270 results about "Particulate concentration" patented technology

The invention provides a method and a system for actively monitoring, regulating and controlling a clean room, which are characterized in that environmental parameters in the clean room and spatial state information of a dynamic object are acquired through cooperation of a fixedly arranged sensor and a sensor carried by a mobile device, and the environmental parameters comprise airflow velocity, temperature and humidity, pressure difference, particulate matter concentration, illuminance and the like; the dynamic objects comprise positions of the personnel and the equipment and geometric models of the personnel and the equipment. And cleaning and fusing the collected data to generate a standardized parameter sequence with a time label, and constructing a three-dimensional dynamic environment model by combining a clean room structure and dynamic object information to realize grid mapping and visual management of environment parameters in the model. And on the basis of the model, an environment state change trend is predicted, risk grade evaluation is executed, a hierarchical regulation and control instruction is generated, closed-loop regulation and control and parameter adaptive adjustment of the clean room environment are finally realized, and the intelligent and refined level of clean room environment regulation and control is improved.

The invention discloses a construction site big data raise dust monitoring system based on big data, which relates to the technical field of construction site big data raise dust monitoring and comprises a data acquisition module, a feature extraction module, a dynamic evaluation module, a trend prediction module, an anomaly identification module, a traceability modeling module and a response control module. The data acquisition module is used for acquiring multi-source heterogeneous data in a construction site environment, and the multi-source heterogeneous data comprises particulate matter concentration, meteorological parameters, construction activity records and image information; the feature extraction module is used for carrying out standardization processing on the multi-source heterogeneous data and extracting time sequence features and space features reflecting the flying dust state; the dynamic evaluation module is used for performing fusion modeling on the extracted features by adopting a graph neural network and a dynamic weight adjustment algorithm to generate a real-time flying dust quality index DQI; and the trend prediction module is used for training a historical flying dust quality index (DQI) sequence by adopting an LSTM-Attention model.

The invention relates to an atmospheric concentration field inversion method and system based on a space-time diagram network. The method comprises the following steps: acquiring atmospheric observation data of different platforms; the method comprises the following steps: standardizing and preprocessing atmospheric observation data of different platforms to generate space-time point cloud data in a unified format; mapping the time-space point cloud data into a graph network structure for describing an atmospheric physical transmission process; constructing a hybrid neural network architecture, taking the graph network structure as the input of the hybrid neural network architecture, and training the hybrid neural network architecture to obtain a prediction model; inputting real-time radar observation data into the prediction model to obtain a three-dimensional particulate matter concentration field prediction result; and analyzing the prediction result of the three-dimensional particulate matter concentration field, and evaluating the pollution cause of the current pollution event. According to the technical scheme, a high-fidelity and time-space continuous concentration field can be generated under the condition that observation data is limited, and the accuracy and reliability of an inversion result are greatly improved.

The invention discloses a method, equipment and medium for measuring the central particle size and the particle concentration of aerosol particles, which are applied to an aerosol device adopting a charge diffusion method, and the central particle size and the particle concentration of the particles in gas to be measured are shown in the specification. Wherein S1 is the integral of an induced current I detected during ion collection in a positive pulse high-voltage period during particle size separation, and S2 is the integral of the induced current I detected during ion collection in a positive pulse low-voltage period during particle size separation; m is a polynomial fitting order, a fitting polynomial coefficient, a particle concentration calculation proportionality coefficient and a central particle size influence coefficient. Equipment adopting the method is small in size, low in manufacturing cost and high in measurement accuracy, so that the method can be popularized and used for scene requirements of vehicle or air detection and the like.

The invention relates to the technical field of clean room environment control, and discloses a clean room environment control compensation method and system based on temperature and humidity independent combined control, and the method comprises the steps: collecting the infrared thermal imaging data of a clean room; infrared thermal imaging data of the clean room are processed into surface heat flux distribution through three-dimensional space interpolation reconstruction; detecting the air dew-point temperature and the particulate matter concentration of the clean room, and combining the air dew-point temperature and the particulate matter concentration into a dynamic heat and humidity load vector; determining a target temperature value and a target humidity value of the clean room; a first control instruction for a temperature adjusting device of the clean room is generated, and a second control instruction for a humidity adjusting device of the clean room is generated; controlling the temperature adjusting device to adjust the temperature of the clean room according to the first control instruction, and controlling the humidity adjusting device to adjust the humidity of the clean room according to the second control instruction. Through organic combination of multi-source sensing, dynamic modeling and decoupling control, the precision, adaptability, safety and energy efficiency level of clean room environment control are remarkably improved.

