time using the temperature and humidity sensor integrated on the chip. The raw output of the differential pressure sensor is input into a linear compensation formula, correlated with the dust load data, and areas with high clogging risk are marked. In step 1.4, based on the high congestion risk area, when a sudden change in the carbon dioxide concentration of a single vehicle is detected, which is greater than 15%, the carbon dioxide data of other vehicles within 10 kilometers of the same area are retrieved. If the proportion of vehicles with sudden changes in the area is greater than 30%, it is determined to be an environmental factor. If only a single vehicle has a sudden change, the engine operating condition data of the vehicle is correlated to determine a fuel injection fault.

[0029] In an embodiment of the present invention, the ambient temperature and humidity are obtained in real time through the temperature and humidity sensor integrated on the chip, the raw data of the differential pressure sensor is linearly compensated, and the high clogging risk areas are marked in association with the dust load, which can effectively eliminate the interference of temperature and humidity on differential pressure monitoring and improve the accuracy of the filter element clogging status judgment; at the same time, through the regional analysis mechanism of sudden changes in carbon dioxide concentration, it can accurately distinguish between environmental factors and engine fuel injection failures, avoid misjudgment and quickly locate the root cause of the problem, provide a more reliable decision-making basis for filter element status evaluation and engine fault warning, and thus reduce the risk of maintenance misoperation and hidden dangers of engine loss.

[0030] In an embodiment of the present invention, the specific steps include: In step 1.3, the temperature and relative humidity of the filter element’s environment are acquired in real time through the temperature and humidity sensor integrated on the chip. The original output value of the differential pressure sensor is input into the linear compensation formula: ,in, =0.002 ℃, =0.001 %RH, is the temperature (°C), is the relative humidity (%), The raw output value of the differential pressure sensor is correlated with the dust load data to mark areas with high clogging risk. Temperature normalization is based on 25°C as the benchmark. Assuming the temperature fluctuation range is 0 to 50°C, the temperature deviation is normalized to the range of [-1, 1]. Humidity normalization is based on 50% RH as the benchmark. The humidity fluctuation range is 0 to 100% RH, and the humidity deviation is normalized to the range of [-1, 1].

[0031] In step 1.4, based on the high-congestion risk areas, if a sudden change in CO2 concentration of more than 15% is detected for a single vehicle, CO2 data from other vehicles within a 10-kilometer radius of the same area is retrieved. If the percentage of vehicles experiencing a sudden change exceeds 30% within the area, environmental factors are considered the cause. If a sudden change occurs in only a single vehicle, the engine operating data from that vehicle is correlated to determine whether it is a fuel injection failure.

[0032] In a preferred embodiment of the present invention, the above step 2 may include: Step 2.1: Based on the corrected pressure differential data and dust load, extract the eigenvalues of the laser scattering intensity matrix, including calculating the mean, variance, and peak value of the scattering intensity as eigenvectors. The eigenvectors are then matched against a pre-set dust signature library for similarity, and a dust type label is output. Simultaneously, the pressure differential-dust coupled thermal map data is integrated to annotate high-wear dust distribution areas. When more than 50 vehicles in the same geographic area identify a new, unpredicted dust type, the dust signature library is automatically expanded and sent to the edge computing unit for update. In step 2.2, based on the dust type tag, connect to the car manufacturer's ECU historical data platform to retrieve the engine cylinder pressure fluctuation value during the filter element's service life; calculate the wear coefficient based on the cylinder pressure fluctuation value, specifically by calculating the standard deviation of the cylinder pressure fluctuation to quantify the degree of wear, and associate the wear coefficient with the dust type tag for output.

[0033] In an embodiment of the present invention, the eigenvalues of the laser scattering intensity matrix are extracted based on the corrected pressure difference data and dust load, and the output type labels are matched with the preset dust feature library. The pressure difference-dust coupled thermal diagram is integrated to mark the high wear area, and the feature library is automatically expanded when a new dust type is identified in more than 50 vehicles in the same geographical area. The dust type can be accurately identified and dynamically adapted to complex environments, avoiding excessive filter replacement or engine damage due to misjudgment of dust type. At the same time, based on the dust type label, the ECU historical data platform of the automobile company is connected, and the degree of wear is quantified by statistically analyzing the standard deviation of the engine cylinder pressure fluctuation and correlating it with the dust type for output. This can accurately evaluate the wear impact of dust on the engine, provide more reliable wear data support for the prediction of the remaining life of the filter element, and reduce the filter element status judgment error from ±25% of the traditional solution to ±8%, thereby reducing the waste of more than 100,000 filter elements per year and improving the accuracy and environmental adaptability of filter element status monitoring.

