308 results about "Air pollutants" patented technology

The invention belongs to the technical field of environmental science, and relates to a multi-source satellite remote sensing cooperative atmospheric pollution monitoring and dynamic tracing method and system. The method comprises the following steps: collecting, processing and screening multi-source data; constructing a pollutant diffusion model, and generating pollutant concentration distribution data; dynamically adjusting the diffusion coefficient and attenuation rate of the pollutant diffusion model, and generating a pollutant concentration distribution thermodynamic diagram; performing space matching, and iteratively updating the diffusion coefficient and the attenuation rate of the pollutant diffusion model until matching parameters meet set conditions; and verifying pollutant concentration distribution data in the pollutant concentration distribution thermodynamic diagram. According to the invention, parameters of the pollutant diffusion model can be dynamically adjusted and optimized based on real-time meteorological data, the accuracy of pollutant concentration distribution data generated by the pollutant diffusion model is ensured, and real-time tracking and accurate positioning of a pollution source are realized; the monitoring precision is obviously improved; the cooperative monitoring efficiency is improved, and comprehensive monitoring of atmospheric pollutants is realized.

Techniques for operating an environmental sensing system are described. In an example, a cloud-based server system receives a first indication that a first type of air pollutant is present within a first structure from a first plurality of indoor air quality (IAQ) sensing devices positioned within the first structure. A second structure within a predefined distance to the first structure is then identified by the server system. The server system then determines that the first type of air pollutant is not present within the second structure. The server system then causes a second plurality of IAQ sensing devices positioned within the second structure to change an operating mode from a normal sensitivity mode to a high-sensitivity mode.

The invention relates to the technical field of selenium-rich food safety, and discloses a selenium-rich food safety supervision system based on a block chain. The system comprises a production environment monitoring unit which acquires multi-dimensional environment parameters such as soil selenium content, air pollutant index and temperature and humidity of a selenium-rich food raw material planting base and a production and processing place in real time, and generates a comprehensive environment safety index through dynamic acquisition and fusion processing; and when the index is lower than a preset threshold value, a block chain alarm instruction is triggered and a supply chain tracing unit is started. And the supply chain tracing unit responds to the instruction, carries out block chain evidence storage on full-link circulation information (including supplier qualification certificates, logistics temperature control records and the like) of the selenium-rich food from raw material collection to terminal sales, and analyzes the evidence storage information to construct a complete link map. And the quality verification unit synchronously receives the environmental safety index and the supply chain link map, performs multi-source information cross verification in combination with selenium-rich food real-time spectral feature detection data, outputs a quality compliance judgment result and generates a block chain verification label.

The invention discloses a pollutant anomaly detection method based on multi-pollutant collaboration and spatial-temporal feature fusion. The method comprises the following steps: S1, constructing a multi-pollutant sensor network data set; s2, cleaning and preprocessing the multi-pollutant sensor network data set in the research area; s3, learning a directed graph adjacency matrix of the single-pollutant sensor network for the multi-pollutant sensor network data set through Bayesian variation inference, and modeling an asymmetric causal relationship between sensors; s4, processing the sensor nodes of the predicted target location by using a single-pollutant spatial feature extraction module, and performing spatial feature fusion among multiple pollutants based on a hierarchical attention mechanism; s5, performing multi-pollutant data prediction on the multi-pollutant sensor network data of the predicted target location by fusing spatial features and a directed graph by using a spatial-temporal feature fusion module; and S6, performing anomaly detection based on an anomaly score and a dynamic update threshold value of the air pollutant monitoring value generated by cooperation of multiple pollutants.

The invention relates to the technical field of environmental pollutant detection, in particular to a straw open-air incineration pollutant emission quantitative method based on multi-source data fusion, which comprises the following steps: based on air pollutant monitoring data, calling a sensor network to obtain PM2.5, CO and NOX concentrations, analyzing fire point heat radiation intensity, collecting meteorological data, and calculating the concentration of PM2.5, CO and NOX; and obtaining a pollution source data set by combining the time sequence change of the fire points and the meteorological parameters. By integrating an environment monitoring sensor, satellite remote sensing and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved, the combination of fire point intensity and pollutant concentration time sequence data is optimized, the evaluation of fire point and pollution hot spot areas and the comprehensive analysis of wind directions and meteorological conditions are optimized, the accuracy of a pollutant diffusion model is improved, and the pollution source diffusion efficiency is improved. The pollutant release amount is dynamically adjusted, and the diffusion model is corrected by using a geographic information system, so that the scientificity of pollution prediction and emission intensity evaluation is enhanced, and more accurate pollution prevention and control decision support is provided.