The invention relates to the technical field of air purification, in particular to a negative oxygen ion dynamic air purification strategy and system.The negative oxygen ion dynamic air purification strategy comprises the following steps that on the basis of linkage of particulate matter concentration and multiple environmental parameters, influences of airflow, pressure, temperature and humidity and personnel flow on particulate matter distribution are analyzed, and formaldehyde and harmful gas parameter fluctuation trends are combined; pollution changes and equipment response grades are judged, the release path, frequency and power of the negative oxygen ion generator are adjusted, and ion release adjustment parameters are output. Data such as multi-element particulate matter concentration, airflow state, environmental pressure, temperature and humidity, personnel flow and the like cooperatively participate in monitoring and judgment, particulate matter grading characteristics are linked in real time through environmental dynamic factors, fluctuation trends of multiple pollutants in air are jointly calculated, high-risk change stages are actively recognized, purification response grades are dynamically output, and the purification effect is improved. The ion release direction and power are adjusted in an automatic linkage mode, the purification response changes along with space pollution hot spots, and diversified pollution distribution scenes are effectively covered.

The invention provides an atmospheric environment particulate matter pollution detection method and device, and the method comprises the steps: carrying out the grading analysis of the scattered light intensity at each environment detection point in an environment pollution detection region, and obtaining the particle size distribution characteristics of air particulate matters at each environment detection point in a plurality of particle size intervals; determining a spatial distribution field of the particulate matter concentration in the air based on the particle size distribution characteristics; determining a dynamic correction factor of diffusion and aggregation of particulate matters in the air according to an atmospheric physical state, and further performing dynamic correction on the spatial distribution field based on the dynamic correction factor; performing spatial reconstruction on the spatial distribution field obtained by dynamic correction on a regular grid, and determining the distribution pattern of the air particulate matter concentration in the environmental pollution detection area according to a reconstruction result; and generating a particulate matter pollution index in the environmental pollution detection area according to the distribution pattern. By adopting the scheme of the invention, the reliability of atmospheric environment particulate matter pollution detection can be improved based on particulate matter concentration space reconstruction of multi-point dynamic correction.

The invention relates to the technical field of environment monitoring, and discloses a real-time detection method and system for concentration and components of air particulate matters. According to the method, signal fragments are intercepted from continuous detection flow of an air sample in a segmented mode to serve as analysis elements, a de-noising characteristic signal set is generated through background field stripping, and background field parameters are recorded. And positioning a high-concentration characteristic peak cluster in the de-noised signal to mark a key detection window, and constructing a corresponding dynamic detection task sequence. And calling a special analysis thread to carry out collaborative analysis on the de-noised characteristic signal set to obtain a hierarchical judgment result. And the result is coupled and compared with background field parameters, and if conflicts exist, the dynamic detection task sequence is recombined in real time. And updating and executing a control strategy for a subsequent detection flow according to the recombined sequence, thereby forming a self-adaptive closed-loop detection process. According to the invention, the accuracy, depth and adaptive capacity of real-time analysis of pollution components in a complex environment are improved.

The invention relates to the technical field of environmental monitoring and particulate matter detection, and discloses a particulate matter concentration and particle size detection method and device.The method comprises the steps that a particulate matter sample is irradiated with laser, and forward and lateral scattered light signals are received through a photoelectric detector and converted into actual voltage vectors; constructing a chromosome data structure by adopting real number coding, and obtaining a to-be-inverted parameter set; within a preset value range of each parameter, generating an initial population by adopting a Latin hypercube sampling method; the particle size parameters are input into a Mie scattering model for calculation, and a theoretical voltage vector is obtained; performing mean square error calculation on the theoretical voltage vector and the actual voltage vector to obtain a fitness evaluation result of population individuals; if the fitness evaluation result of the population individuals meets a preset termination condition, executing a result output step; otherwise, executing genetic iteration by using the current population to generate a new population, and then returning to the scattering calculation step; on the basis of the optimal chromosome parameters meeting the termination conditions, a particulate matter concentration value and a particle size distribution curve are obtained.