[0034] In an embodiment of the present invention, the specific steps include: Step 2.1, extract the eigenvalues of the laser scattering intensity matrix: The corrected differential pressure data and dust load are correlated and analyzed to determine the effective data range of the laser scattering intensity matrix. The average value of the scattering intensity in the matrix is calculated, and the variance of the scattering intensity is calculated to characterize the discrete degree and stability characteristics of the scattering intensity. The peak value of the scattering intensity is identified and extracted as the key feature reflecting the sudden high scattering event. The above average value, variance and peak value are combined into a feature vector. ,in, is the average value of the scattered intensity, is the variance of the scattered intensity, It is the peak value of laser scattering intensity, which serves as the basic data for dust type identification.

[0035] Dust type label matching and output: The generated feature vector is compared with the various dust feature vectors in the preset dust feature library for similarity calculation, and the matching is performed in descending order of similarity. The dust type with the highest similarity is selected as the matching result, and the matched dust type label is output.

[0036] High wear dust distribution area marking: The pressure difference data and dust load data are integrated to generate a pressure difference-dust coupling thermodynamic map, with the horizontal axis showing the pressure difference (kPa) and the vertical axis showing the dust load (g). In the thermodynamic map, areas with high dust concentration and significant pressure difference changes are identified based on the preset wear threshold. These areas are marked and determined as high-wear dust distribution areas.

[0037] Dynamic expansion of dust feature library: Real-time statistics are collected on the dust types identified by each vehicle in the same geographic area. When more than 50 vehicles in the same geographic area identify a new dust type not included in the preset dust signature library, the signature library expansion mechanism is triggered, adding the newly identified dust feature vectors to the dust signature library to form a new category. The updated dust signature library is then sent to the edge computing unit to enable real-time updates of the local model.

[0038] In step 2.2, based on the dust type label output, the filter element's usage cycle for analysis is determined. The system then connects to the vehicle manufacturer's ECU historical data platform and retrieves engine cylinder pressure fluctuation data during the filter element's usage period. This data is then preliminarily cleaned to remove any significant abnormalities and ensure data validity.

[0039] Wear coefficient calculation and quantification: The cylinder pressure fluctuation values of the engine after cleaning are statistically analyzed, and their standard deviation is calculated. This standard deviation is used as an indicator to quantify the degree of engine wear and is defined as the wear coefficient. The larger the standard deviation, the more severe the cylinder pressure fluctuation and the higher the degree of engine wear.

[0040] The wear coefficient is associated with the dust type label and output: The calculated wear coefficient is associated and bound with the corresponding dust type tag to form an associated data record including the dust type tag and the wear coefficient.

[0041] In a preferred embodiment of the present invention, the above step 2 may include: Step 2.3: Match the preset air permeability attenuation baseline curve based on the dust type label; superimpose the wear coefficient to correct the slope of the attenuation curve; fuse the corrected pressure difference data with the dust load to calculate the real-time air permeability attenuation value to obtain the initial value of remaining life; perform a weighted fusion calculation on the initial value of remaining life with the regional operating condition feature library and historical filter element scrapping data, where the regional operating condition feature weight accounts for 60% and the historical data weight accounts for 40%, and output the optimized value of the remaining life percentage with a prediction error of less than ±8%; Step 2.4: When the filter element is actually scrapped, dynamic compensation is performed based on the optimized value of the remaining life percentage output in step 5.1: Calculate the ratio of the predicted lifespan days to the actual usage days as the error compensation factor; If the error compensation factor is greater than 1.1, add the attenuation rate compensation value for high dust conditions to the air permeability attenuation reference curve; If the error compensation factor is less than 0.9, a dust environment correction factor of 0.15 is added to the dust load.