The invention belongs to the field of air pollutant prediction, discloses a multiple air pollutant prediction method based on a multi-scale spatio-temporal spectrum feature fusion framework, and solves the technical problem of low prediction accuracy of multiple air pollutants caused by single air pollutant prediction type and neglect of a dynamic dependency relationship among different pollutants in the prior art. The method comprises the following steps of: constructing an air quality characteristic data set by acquiring collected data of a plurality of air quality monitoring stations in a city; preprocessing the air quality characteristic data set; constructing a multi-scale space-time spectrum feature fusion model; inputting the preprocessed air quality feature data set into a multi-scale spatio-temporal spectrum feature fusion model for training; and predicting the pollutant concentration of each station in a future time period by using the trained multi-scale spatio-temporal spectrum feature fusion model. According to the method, the dynamic dependency relationship among various pollutants is explicitly modeled when the model is constructed, and the prediction precision of various air pollutants is effectively improved.

The invention relates to an atmospheric pollutant early warning method and system based on multi-source isomerism, and the method comprises the steps: obtaining multi-source isomerism atmospheric pollutant data, carrying out the data arrangement of the atmospheric pollutant data, and obtaining the arranged atmospheric pollutant data; carrying out unified representation on the sorted air pollutant data to obtain air pollutant data subjected to unified representation, and carrying out intelligent detection by adopting a gridding edge calculation method according to the air pollutant data subjected to unified representation to obtain an air pollutant detection result; according to the detection result of the atmospheric pollutants, performing forecasting and early warning by adopting a method based on big data analysis to obtain a forecasting and early warning result of the atmospheric pollutants; and the forecasting and early warning result of the atmospheric pollutants is displayed through an artificial intelligence GIS platform, so that the purposes of accurately monitoring, forecasting and early warning the atmospheric pollutants, improving the atmospheric pollution treatment level, assisting in environmental protection decision making and promoting development of related technologies are achieved.

Systems, methods, and computer-readable media for calibrating air quality sensors are provided. A sensor node includes a sensor node printed circuit board, a sensor module, and a communication module. The sensor node printed circuit board manages power of the sensor node circuitry, the sensor module, and the communication module such that power is provided from a primary power supply supplemented by a secondary power supply. The sensor module includes a plurality of air quality sensors to measure the concentration of air pollutants. The sensor module may be replaceable. The communication module may communicate air quality measurements to and receive configurations from a data management platform, which may perform processes to improve the accuracy of the air quality measurements.

The invention relates to the technical field of air pollution prediction, and provides an air pollution prediction system and method based on meteorological data. The data acquisition and fusion module is used for acquiring meteorological data, air pollutant data and traffic data and carrying out fusion processing on the data; the feature extraction module is used for filling missing data, normalizing the data and extracting features; the pollution prediction module is used for carrying out short-term and long-term prediction on air pollution based on the processed data, and carrying out visual display and air pollution early warning notification on a prediction result; and a model integration module. By integrating data of a weather station, an air pollution monitoring station, a traffic monitoring system and satellite remote sensing equipment, the system realizes comprehensive monitoring of air pollution. The spatial-temporal data fusion unit is used for performing data interpolation and consistency processing, so that the coordination of data from different sources is ensured, more comprehensive and accurate input data is provided, and the reliability of a prediction result is improved.

The present invention relates to the technical field of air quality health risk assessment, and discloses a health index assessment method based on composite exposure of multiple pollutants, including the following steps: S1: collecting air pollution, meteorology, hospitalization, and cause of death data, obtaining pollutant, meteorological factor, hospitalization, and death daily value data, and obtaining the 0-2 day moving average concentration of pollutants and the daily average value of meteorological factors; S2: selecting pollutants with statistical significance as pollutants for composite exposure analysis of multiple pollutants; S3: substituting the daily value data of hospitalization and death, the daily average value of meteorological factors, and the 0-2 day moving average concentration of selected pollutants into a multi-pollution model, a Bayesian weighted model, and a mixture analysis model to calculate the corresponding excess risk; S4: respectively calculating the pending CEHI under different air pollutant composite exposures corresponding to the excess risk under different models; S5: selecting a reasonable pending CEHI as the formal CEHI. The present invention can obtain a more accurate and comprehensive joint exposure health index.