The invention relates to the technical field of vehicle control, and discloses a vehicle oxygen generation control method and device, a vehicle and a computer readable storage medium. The method comprises the steps that in the vehicle running process, vehicle working condition information, the concentration of particulate matter in air and altitude information are obtained; according to the concentration of the particulate matter in the air, an air purification strategy is determined, and a protection purification module in an oxygen generation system of the vehicle is controlled to purify the air based on the air purification strategy; and according to the vehicle working condition information and the altitude information, an oxygen generation module in the oxygen generation system is controlled to treat purified air so as to generate oxygen. The oxygen generation system of the vehicle is controlled to generate oxygen according to the working condition information of the vehicle, the concentration of the particulate matter in the air and the altitude information, so that the oxygen generation control is more adaptive to the current vehicle state and environment condition, and the oxygen generation reliability and stability are further improved.

The invention relates to the technical field of particulate matter concentration monitoring, in particular to a particulate matter concentration real-time monitoring system in the dust removal process, and solves the technical problem of poor measurement stability in the prior art. The system comprises a data acquisition unit used for acquiring a particulate matter concentration monitoring value and an environment temperature value, and the particulate matter concentration monitoring value represents the particulate matter concentration measured in real time through a microscale oscillation balance method; the correction factor determination unit is used for respectively determining a temperature deviation reference value and a sequence stability index according to the environment temperature value and the historical particulate matter concentration monitoring value sequence; the temperature deviation reference value is used for representing the measurement deviation caused by the temperature change, and the sequence stability index is used for representing the change condition of the particulate matter concentration monitoring value in the particulate matter concentration real-time monitoring process; and the concentration correction unit is used for obtaining the corrected particulate matter concentration through weighted calculation according to the temperature deviation reference value, the sequence stability index and the current particulate matter concentration monitoring value.

The invention belongs to the technical field of meteorological safety, and discloses a forest fire spreading automatic forecasting method integrating laser radar detection and micro-meteorological judgment. According to the method, high-temporal-spatial-resolution real-time monitoring of forest fire points is realized through a laser radar system, and micro-meteorological elements such as particle concentration, wind speed and wind direction around the fire points are inverted based on radar echo data, so that a real-time micro-meteorological judgment mechanism is formed. A judgment result automatically drives WRF-SFIRE numerical fire spreading simulation, and automatic prediction and dynamic updating of a fire spreading trend are carried out in combination with high-resolution numerical weather forecast data. A closed-loop intelligent forecasting process of fire point detection-microclimate judgment-automatic triggering simulation-trend forecasting output is integrally constructed, and the early-stage finding and spreading trend forecasting capability of the forest fire is remarkably improved.

The invention discloses a road air particulate matter monitoring and early warning method and system, and the method comprises the steps: receiving multi-source data of a fixed monitoring network, a mobile monitoring network and a video monitoring network, and carrying out the differential self-verification and adaptive calibration of the mobile monitoring data; performing space-time alignment fusion on the calibrated data and fixed and video data to construct a unified space-time data cube; identifying a pollution behavior based on the fusion data and detecting a particle concentration peak value, and performing correlation matching on the two to generate a candidate traceability event; when the composite early warning condition is met, the structured early warning information is automatically triggered and pushed; according to the invention, a cooperative sensing system of fixed vertical monitoring, mobile dynamic sensing and video monitoring is constructed, and adaptive calibration and artificial intelligence visual identification are combined, so that a complete technical closed loop from data acquisition, fusion analysis to traceability early warning is formed; full-process automatic management of monitoring, analysis, tracing and early warning of road particulate matter pollution is achieved, and the intelligent level and response efficiency of urban road environment management are improved.

The present application relates to the technical field of engine after-treatment, and in particular to a diagnostic method, device, engine, and vehicle for identifying the risk of DPF failure, wherein the method comprises: S1, obtaining a first particulate matter concentration and a second particulate matter concentration; S2, determining a particulate matter concentration ratio based on the first particulate matter concentration and the second particulate matter concentration; S3, determining the type of failure based on the particulate matter concentration ratio and the second particulate matter concentration. The present application sets a particulate matter concentration sensor at the air inlet and outlet of the DPF, and determines whether the DPF has a fault by detecting the particulate matter concentration, which is more accurate than judging the fault by the pressure difference. In addition, the present application detects the particulate matter concentration ratio between the two sides of the DPF, and then judges the type of DPF failure based on the particulate matter concentration ratio, which can eliminate the situation where the particulate matter emission exceeds the standard due to a fault of a non-DPF device, and avoid false fault reports.