[0042] In the embodiment of the present invention, by matching the preset air permeability attenuation reference curve according to the dust type label and superimposing the wear coefficient correction slope, the corrected pressure difference data and dust load are integrated to calculate the real-time air permeability attenuation value, and then the initial value of the remaining life is weightedly integrated with the regional working condition feature library (weight 60%) and the historical filter element scrapping data (weight 40%), the remaining life percentage optimization value can be accurately output (prediction error <±8%), realizing dynamic and accurate prediction of the remaining life of the filter element, which greatly improves the accuracy compared with the traditional technology with an error of ±25%; when the filter element is actually scrapped, By calculating the error compensation factor based on the ratio of predicted life to actual life, and dynamically compensating the air permeability attenuation benchmark curve or dust load, the prediction model can be self-optimized according to the actual usage scenario, adapting to the working conditions and dust environment in different areas, avoiding the prediction deviation caused by a single benchmark curve, further improving the prediction accuracy, and reducing the risk of excessive filter replacement or engine damage due to inaccurate life prediction. It can reduce the waste of more than 100,000 filter elements annually, and at the same time provide more reliable data support for filter element residual value assessment and remanufacturing, promoting the upgrade of filter element management to intelligent and precise ones.

[0043] In an embodiment of the present invention, the specific steps include: Step 2.3, match the air permeability attenuation reference curve: Based on the dust type label output in step 2.2, access the built-in benchmark curve library. The library structure is in the form of key-value pairs: {dust type label: attenuation curve model}. The benchmark curve library presets 20 typical dust models, including: Mine silica dust, highway carbon dust, metal particles, sea salt particles, cotton and linen fibers, construction site mixed dust, desert dust, industrial carbon smoke, pollen fibers, ceramic powder, etc.

[0044] Superimposed wear coefficient correction slope: Call the wear coefficient calculated in step 2.2 to correct the slope of the reference curve. If the original slope of the reference curve is , the wear coefficient is , then the corrected slope .

[0045] Calculate the real-time air permeability attenuation value: The corrected differential pressure data (ΔP) and the dust load (D) are combined and normalized to calculate the air permeability attenuation value. The normalization formula is: ,in, is the corrected pressure difference data, =10kPa, =600kPa, is the dust load, =0g, =300g.

[0046] Real-time air permeability attenuation value ,in, 、 is the experience weight, usually =0.6, =0.4.

[0047] Get the initial value of remaining life: Based on the real-time air permeability attenuation value A, and comparing it with the air permeability-remaining life mapping relationship corresponding to the reference curve, the initial value of remaining life L0 is obtained. For a linear attenuation curve (such as silica dust), the mapping relationship can be expressed as: ,in, is the real-time air permeability attenuation value, The air permeability attenuation value when the filter element is scrapped (usually 80%), Initial filter attenuation value (usually 0%).

[0048] Weighted fusion generates optimized values: Retrieve typical operating condition data for the current area (such as mountain roads, urban commuting, etc.) from the regional operating condition feature library; retrieve the average remaining life of the same dust type from historical filter element scrapping data.

[0049] Weighted formula for the remaining life optimization value: ,in, is the regional operating condition characteristic database data, For historical scrapped data, ensure that the prediction error is less than ±8%.

[0050] Step 2.4, calculate the error compensation factor: When the filter element is actually scrapped, obtain the predicted lifespan days corresponding to the optimized value of the remaining lifespan percentage output in step 2.3 and actual usage days .

[0051] Error compensation factor .

[0052] Adjust the baseline curve based on the factor: If f>1.1 (the predicted life is too long), in the air permeability attenuation benchmark curve, the attenuation rate compensation value Δk is added to the high dust condition (dust load D>200g). .

[0053] If f < 0.9 (the predicted life is too short), add a dust environment correction factor of 0.15 to the dust load D. The corrected load , increase the weight of wear assessment in dusty environments.

[0054] Update the local prediction model: The adjusted baseline curve or correction coefficient is synchronized to the edge computing unit to update the parameters of the local LSTM life prediction model to achieve model self-optimization.

[0055] In a preferred embodiment of the present invention, the above step 3 may include: Step 3.1: Read the air permeability attenuation value corresponding to the outputted optimized value of the remaining life percentage in real time. When the air permeability attenuation value reaches the initial threshold of 50%, activate the IoT communication module to send an APP pop-up window and SMS notification to the user terminal; Step 3.2: When the air permeability attenuation value continues to drop to 40%, the vehicle voice module is called to broadcast the warning content. At the same time, the current pressure difference increase ratio is calculated based on the corrected pressure difference data. If the pressure difference increase ratio is greater than 30%, the warning level is simultaneously raised to level 2. In step 3.3, when the air permeability attenuation value reaches the critical threshold of 20%, a mandatory pop-up reminder will be displayed on the screen, and the fuel consumption increment calculation will be performed at the same time: Retrieve dust load data and output wear coefficient to calculate dust wear equivalent value; Based on the marked high-clogging risk area data, extract the actual pressure difference before and after the current filter element; Combined with the engine operating condition data, the actual pressure difference value is converted into the engine intake resistance coefficient; The absolute value of the fuel consumption increase per 100 kilometers is output through the intake resistance coefficient and the fuel consumption mapping table calibrated by the car manufacturer's ECU.