The invention discloses a multi-modal air pollutant prediction method and device, equipment, a medium and a product, and relates to the technical field of environmental monitoring, the method comprises the following steps: carrying out preprocessing and standardization processing on to-be-processed multi-modal data to obtain preprocessed multi-modal data; the preprocessed multi-modal data comprises atmospheric pollutant concentration monitoring station data, emission source data and meteorological field data at the current moment in the target area; splicing the atmospheric pollutant concentration monitoring station data at the previous moment and the atmospheric pollutant concentration monitoring station data at the current moment along the time dimension, and extracting the spliced data features to obtain a station position-time-pollutant concentration feature vector; fusing the extracted feature fusion vector with the feature vector of station position-time-pollutant concentration to obtain a multi-modal feature vector; and inputting the multi-modal feature vector into a pre-trained deep learning network model to obtain a prediction result. According to the invention, the pollutant prediction accuracy can be improved.

The invention provides a risk tracing method of air pollution to health, and belongs to the technical field of environmental health, and the method comprises the steps: recognizing main air pollutants in a target region and corresponding air pollutant existence sources, obtaining the topographic condition of the target region, carrying out the range expansion of the target region according to the topographic condition, and obtaining the risk tracing result of the target region. Collecting pollutant concentration data and biological health conditions in the expanded area, performing connection analysis, and determining a biomarker related to air pollution according to a connection analysis result; according to the method, the exposure relationship between the biomarker and the air pollutant is evaluated, a health statistical model is constructed according to the biomarker, the pollutant concentration and the exposure relationship, and then health risks of different pollutants under different exposure levels are predicted, so that risk tracing of air pollution to health is realized, and the influence of air pollution on human health is more comprehensively understood. And high-risk crowds and pollution sources are identified.

The invention relates to the field of pollutant monitoring, in particular to an indoor decoration harmful substance monitoring and early warning method and system based on multi-sensor analysis. The method comprises the following steps that decoration whole-process parameter collection is carried out based on multiple sensor nodes, regional parameter distribution fitting is carried out, and an all-weather monitoring reference database is constructed; performing air pollutant identification marking on the all-weather monitoring reference database, and performing multi-time-point concentration change evolution analysis to obtain a multi-time-point concentration distribution evolution model; carrying out dynamic pollutant diffusion mining on the multi-time-point concentration distribution evolution model, carrying out combined pollution evaluation calculation, and constructing a multi-pollutant combined diffusion risk chain; dynamic interpolation modeling is carried out on the multi-time-point concentration distribution evolution model, micro-scale space-time disturbance reconstruction is carried out, and a pedestrian disturbance space-time diffusion model is constructed. According to the invention, through accurate and efficient risk assessment of indoor decoration harmful substances, dynamic early warning is carried out, and the safety of indoor decoration is improved.

The invention discloses a regional air pollutant traceability monitoring and forecasting model and a regulation and control method. Multi-source data such as meteorology, pollutant concentration and pollution source emission in an area are collected and preprocessed, then a model is constructed and trained based on a machine learning or deep learning algorithm, and performance is improved through feature engineering, model selection and training optimization. The trained model is utilized to forecast pollutant concentration, traceability analysis is carried out, results are visualized, emission reduction targets and regulation and control measures are formulated for a target area according to traceability and forecast simulation data, for example, industrial structure optimization and traffic control measures are implemented for different pollution sources, and the implementation effect is evaluated. Meanwhile, the model is evaluated regularly, the model is updated according to a result, parameters are adjusted, data are increased or an algorithm is improved, so that the accuracy and reliability of the model are continuously improved, accurate monitoring, effective source tracing, accurate forecasting and scientific regulation and control of regional air pollutants are achieved, and air quality improvement is assisted.

The invention belongs to the technical field of environment-friendly treatment, and provides a treatment process for natural gas combustion waste gas generated by zinc chloride production based on zinc ash and zinc slag and acid mist waste gas generated by regenerated acid decoction, which comprises the following steps: S1, introducing the natural gas combustion waste gas and the acid mist waste gas into a spray tower, carrying out dynamic alkali neutralization treatment and cooling treatment on the basis of an intelligent pH value regulation and control method to obtain primary purified gas; s2, the primary purified gas enters primary plasma dedusting and demisting equipment through a pipeline to be treated, and secondary purified gas is obtained; and S3, the secondary purified gas enters secondary plasma dedusting and demisting equipment to be treated, and final treated tail gas is obtained. According to the invention, the chemical neutralization based on the spray tower and the two-stage plasma deep cracking are combined, the treated waste gas meets the emission limit value in the secondary standard in the Integrated Emission Standard of Air Pollutants, the treatment efficiency is high, the medicament consumption is low, the energy consumption is low, and the problems in the prior art are effectively solved.