The application discloses a kind of switch cabinet purification method, system, device, storage medium and computer equipment.The method comprises: obtaining the environmental information of all grid spaces pre-divided in switch cabinet, wherein each grid space has the same spatial complexity;Based on the environmental information of each grid space, determine the condensation temperature threshold and particulate matter deposition threshold corresponding to each grid space, and monitor the temperature information and particulate matter concentration information of each grid space;Determine whether the temperature information of each grid space is less than or equal to the condensation temperature threshold corresponding to the grid space, and whether the particulate matter concentration information is greater than or equal to the particulate matter deposition threshold corresponding to the grid space;If the temperature information is less than or equal to the condensation temperature threshold and / or the particulate matter concentration information is greater than or equal to the particulate matter deposition threshold, then perform environmental purification work on the switch cabinet.The above method can greatly improve the environmental purification effect of switch cabinet.

The invention provides a particulate matter concentration determination method and device, electronic equipment and a storage medium. The determination method comprises the steps that a voltage value, environment temperature information and environment humidity information collected by a particle sensor are acquired; inputting the voltage value, the environment temperature information and the environment humidity information into a pre-trained particulate matter concentration fitting model, and performing nonlinear curve fitting on the voltage value, the environment temperature information and the environment humidity information, predicting the particulate matter concentration under the voltage value, the environment temperature information and the environment humidity information; wherein the particulate matter concentration fitting model is obtained by performing iterative training on a BP neural network model. The particulate matter concentration can be automatically and accurately predicted by using the particulate matter concentration fitting model, and the stability of the prediction result can be ensured when the environment temperature information and the environment humidity information change drastically.

The invention relates to the technical field of ammonia water purification, in particular to a high-efficiency purification system for high-purity ammonia water, comprising: a purification module comprising an ion exchange unit, a rectification unit, a vacuum degassing unit, a membrane filtration unit and a chelation adsorption unit to obtain high-purity ammonia water; comprising a first acquisition unit, a second acquisition unit, a third acquisition unit and a fourth acquisition unit; the control module is used for judging whether purification of the refined ammonia water meets a preset standard or not according to the conductivity characterization value of the refined ammonia water and judging whether purification of the finished ammonia water meets the preset standard or not according to the particulate matter concentration of the finished ammonia water; the path selection module is used for determining a purification path under the condition that the purification of the refined ammonia water does not meet the preset standard; and the adjusting module is used for increasing the operating pressure of the membrane filtration unit. The ammonia water purification efficiency is improved.

The invention belongs to the technical field of trace detection and intelligent early warning, and particularly discloses an intelligent sensing fire early warning system based on pyrolysis particulate matter detection. The system comprises a multi-angle polarized light scattering spectrum acquisition device, a physical information neural network inference engine, an airflow interference filtering unit, a pyrolysis dynamics constraint embedding module and a fire trend early warning output unit. A pyrolysis dynamic rule in an Arrhenius equation form is used as a physical constraint to be embedded into a neural network loss function, air flow disturbance is filtered out by combining topological data analysis and computational fluid mechanics, and a pyrolysis reaction stage and a fire behavior increasing trend are reversely deduced by using a multi-angle polarization scattering spectrum. And graded early warning is realized before the particulate matter concentration does not reach a traditional threshold value. According to the technical scheme, the accuracy, the robustness and the response lead of early fire recognition are improved.

According to the present invention, an air pump conventionally located in a sensor module is relocated to a flow control module, thereby reducing the noise of the sensor module and making it possible to reduce the size of the sensor module. In addition, a system is modularized so that any equipment failures that occur can be quickly resolved. To this end, the system comprises: a plurality of sensor modules that include a particle sensor for measuring the concentration of particles in the air, an air inlet and an air outlet for generating airflow, and a communication port; a flow control module that includes a gas sensor, a valve, an air pump, a manifold block, and a control circuit; and a monitoring module that receives values measured by the sensor modules and values measured by the gas sensor from the flow control module.