[0056] In an embodiment of the present invention, by reading the air permeability attenuation value corresponding to the optimized value of the remaining life percentage in real time, a three-level warning mechanism is triggered in stages: when the air permeability decays to 50%, an APP pop-up window and SMS notification are sent to achieve early risk warning, so that the driver can pay attention to the filter element status in time; when it drops to 40%, the vehicle voice broadcast is called and the warning level is dynamically adjusted in combination with the pressure difference increase ratio to avoid misjudgment of a single indicator and improve the accuracy of the warning; when the critical threshold of 20% is reached, a screen forced pop-up window is executed and the fuel consumption increase is calculated (through the correlation calculation of the dust wear equivalent value, the actual pressure difference value and the engine intake resistance coefficient), so as to quantify the fuel consumption loss and enhance the driver's initiative to replace the filter element. This multi-level warning system upgrades the vague warning mode in traditional solutions to a data-driven, precise, graded reminder, shortening the maintenance response time from a delay of over 48 hours to real-time triggering. At the same time, it visualizes the economic impact of filter blockage through incremental fuel consumption, encouraging drivers to actively maintain the system, and reducing the annual engine repair costs caused by filter problems by more than 40%. The three-level warning logic and the pressure difference increase linkage mechanism increase the warning accuracy to 92%, effectively avoiding excessive replacement and waste of resources due to warning lags or misjudgments.

[0057] In an embodiment of the present invention, the specific steps include: Step 3.1, real-time data reading: The air permeability attenuation value corresponding to the remaining life percentage optimization value is read from the edge computing unit cache at a frequency of once per minute.

[0058] Synchronously retrieve the initial value of the current filter element air permeability (the air permeability of a new filter element is 100%) and the real-time attenuation curve.

[0059] Threshold determination and warning activation: When the air permeability attenuation value reaches or exceeds 50% for the first time, the first-level warning logic is triggered.

[0060] Activate the IoT communication module (Huawei HiSilicon Boudica200) and send warning instructions to user terminals (mobile phone APP, vehicle-mounted central control screen) via the 4G network.

[0061] Multi-terminal notification execution: A red-bordered warning window pops up in the FilterFusion APP, indicating that "the air permeability of the filter element has decreased by 50%, and it is recommended to pay attention to the status."

[0062] A text notification is sent to the driver's reserved mobile phone number via the SMS gateway, containing the vehicle number, filter status and maintenance recommendations.

[0063] Step 3.2, secondary threshold determination: Continuously monitor the air permeability attenuation value, and when it drops to 40%, trigger the second-level warning.

[0064] Synchronously retrieve the corrected differential pressure data for the past hour (sampling frequency 10 seconds each time).

[0065] Pressure differential increase calculation and warning upgrade: Calculate the current pressure difference Pressure difference compared to 1 hour ago The increase ratio: Increase ratio = , is the current pressure difference before and after the filter element, It is the pressure difference 1 hour ago.

[0066] If the increase rate is greater than 30%, the warning level will be raised from level one to level two, otherwise the level two warning will be maintained.

[0067] Voice broadcast and data synchronization: The vehicle voice module is called and the broadcast content is: "The air permeability of the filter element has decreased by 40%, and the current pressure difference has increased by XX%. Please arrange an inspection as soon as possible."

[0068] A yellow warning icon is displayed on the vehicle's dashboard, and the secondary warning details are pushed to the APP.

[0069] Step 3.3, critical threshold forced warning: When the air permeability attenuation value reaches 20%, a forced pop-up window will be triggered on the screen, covering the current vehicle system interface, and displaying a red warning icon and the text "Replace the filter immediately".

[0070] Synchronously lock some functions of the vehicle's central control screen until the driver confirms the warning information.

[0071] Calculation of dust wear equivalent value: Retrieve the current dust load ( , unit: g) and the wear coefficient output in step 2.2 ( ).

[0072] Wear equivalent value = , used to quantify the comprehensive wear degree of dust on the filter element and engine.

[0073] Intake resistance coefficient conversion: Extract the actual value of the current pressure difference from the high-blockage risk area annotation data ( , unit: kPa).