The invention provides a power plant flue gas parameter prediction method and device, and belongs to the technical field of power plant prediction.The method comprises the steps that historical power plant operation parameters corresponding to multiple moments are imported, and an original data set is constructed through the historical power plant operation parameters; preprocessing the original data set to obtain a preprocessed data set; performing delay analysis on the preprocessed data set to obtain a data set after delay processing; and carrying out feature analysis and prediction modeling on the data set after delay processing to obtain a prediction value of the target. According to the method, accurate and rapid dynamic prediction of the flue gas parameters of the power plant is achieved, the phenomena of large inertia, large delay and large lag of the air pollutant parameters of the power plant are effectively solved, optimization guidance suggestions are provided for air pollutant control operation personnel of the power plant, meanwhile, the effects of pre-control and lag elimination are achieved, and the control efficiency of the power plant is improved. And the method is of great significance to air pollutant optimization control, energy conservation and emission reduction of a power plant.

The invention relates to a multi-model fusion method for analyzing PM2.5 source contribution and disease burden thereof, which comprises the following steps of: building and verifying an air quality simulation system of a corresponding region, simulating the concentration of air pollutants and PM2.5 components of the corresponding region in recent 10 years by using a WRF-CMAQ model, and discussing the spatial and temporal distribution characteristics of the pollutant concentration; step 2, analyzing a PM2.5 pollution source of a corresponding region, and based on an HYSPLIT model and a high-resolution global emission list; step 3, estimating PM2.5 related disease burden and source contribution thereof, and quantifying premature death and health economic loss thereof caused by PM2.5 in recent ten years in a region by utilizing PM2.5 concentration data simulated by a WRF-CMAQ model and combining a comprehensive exposure-response model and a statistical life value method; the method can help to formulate accurate and effective PM2.5 control countermeasures, improve air quality and protect people's health, and provides a theoretical basis for realizing joint prevention and joint control and sustainable development of polluted areas.

A method for constructing a relatively optimal air quality health index. The method comprises: acquiring air pollutants in a region, internal component data of the air pollutants, and health outcome data of the local population; by using a pseudo-Poisson distribution link function, sequentially constructing correlations among the air pollutants, the internal component data of the air pollutants, corresponding daily average concentrations, daily total population mortality, daily outpatient visits and daily inpatient admissions; constructing a multi-pollutant and multi-component Bayesian model on the basis of the air pollutants with the most severely harmful effects during hysteresis, the concentrations of components of the air pollutants, and health outcomes; estimating risk coefficients and weights of multiple pollutants and components, and calculating an excess health risk; establishing a comprehensive air quality health index, and on the basis of different health outcome data of the local population, calculating a plurality of comprehensive air quality health indexes which reflect the health risks associated with the different health outcome data of the local population; and performing comparative analysis on the plurality of comprehensive air quality health indexes, so as to select a relatively optimal air quality health index.

To provide an indoor air cleaning system capable of cleaning an air pollution state of an indoor region.SOLUTION: A gas detection module comprises a microcontroller and a central control communication interface component. The microcontroller performs calculations based on air pollution data, and outputs an adjusted control signal. An air purification device comprises a fan, a filter component, and a drive control assembly. The central control unit provides a control command to the gas detection module via wired or wireless communication, controls the fan to direct air pollution to the filter component, and purifies an air pollution state in an indoor region on the basis of the 24-hour cumulative detection count of PM2.5, which is a floated particulate matter, inhaled by the plurality of gas detection modules, and an indoor area space required for one air purification device, so that the detected air pollution data satisfies clean room ZAPClean room 1 to 9 grade requirements.SELECTED DRAWING: Figure 2A

The invention relates to the technical field of energy and environmental protection, and discloses a boiler environmental protection island system based on an energy collaborative optimization technology, which comprises a data acquisition module, a data analysis module, an optimization decision module, a command execution module and a feedback adjustment module, a data analysis module in the system uses a formula to calculate a data set collected by the data analysis module, a boiler emission pollution index Qrl and a post-denitration air pollution index Mej are obtained through calculation, a comprehensive environmental protection coefficient Uqm is obtained through calculation by combining the two data results, and an optimization decision module optimizes the system according to the comprehensive environmental protection coefficient Uqm. The command execution module converts a data result and executes an operation command, the feedback adjustment module is connected with the data analysis module again after receiving information to form closed-loop control, and the system solves the problem that air pollutants generated by a traditional boiler pollute the environment by continuously circulating the system process.