The invention discloses an intelligent leak detection monitoring method for a bag-type dust collector. The method comprises the following steps: S1, acquiring detection air; s2, acquiring real-time monitoring data of the concentration of the particulate matters in the detected air; and S3, performing leakage detection and leakage identification on the bag-type dust collector based on the particulate matter concentration real-time monitoring data and a dust concentration anomaly detection algorithm, wherein the leakage identification comprises identification of leakage source features and characterization of leakage positions, devices and systems. The invention further discloses a corresponding device and system, electronic equipment and a computer readable storage medium, the particulate matter concentration at the outlet of each cloth bag is monitored through the low-cost particulate matter concentration acquisition module, data are wirelessly sent to the cloud platform through the console, and then the data are analyzed and processed through a dust concentration anomaly detection algorithm embedded in the cloud platform, so that the dust concentration anomaly detection accuracy is improved. Normal operation conditions of the bag-type dust collector are determined, if abnormity occurs, an operator can rapidly position and overhaul the bag-type dust collector, and a large amount of manpower and material resources are saved.

The invention discloses a method, a system, a medium, equipment and a product for adjusting gas particulate matters of a sulfur hexafluoride gas insulated switchgear, and belongs to the technical field of adjustment of gas particulate matters of sulfur hexafluoride gas insulated switchgears. The method comprises the following steps: carrying out data preprocessing on obtained gas characteristic data of the sulfur hexafluoride gas insulated switchgear in a fixed time window to obtain preprocessed gas characteristic data, carrying out particulate matter concentration prediction by utilizing a trained gas characteristic prediction model to obtain predicted particulate matter concentration, and calculating a prediction slope; according to the obtained real-time gas flow rate and the real-time pressure of the sulfur hexafluoride gas insulated switchgear, a GPR online learning mechanism is utilized to obtain a self-adaptive particulate matter accumulation threshold value; and if the predicted slope is greater than or equal to a slope threshold value or the predicted particulate matter concentration is greater than or equal to a self-adaptive particulate matter accumulation threshold value, adjusting the gas flow rate of the sulfur hexafluoride gas insulated switchgear by using a GPR-based Bayesian optimization algorithm.

The invention relates to the field of detection equipment, in particular to a dust concentration detection device based on multi-light-path fusion. Comprising a straight-through detection cavity, a laser and a detector, the straight-through type detection cavity is used for circulation of gas to be detected; the laser is fixed outside the straight-through type detection cavity and is used for emitting laser to the gas to be detected in the straight-through type detection cavity; the detector is fixed outside the straight-through type detection cavity and is used for receiving transmission light or scattered light obtained after the laser acts with the gas to be detected; the detector comprises a transmission detector and more than one scattering detector; the method for detecting the particulate matter concentration by the dust concentration detection device comprises the step of inputting intensity data obtained by all the detectors into a pre-trained regression model so as to obtain the particulate matter concentration C. Dustproof performance of the detection device is realized through structures such as the straight-through cavity, the measuring range is greatly increased through fusion of multiple optical paths, automatic calibration is realized through dynamic calibration, and the maintenance cost is reduced.

The application relates to a detector calibration method, a calibration device, a battery, a vehicle and a storage medium. The method comprises the following steps: collecting N particulate matter concentration information in a current environment in a preset period; determining a concentration change trend according to the N particulate matter concentration information; determining a noise value based on the concentration change trend and the N particulate matter concentration information, so that the detector can be accurately calibrated according to the noise value.

The invention relates to an air pollution identification method and a vehicle, features corresponding to abrupt change events in multi-modal data are encoded based on an attention mechanism to obtain pollution abrupt change event information, and the sudden change trend of air pollution can be accurately identified from high-frequency environmental data. According to the method, particulate matter concentration data in the multi-modal data are used as a drift term, other data in the multi-modal data are used as diffusion terms, pollution trend information is obtained through prediction, and prospective prediction and uncertainty quantification of the pollution evolution trend are achieved. A sensor map is coded based on an attention mechanism to obtain pollution spatial distribution information, and non-uniformity perception of pollution source direction and spatial distribution is realized. Therefore, through multi-modal fusion and time-space collaborative perception, the problems of recognition lag and misjudgment caused by dependence on single sensor data are effectively solved, so that a relatively complex air pollution scene can be dealt with, and the accuracy and speed of air pollution recognition are improved.