[0074] Combined with the real-time engine operating data (speed, load), the pressure difference is converted into the intake resistance coefficient ( ): , is standard atmospheric pressure, is the engine drag coefficient constant.

[0075] Fuel consumption increment calculation and output: Find the fuel consumption mapping table (k-ΔL / 100km) calibrated by the car manufacturer's ECU, enter the intake resistance coefficient k, and obtain the corresponding fuel consumption increment per 100 kilometers (ΔL).

[0076] A forced pop-up window displays: "Filter element blockage causes fuel consumption to increase by ΔLL / 100km. Replace it immediately to reduce the loss."

[0077] In a preferred embodiment of the present invention, the above step 4 may include: Step 4.1: When the outputted optimized value of the remaining life percentage falls below 5% for the first time, the real-time location data of the vehicle's GPS module is retrieved, and a unique identification code is generated in combination with the filter element serial number. The identification code and location data are uploaded to the cloud server via the Internet of Things communication module; In step 4.2, based on the received filter serial number, the company arranges after-sales personnel to visit the driver and recommend the driver to perform filter maintenance. The company also retrieves the output wear coefficient and recorded dynamic compensation historical data to perform residual value evaluation calculations: The total accumulated dust load of the filter element is extracted and divided by the preset maximum dust holding threshold of the filter material to obtain the physical loss rate. The wear coefficient and the physical loss rate are weighted and combined, with weights of 70% and 30% respectively, to output the residual value percentage. If the residual value percentage is greater than 30%, a reverse logistics work order containing the filter element location information is automatically generated, and a remanufacturing feasibility assessment is performed simultaneously: The calculated dust wear equivalent value is retrieved. If the wear equivalent value is ≤0.3 and the error compensation factor is in the range of [0.95, 1.05], a filter element disassembly instruction is sent to the remanufacturing factory. If the wear equivalent value is greater than 0.3, the filter element scrapping process is triggered and the user is notified.

[0078] In an embodiment of the present invention, when the optimized value of the remaining life percentage falls below 5% for the first time, the vehicle's GPS location data is retrieved and a unique identification code is generated in combination with the filter element serial number and uploaded to the cloud, thereby achieving accurate positioning and identity tracing of the scrapped filter element, providing a data basis for reverse logistics; the cloud retrieves the wear coefficient, dynamic compensation historical data and accumulated dust load based on the filter element serial number, and accurately evaluates the residual value through a weighted fusion of the physical loss rate and the wear coefficient (weight 70%:30%). When the residual value percentage is greater than 30%, a reverse logistics work order containing positioning information is automatically generated, and the remanufacturing feasibility is dually determined by the wear equivalent value (≤0.3) and the error compensation factor ([0.95,1.05]), thereby increasing the residual value utilization rate of the filter element from 5% of the traditional technology to 35%. This process not only realizes the intelligent recycling management of scrapped filter elements, avoiding the waste of resources caused by the direct discard of 30% of the remaining life filter elements in a certain mining area, but also promotes carbon emission reduction of 1.2kg CO2e per single filter element through precise residual value assessment and remanufacturing judgment, helping to achieve the "dual carbon" goals. At the same time, it shortens the reverse logistics response time from the unsystematic management of traditional solutions to real-time triggering, forming a circular economy closed loop of "monitoring-scrap-recycling-remanufacturing", greatly improving the economy and environmental protection of the filter element's full life cycle management.

[0079] In an embodiment of the present invention, the specific steps include: Step 4.1, Remaining life threshold monitoring: The optimized value of the remaining life percentage output by the edge computing unit is polled in real time with a monitoring frequency of 1 time per 10 minutes. When the value falls below 5% for the first time, the scrapping process start signal is triggered.

[0080] GPS location data retrieval: Access the vehicle's GPS module through the on-board OBD interface to obtain real-time longitude, latitude, and altitude data (accuracy ±10 meters) and record data collection timestamps (accurate to seconds).

[0081] Unique identification code generation: Read the 16-digit serial number (e.g. ABC123456789DEF) from the filter element's built-in NFC chip.

[0082] Concatenate the serial number and GPS coordinates in the format of "serial number_longitude_latitude_timestamp" to generate a unique identification code.

[0083] Data encryption upload: The identification code and positioning data are packaged into JSON format through the IoT communication module (supporting 4G / 5G), the data packet is encrypted using the AES-128 algorithm, and uploaded to the specified interface of the cloud server.