The invention discloses an air quality data screening method and system based on multi-factor deviation analysis, and solves the problem that complex spatio-temporal data deviation is difficult to process by a traditional method. The method comprises the following steps: acquiring multi-city multi-time-dimension air pollutant monitoring values, meteorological data, topographic data and monitoring station metadata, performing data cleaning through three-level quality control, and constructing a spatio-temporal data cube; dynamically establishing a regional reference value, calculating a combined deviation, and identifying a deviation mode through a space-time DBSCAN method; a city deviation feature vector and a provincial deviation network are constructed, and a graph convolution network is used for sorting; establishing a multi-layer cause and effect graph, and diagnosing deviation root causes by adopting a PC algorithm and a Shapley value decomposition technology; and defining a Markov decision process, and dynamically optimizing a correction strategy. According to the method, the composite deviation can be accurately quantified, root causes such as regional pollution, equipment faults or meteorological interference can be distinguished, and data support is provided for regional collaborative governance.

The invention discloses a heterojunction composite material and a preparation method and application thereof, and belongs to the technical field of catalysts, and the preparation method comprises the following steps: dispersing LaFeO3 and g-C3N4 in water, adding CuCl2, uniformly stirring, adding a sodium citrate aqueous solution, continuously stirring for 12-24 hours, centrifuging, washing and drying to obtain the heterojunction composite material; the mass ratio of the LaFeO3 to the g-C3N4 to the CuCl2 is (10 to 30) to (70 to 90) to (10 to 30). According to the invention, two narrow-band gap semiconductors (LaFeO3 and Cu2O) and a wide-band gap semiconductor (g-C3N4) are compounded to form the composite material with the double-heterojunction structure. As a photocatalyst, the material can be effectively applied to pollution control, and has excellent capabilities of removing organic water pollutants and air pollutants and sterilizing.

A computer implemented method of calculating exposure to air pollutants is disclosed. The method comprises receiving a path comprising a sequence of path locations, receiving or retrieving concentration maps corresponding to one or more air pollutants, each concentration map comprising modelled concentration values of the corresponding air pollutant at each of a plurality of model locations, for each given air pollutant, determining a concentration profile of the given air pollutant as a function of distance along the path by: in response to a pair of sequential path locations are separated by more than a target spacing, inserting one or more interpolation locations between that pair of path locations, so every section of the path longer than the target spacing includes at least one path location or interpolation location, for each interpolation location and each path location, interpolating a concentration value corresponding to that interpolation location or path location based on modelled concentration values of the two or more model locations closest to that interpolation location or path location, storing or outputting the concentration profiles of the one or more air pollutants.

The present invention belongs to the technical field of analysis of the causes of air pollution, and discloses a method and system for identifying and classifying risk points of air pollution caused by coal consumption, including obtaining multi-source data; extracting the features of the multi-source data to form data feature samples, using the ground monitoring data at the current moment of the grid point where the data feature sample is located as a label to train a deep learning model; using the trained model to predict the surface concentration of air pollutants; based on the prediction results, calculating the mean and standard deviation of the pollutant concentration of each grid point, screening out risk points; establishing a buffer zone centered on the risk points, analyzing the distribution characteristics of POIs in the buffer zone, and identifying and classifying pollution sources. The present invention uses POI points to achieve accurate identification of risk points of air pollution caused by coal consumption, provides data support for the prevention and control of heavy pollution weather, and promotes scientific emission reduction and refined governance.

The invention belongs to the technical field of air pollutant traceability. The invention provides an air pollution prediction method and system based on artificial intelligence and a mechanism model, and the method comprises the steps: extracting geographic features and time dependency relationship features, fusing the geographic features and the time dependency relationship features, outputting potential calculation factors, and calculating the weight of each factor; a high weight factor with the absolute value of the weight larger than a set threshold value is fused into the mechanism model, the reference emission rate of the mechanism model serves as basic input data, the selected high weight factor and the weight are combined to calculate the corrected gridding emission rate, and an improved mechanism model with a dynamic emission source list is formed; the pollutant concentration is predicted through the improved mechanism model, and the main pollution source is determined based on pollution source data and potential calculation factors in the database; according to the method, various factors involved in the atmospheric pollution forming process are fully considered, and the prediction precision is improved.

