419 results about "Particulate concentration" patented technology

The invention is suitable for the technical field of flying dust monitoring, and provides a road construction site all-weather flying dust online monitoring method and system, and the method comprises the following steps: collecting flying dust data and wind power information through a sensor; the particulate matter concentration is judged, and the particulate matter concentration exceeding the standard and the corresponding coordinate position, wind speed data and wind direction data are screened out; determining a flying dust source area according to the coordinate position, the wind speed data and the wind direction data; a road transportation monitoring video is called, and the area where the transportation vehicle is located is determined according to the time information and the road transportation monitoring video; a road construction map is called, and the area where the transport vehicle is located is marked in the road construction map; and comparing and analyzing the flying dust source area with a road construction map, determining a potential flying dust source position, and sending the potential flying dust source position to an inspection terminal. According to the invention, the flying dust pollution source can be accurately tracked, and the inspection personnel can quickly position the potential flying dust pollution source according to the information.

The invention relates to the technical field of urban road construction, in particular to an intelligent flying dust monitoring and dust falling control system for urban road construction, which comprises a flying dust monitoring module, a dust falling control module, a dust falling control module and a dust falling control module, the data fusion processing center is used for processing the monitoring data and generating a dust fall control instruction; the intelligent dust falling execution module is used for triggering a dust falling device according to the instruction; the cloud platform and the mobile terminal are used for storing the monitoring data, visually displaying the flying dust thermodynamic diagram and pushing standard exceeding early warning information to management personnel; data are collected in real time through the flying dust monitoring module, a flying dust pollution source and the diffusion trend of the flying dust pollution source are recognized in combination with the dynamic analysis and prediction capacity of the data fusion processing center, the intelligent dust falling execution module is used for triggering dust falling measures, and meanwhile remote monitoring and early warning are achieved through the cloud platform and the mobile terminal. The problem of response lag in a traditional method is solved, and the control efficiency is improved.

The invention discloses a small waste incineration full-automatic control system and method, and particularly relates to the technical field of solid waste treatment.The method comprises the following steps that waste throwing is recognized through infrared induction, a preheating device is started to heat a combustion chamber, and the heating intensity is adjusted in real time; when the temperature reaches a preset threshold value, the air volume, the feeding time and the combustion time are controlled according to the garbage type, and stable combustion is achieved; the concentration of carbon monoxide, nitric oxide and particulate matter in tail gas is monitored in the whole process, and combustion parameters are automatically adjusted when the emission limit value is close; after combustion is finished, slag is automatically discharged according to the amount of slag; meanwhile, temperature and emission data are collected and uploaded to a remote terminal, and full-process intelligent control and environment-friendly management are achieved; the whole-process automatic control is realized, and the manual dependence is reduced; combustion efficiency and adaptability are improved through an intelligent model; an emission prediction and feedback mechanism is introduced to ensure that the tail gas is environmentally friendly and reaches the standard, and the system stability and the intelligent level are enhanced.

The invention relates to the field of meteorological observation management, and discloses a low-altitude meteorological observation equipment operation and maintenance management method and system based on the Internet of Things, and the system comprises a meteorological sensor array module which is used for collecting low-altitude atmospheric environment parameters as observation data, including temperature, humidity, air pressure, wind speed, wind direction and particle concentration; the Internet of Things communication module is used for transmitting observation data to the edge computing node module in real time; the edge computing node module is used for performing local preprocessing on original observation data, including data filtering, abnormal value elimination and compression; the cloud operation and maintenance management platform is used for storing and analyzing the preprocessed observation data, judging an equipment state according to an analysis result, and generating an operation and maintenance instruction according to the equipment state; and the control module is used for receiving the alarm information and remotely controlling start and stop of equipment. According to the technical scheme, the safety of the low-altitude meteorological observation equipment can be improved.

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 provides a cage-rearing chicken abnormal behavior detection and early warning system and method based on image analysis, and relates to the technical field of computer vision. The ammonia concentration can be indirectly speculated through environmental pollution influence indexes, the cost of an ammonia concentration monitoring sensor can be saved, and the detection accuracy is improved. A stimulation influence index is introduced to further analyze the influence of burst noise intensity and particulate matter concentration on lower-layer cage rearing chickens, and the spatial relevance and the time dynamic nature of multi-point abnormal behavior detection results are analyzed, so that real-time dynamic evaluation can be carried out aiming at an instantaneous high-frequency stimulation event; the system is suitable for large-scale abnormal behavior recognition and management adjustment tasks in a high-density breeding scene, independent effects of different environment factors can be recognized, complex interaction effects of the environment factors can be revealed, early warning threshold values of cage rearing chickens of all grades are set, and early warning strategies of different grades are implemented based on the early warning threshold values.