[0084] Step 4.2, historical data retrieval: Query the cloud database based on the filter element serial number to obtain: the wear coefficient (w) output in step 2.2 and the dynamic compensation history data (error compensation factor f sequence) recorded in step 2.4; Cumulative dust load of the filter element throughout its life cycle ( , unit: g).

[0085] Physical loss rate calculation: The preset maximum dust holding threshold of the filter material is 300g, and the physical loss rate = ,in, is the cumulative dust load (g), The preset maximum dust holding threshold (g).

[0086] Weighted calculation of residual value percentage: The wear coefficient weight is 70%, and the physical loss rate weight is 30%: Residual value percentage = , is the wear coefficient.

[0087] Reverse logistics work order generation: If the residual value percentage is greater than 30%, a reverse logistics work order will be automatically generated, including: Filter element positioning information; Residual value assessment report; The estimated return time (within 48 hours by default) is set, and the work order is pushed to the third-party logistics platform via the API.

[0088] Remanufacturing feasibility determination: Retrieve the dust wear equivalent value calculated in step 3.3.

[0089] At the same time, obtain the error compensation factor f of the last dynamic compensation: If E≤0.3 and f∈[0.95,1.05], a disassembly instruction is sent to the remanufacturing factory. If E>0.3, the scrapping process is triggered and a scrapping notification SMS is sent to the user.

[0090] like Figure 2 As shown, an embodiment of the present invention further provides an air filter element Internet of Things smart chip monitoring system, comprising: The acquisition module uses a laser particulate matter sensor to collect the PM2.5 or PM10 concentration and dust holding capacity of the filter element at 10-second intervals. A differential pressure sensor monitors the pressure differential before and after the filter element and corrects the pressure differential data using a temperature and humidity compensation algorithm. A non-dispersive infrared carbon dioxide sensor detects intake air concentration and identifies air-fuel ratio abnormalities where the carbon dioxide concentration changes by more than 15%. The extraction module is used to extract eigenvalues based on the dust holding capacity and laser scattering intensity matrix eigenvalues, match them with the preset dust feature library, output the dust type label and wear coefficient, and input the air permeability time series, ambient temperature and humidity, dust load and engine operating conditions based on the dust holding capacity, corrected pressure difference data, ambient temperature and humidity, and engine operating conditions to obtain the remaining life percentage of the filter element, with a prediction error of less than ±8%; The calculation module triggers three levels of warning based on the output remaining life percentage and air permeability threshold: when the air permeability is ≤50%, a pop-up notification is executed; when the air permeability is ≤40%, a pop-up window and SMS notification are executed; when the air permeability is ≤20%, a pop-up screen reminder is executed and the fuel consumption increment is calculated; When the remaining life output by the processing module is less than 5%, the filter element positioning data is sent to the cloud. The company arranges after-sales personnel to revisit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the remanufacturing process is initiated.

[0091] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for monitoring an air filter element IoT smart chip, characterized in that: The method comprises: Step 1: A laser particulate matter sensor is used to collect the PM2.5 or PM10 concentration and dust holding capacity of the filter element. A differential pressure sensor is used to monitor the pressure difference before and after the filter element. A temperature and humidity compensation algorithm is used to correct the pressure difference data. A non-dispersive infrared carbon dioxide sensor is used to detect the intake air concentration and identify air-fuel ratio abnormalities with a sudden change in carbon dioxide concentration greater than 15%. Step 2: Based on the dust holding capacity and laser scattering intensity matrix eigenvalues, extract the eigenvalues and match them with the preset dust feature library, output the dust type label and wear coefficient, and input the air permeability time series, ambient temperature and humidity, dust load and engine operating conditions according to the dust holding capacity, corrected pressure difference data, ambient temperature and humidity, and engine operating conditions to obtain the remaining life percentage of the filter element, with a prediction error of less than ±8%; Step 3: Trigger a three-level warning based on the outputted remaining life percentage and air permeability threshold: When the air permeability is ≤50%, a pop-up notification will be displayed; When the air permeability is ≤40%, a pop-up window and SMS notification will be displayed; When the air permeability is ≤20%, a pop-up window will pop up on the screen to remind you and calculate the fuel consumption increment; Step 4: When the output remaining life is less than 5%, the filter element positioning data is sent to the cloud, and a reverse logistics work order is generated on the cloud. The company arranges after-sales personnel to revisit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the return to factory remanufacturing process is initiated.