This invention provides a particulate matter gradient concentration generation system, belonging to the technical field of testing equipment. It includes a particulate matter generating device, a particulate matter conveying device, and a particulate matter sampling device. The particulate matter generating device includes a housing and a first pipe. The housing has a support platform for combustion of a combustible material, and the first pipe has an air inlet for balancing the air pressure within the housing. The particulate matter conveying device includes a particulate matter pipe and a transition chamber. The particulate matter sampling device is located on the particulate matter pipe. By controlling the amount of combustible material burning on the support platform, the concentration of particulate matter entering the particulate matter pipe can be controlled. The particulate matter concentration in the particulate matter pipe is sampled by the particulate matter sampling device, and the concentration in the particulate matter pipe is detected by a standard particulate matter monitor. This allows for a gradient change in the particulate matter concentration within the particulate matter pipe, thus enabling testing of the particulate matter monitor under test at different concentrations.

The invention relates to the field of building energy conservation, in particular to a control method and system for a flat-urgent combined ventilation air conditioning unit and a storage medium. The control method comprises the steps that the set air supply amount of an air supply outlet of the unit into a room is set, and a particulate matter concentration control target C0 is set; detecting particulate matter concentrations C1 and C2 in real time; if C1 is smaller than or equal to C0, the opening degree of the first air valve and the opening degree of the second air valve are fully closed, and the opening degree of the third air valve and the opening degree of the fourth air valve are fully opened; if C1 is larger than C0, the optimal air valve opening degree combination is solved by taking the minimum total resistance of air flowing through the bypass air duct and the deep filtering air duct as the target, and the air valve opening degree is adjusted to the optimal air valve opening degree; the current air supply amount is detected in real time, and the fan rotating speed is adjusted to the target rotating speed; if C2 is smaller than or equal to C0, the current air valve opening is kept for operation, and otherwise, the steps are repeated till C2 is smaller than or equal to C0. Accurate and stable output of the air supply amount is achieved, it can be guaranteed that the indoor air quality accurately meets the control requirement, and the operation energy consumption of the ventilation and air conditioning system can be remarkably reduced.

The invention relates to the technical field of environment air sampling monitoring, and discloses an air quality monitoring device with a self-cleaning function, the air quality monitoring device comprises a monitoring main body, a PM monitoring mechanism is arranged in the monitoring main body, the PM monitoring mechanism comprises a monitoring assembly, the monitoring assembly is fixedly installed in the monitoring main body, and the monitoring assembly is used for detecting an air PM value; the ventilation pipe penetrates through the monitoring main body and is used for communicating the monitoring assembly with external air; the air suction head is fixedly connected to the top of the ventilation pipe; communication and opening and closing of the breather pipe, the outer flow cavity and the inner detection cavity are controlled through the valve plate, when the breather pipe is communicated with the inner detection cavity, flowing air can be detected in the movable pipe, meanwhile, the inner detection cavity can be closed, the inner detection cavity forms a sealed detection environment, and at the moment, air in the inner detection cavity is subjected to closed detection through the monitor; two groups of detection conditions are set, and two groups of detection data are obtained, so that the detection accuracy of the concentration of the particulate matters in the air is further ensured.

The invention provides a particulate matter concentration detection device and a particulate matter concentration detection method. The particulate matter concentration detection device comprises a particulate matter density detector used for detecting the density of particulate matters in an environment to be monitored; the particulate matter separation unit is used for separating target particulate matters; a suction port of the air pump is communicated with the flow channel outlet of the particulate matter separation unit; the concentration detection unit is used for detecting the particulate matter concentration of the target particulate matter, the air pump is provided with a flow adjusting part, and the flow of the air pump can be adjusted through the flow adjusting part on the basis of the particulate matter density detected by the particulate matter density detector.

The invention relates to the field of pollution detection, in particular to a pollutant digital probe detector. The pollutant digital probe detector comprises a fixed cylinder, a cover body is detachably connected to the fixed cylinder, and a mounting rod is fixedly connected in the fixed cylinder; the air storage cylinders are arranged, the mounting rod is provided with mounting grooves corresponding to the air storage cylinders, and each mounting groove corresponds to one air storage cylinder; and an air exhaust assembly and the like. When the VOC concentration is detected, a particulate matter concentration sensor is arranged in a gas storage cylinder, the particulate matter concentration in sample gas is synchronously detected through a light scattering method, particulate matter concentration detection is achieved while particulate matter is filtered, the comprehensiveness of detection parameters is improved, the running state of the activated carbon adsorption bed can be comprehensively evaluated, and the VOC concentration can be accurately detected through step-by-step filtering. Particulate matters entering the sensor along with sample gas are reduced, the pollution risk of the sensor is remarkably reduced, and the long-term precision and stability of online continuous detection of the VOC concentration sensor are ensured.