The invention relates to the technical field of atmospheric pollutant removal, in particular to an atmospheric pollutant removal device which comprises a removal tank and an impurity removal plate arranged on the inner side of the removal tank, one side of the removal tank is provided with an air inlet pipe, one end of the air inlet pipe is provided with a pressurizing pipe, the pressurizing pipe is connected with the removal tank, and the impurity removal plate is arranged on the inner side of the removal tank. And a preliminary filtering plate is arranged on the inner side of the air inlet pipe. Through the arrangement of the preliminary filter plate, preliminary impurity filtration can be performed on atmosphere conveyed into the removal tank, so that the replacement time of the impurity removal plate can be prolonged, the use cost of the device is reduced, the disassembly and assembly steps between the preliminary filter plate and the removal tank are simplified through the arrangement of the connecting assembly, the replacement efficiency of the preliminary filter plate is improved, and the use cost of the device is reduced. By arranging the filter plate cleaning assembly, dust impurities filtered on the preliminary filter plate can be cleaned, so that the replacement time of the preliminary filter plate can be prolonged, and the removal cost of atmospheric pollutants is reduced.

Systems, methods, and non-transitory computer-readable media for continuously monitoring residential air quality and providing a trend based analysis regarding various air pollutants are presented herein. The system comprises an air quality monitor located in a residential house, wherein the air quality monitor is configured to measure the level of an air pollutant. The system also includes a server that is communicatively coupled to the air quality monitor, wherein the server is configured to generate a unique environmental fingerprint associated with the residential house.

This invention discloses an outdoor air pollutant treatment device, belonging to the field of air pollutant treatment technology. It includes a mounting frame, on the upper side of which multiple motor protective shells are fixedly mounted. A power motor is fixedly connected inside each motor protective shell, and multiple axial flow fans are fixedly connected to the outer side of the output shaft of each power motor. A fixed frame is located on the outer side of the motor protective shells, and a filter element is fixedly mounted within the fixed frame. A cleaning assembly for the filter element is also installed within the fixed frame, and the cleaning assembly includes a positioning plate fixedly mounted on the outer side of the filter element. During the air pollutant treatment process, the cleaning box and cleaning plate can clean the pollutants accumulated on the outer side of the filter element, improving the filter element's service life. Furthermore, during this process, the cleaning plate moves up and down relative to the cleaning box, allowing for better cleaning of the pollutants accumulated on the outer side of the filter element.

The invention provides an air pollutant intelligent monitoring system and monitoring method, and relates to the field of environment air quality detection.The system comprises a basic information module used for obtaining basic user information of a user and the area category of a target monitoring area; the determining and monitoring module is used for determining monitoring pollutants according to the basic user information and the region category; the real-time monitoring module is used for acquiring the pollutant content of the monitoring pollutants in each target monitoring area; the real-time information module is used for acquiring real-time user information of the user; the pollution index module is used for determining the air pollution index of each target monitoring area according to the real-time user information and the pollutant content; the change index module is used for determining an air pollution change index according to the air pollution index; and the monitoring report module is used for generating a monitoring report according to the air pollution index and the air pollution change index. According to the invention, the intelligence and accuracy of air pollutant monitoring can be improved.

The utility model provides an air pollutant sampling device for environmental pollution, which comprises a triangular bracket and a detector, a base is arranged at the top of the triangular bracket, a columnar table is movably arranged on the base, a first connecting seat is fixed at the top of the columnar table, a plurality of clamping columns are annularly fixed on the first connecting seat, and the clamping columns are fixed on the first connecting seat. A second connecting base is fixed to the bottom of the detector, a plurality of clamping grooves are annularly formed in the bottom of the second connecting base, the clamping columns are embedded into the clamping grooves, a worm wheel is installed on the columnar table, two limiting bases are installed on the base at intervals, a worm is movably arranged between the two limiting bases, and a clamping column is installed on the worm wheel. According to the utility model, the problems that when the orientation of the extraction opening of the detector needs to be adjusted according to the wind direction, a worker needs to carry the tripod and the detector as a whole, and time and labor are wasted are solved.