2. The air filter element Internet of Things smart chip monitoring method according to claim 1 is characterized in that: The laser particle sensor collects the PM2.5 or PM10 concentration and dust holding capacity of the filter element, including: Step 1.1: Use a MEMS laser scattering sensor to synchronously collect PM2.5 concentration values, PM10 concentration values, and dust holding capacity data at a fixed period of 10 seconds to obtain the collected data. In step 1.2, based on the collected data, outliers caused by transient environmental interference are eliminated, the dust holding capacity is converted into dust load, and the verified PM2.5 or PM10 concentration and dust load are encrypted and uploaded to the cloud via a low-power communication module.

3. The air filter element Internet of Things smart chip monitoring method according to claim 2 is characterized in that: The differential pressure sensor monitors the pressure difference before and after the filter element, and uses a temperature and humidity compensation algorithm to correct the differential pressure data. The non-dispersive infrared carbon dioxide sensor detects the intake air concentration and identifies air-fuel ratio abnormalities with sudden changes in carbon dioxide concentration greater than 15%, including: In step 1.3, the temperature and relative humidity of the filter element's environment are acquired in real time using the temperature and humidity sensor integrated on the chip. The raw output of the differential pressure sensor is input into a linear compensation formula, correlated with the dust load data, and areas with high clogging risk are marked. In step 1.4, based on the high-congestion risk area, when a sudden change in CO2 concentration of a single vehicle is detected, greater than 15%, the CO2 data of other vehicles within 10 kilometers of the same area are retrieved. If the proportion of vehicles with such a sudden change in the area is greater than 30%, it is determined to be an environmental factor. If only a single vehicle has a sudden change, the engine operating data of that vehicle is correlated to determine a fuel injection fault.

4. The air filter element Internet of Things smart chip monitoring method according to claim 3 is characterized in that: Based on the dust holding capacity and laser scattering intensity matrix eigenvalues, the eigenvalues are extracted and matched with the preset dust feature library to output the dust type label and wear coefficient, including: Step 2.1: Based on the corrected pressure differential data and dust load, extract the eigenvalues of the laser scattering intensity matrix, including calculating the mean, variance, and peak value of the scattering intensity as eigenvectors. The eigenvectors are then matched against a pre-set dust signature library for similarity, and a dust type label is output. Simultaneously, the pressure differential-dust coupled thermal map data is integrated to annotate high-wear dust distribution areas. When more than 50 vehicles in the same geographic area identify a new, unpredicted dust type, the dust signature library is automatically expanded and sent to the edge computing unit for update. In step 2.2, based on the dust type tag, connect to the car manufacturer's ECU historical data platform to retrieve the engine cylinder pressure fluctuation value during the filter element's service life; calculate the wear coefficient based on the cylinder pressure fluctuation value, specifically by calculating the standard deviation of the cylinder pressure fluctuation to quantify the degree of wear, and associate the wear coefficient with the dust type tag for output.

5. The air filter element Internet of Things smart chip monitoring method according to claim 4 is characterized in that: Based on the dust holding capacity, corrected differential pressure data, ambient temperature and humidity, and engine operating conditions, the air permeability time series, ambient temperature and humidity, dust load, and engine operating conditions are input to obtain the remaining filter life percentage with a prediction error of less than ±8%, including: Step 2.3: Match the preset air permeability attenuation baseline curve based on the dust type label; superimpose the wear coefficient to correct the slope of the attenuation curve; fuse the corrected pressure difference data with the dust load to calculate the real-time air permeability attenuation value to obtain the initial value of remaining life; perform a weighted fusion calculation on the initial value of remaining life with the regional operating condition feature library and historical filter element scrapping data, where the regional operating condition feature weight accounts for 60% and the historical data weight accounts for 40%, and output the optimized value of the remaining life percentage with a prediction error of less than ±8%; Step 2.4: When the filter element is actually scrapped, dynamic compensation is performed based on the optimized value of the remaining life percentage output in step 5.1: Calculate the ratio of the predicted lifespan days to the actual usage days as the error compensation factor; If the error compensation factor is greater than 1.1, add the attenuation rate compensation value for high dust conditions to the air permeability attenuation reference curve; If the error compensation factor is less than 0.9, a dust environment correction factor of 0.15 is added to the dust load.

6. The air filter element Internet of Things smart chip monitoring method according to claim 5 is characterized in that: According to the output remaining life percentage and air permeability threshold, a three-level warning is triggered: when the air permeability is ≤50%, a pop-up notification is executed; when the air permeability is ≤40%, a pop-up window and SMS notification are executed; When the air permeability is ≤20%, a pop-up screen will appear to remind you and calculate the fuel consumption increment, including: Step 3.1: Read the air permeability attenuation value corresponding to the outputted optimized value of the remaining life percentage in real time. When the air permeability attenuation value reaches the initial threshold of 50%, activate the IoT communication module to send an APP pop-up window and SMS notification to the user terminal; Step 3.2: When the air permeability attenuation value continues to drop to 40%, the vehicle voice module is called to broadcast the warning content. At the same time, the current pressure difference increase ratio is calculated based on the corrected pressure difference data. If the pressure difference increase ratio is greater than 30%, the warning level is simultaneously raised to level 2. In step 3.3, when the air permeability attenuation value reaches the critical threshold of 20%, a mandatory pop-up reminder will be displayed on the screen, and the fuel consumption increment calculation will be performed at the same time: Retrieve dust load data and output wear coefficient to calculate dust wear equivalent value; Based on the marked high-clogging risk area data, extract the actual pressure difference before and after the current filter element; Combined with the engine operating condition data, the actual pressure difference value is converted into the engine intake resistance coefficient; The absolute value of the fuel consumption increase per 100 kilometers is output through the intake resistance coefficient and the fuel consumption mapping table calibrated by the car manufacturer's ECU.

7. The air filter element IoT smart chip monitoring method according to claim 6, characterized in that: When the remaining life output is less than 5%, the filter element location data is sent to the cloud, which generates a reverse logistics work order. The company arranges after-sales personnel to visit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the remanufacturing process is initiated, including: Step 4.1: When the outputted optimized value of the remaining life percentage falls below 5% for the first time, the real-time location data of the vehicle's GPS module is retrieved, and a unique identification code is generated in combination with the filter element serial number. The identification code and location data are uploaded to the cloud server via the Internet of Things communication module; In step 4.2, based on the received filter serial number, the company arranges after-sales personnel to visit the driver and recommend the driver to perform filter maintenance. The company also retrieves the output wear coefficient and recorded dynamic compensation historical data to perform residual value evaluation calculations: The total accumulated dust load of the filter element is extracted and divided by the preset maximum dust holding threshold of the filter material to obtain the physical loss rate. The wear coefficient and the physical loss rate are weighted and combined, with weights of 70% and 30% respectively, to output the residual value percentage. If the residual value percentage is greater than 30%, a reverse logistics work order containing the filter element location information is automatically generated, and a remanufacturing feasibility assessment is performed simultaneously: The calculated dust wear equivalent value is retrieved. If the wear equivalent value is ≤0.3 and the error compensation factor is in the range of [0.95, 1.05], a filter element disassembly instruction is sent to the remanufacturing factory. If the wear equivalent value is greater than 0.3, the filter element scrapping process is triggered and the user is notified.

8. An air filter element Internet of Things smart chip monitoring system, the system implementing the method according to any one of claims 1 to 7, characterized in that: include: The acquisition module uses a laser particulate matter sensor to collect the PM2.5 or PM10 concentration and dust holding capacity of the filter element at 10-second intervals. A differential pressure sensor monitors the pressure differential before and after the filter element and corrects the pressure differential data using a temperature and humidity compensation algorithm. A non-dispersive infrared carbon dioxide sensor detects intake air concentration and identifies air-fuel ratio abnormalities where the carbon dioxide concentration changes by more than 15%. The extraction module is used to extract eigenvalues based on the dust holding capacity and laser scattering intensity matrix eigenvalues, match them with the preset dust feature library, output the dust type label and wear coefficient, and input the air permeability time series, ambient temperature and humidity, dust load and engine operating conditions based on the dust holding capacity, corrected pressure difference data, ambient temperature and humidity, and engine operating conditions to obtain the remaining life percentage of the filter element, with a prediction error of less than ±8%; The calculation module is used to trigger three levels of warning based on the output remaining life percentage and air permeability threshold: when the air permeability is ≤50%, a pop-up notification is executed; when the air permeability is ≤40%, a pop-up window and SMS notification are executed; when the air permeability is ≤20%, a screen pop-up reminder is executed and the fuel consumption increment is calculated; The processing module is used to send the filter element positioning data to the cloud when the output remaining life is less than 5%. The cloud generates a reverse logistics work order. The company arranges after-sales personnel to revisit the driver and recommend the driver to perform filter element maintenance. For filter elements with a residual value assessment of more than 30%, the return and remanufacturing process is initiated.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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