The invention relates to the technical field of data processing, in particular to a building construction operation environment monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting the particle concentration and wind direction data of a construction site area; according to the positions between different monitoring points and the particulate matter concentration, a flying dust influence area and a plurality of flying dust source points are obtained; further obtaining the influence degree of each monitoring point on each flying dust source point; based on the influence degree of the flying dust source points on the monitoring points, the particulate matter concentration of each flying dust source point is corrected, and then the flying dust contribution amount of each flying dust source point on each monitoring point is obtained; fitting weights of all the flying dust source points to all the monitoring points are obtained according to the flying dust contribution amount of the flying dust source points on the monitoring points, then data fitting is carried out, and the predicted particle concentration of each monitoring point is obtained; and monitoring the building construction pollution condition according to the predicted particle concentration. The influence caused by the external environment can be eliminated, and the environmental pollution caused by the construction site construction can be accurately monitored.

The invention discloses a sand and dust weather satellite-ground cooperative monitoring method, and belongs to the technical field of atmospheric environment and meteorological disaster monitoring, and the method comprises the steps: calculating a particulate matter concentration change abnormal coefficient of each monitoring station according to a ground environment air quality automatic monitoring network; calculating visible light, intermediate infrared and thermal infrared indexes; extracting a satellite remote sensing monitoring index near each ground monitoring station, and comprehensively comparing ground monitoring to confirm an optimal threshold value of sand and dust discrimination of each index; and extracting a sand and dust weather distribution area, and calculating a sand and dust intensity index. According to the invention, by fusing ground environment air quality monitoring point data and a large-range satellite remote sensing monitoring result, a sand-dust weather integrated monitoring technology method model is comprehensively constructed, dynamic monitoring of regional large-range sand-dust weather distribution is realized, full-coverage sand-dust weather distribution judgment can be carried out on a large-range region, and real-time monitoring of the large-range region is realized. And the sand and dust weather transmission path and the influence thereof can be dynamically reflected.

The invention relates to a regional pollutant concentration prediction method and device and a storage medium, and is applied to the technical field of pollution monitoring. The method specifically comprises the following steps: acquiring historical pollutant concentration monitoring data and meteorological data of each single site, and predicting future fine particulate matter concentration and pollutant chemical components of the single site based on an EnvMulti-KAN model fused by KAN and Transform architectures; according to the prediction result of each single station in the target area, particle diffusion and convergence are simulated by adopting a multi-station fine particle diffusion model in combination with geographic information and interaction among the single stations, the pollutant area concentration is constructed, the area fine particle concentration is accurately predicted, and the view limitation of the single station is broken; according to the method, the single-site model and the multi-site model are cooperated, the concentration of the regional fine particulate matter is accurately predicted, the main components of the pollutants are synchronously and accurately predicted, and a demonstration is provided for atmospheric chemistry theoretical research.

The invention provides a flying dust online detection method and system based on laser, and the method comprises the following steps: obtaining laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data before and after the abnormality when the flying dust concentration of a flying dust monitoring region is monitored to be abnormal; calculating laser scattering energy and a particulate matter concentration change rate to obtain a flying dust concentration change coefficient; and judging an abnormal type according to the change coefficient and an abnormal identification criterion, and if the flying dust concentration in the region exceeds the standard, starting an early warning action to realize management of the flying dust monitoring region. According to the invention, the abnormal type of dust raising can be accurately identified, early warning is started in time, and the environment management and control capability of a dust raising monitoring area is improved.

The invention provides an intelligent fire-fighting method and system for a photovoltaic power station based on the Internet of Things, and the method comprises the steps: collecting temperature data through a distributed optical fiber temperature measurement unit, avoiding electromagnetic interference through optical signal conversion, analyzing the spectral feature difference between electro-arc light and combustion flame through a spectral feature separation unit, and carrying out the analysis of the spectral feature difference. Correlating the particulate matter concentration with the specific gas component through the multi-source coupling smoke sensing unit to obtain anti-interference data; space-time probability fusion is executed, and fire confidence is generated through alignment; a trigger response is calculated according to the combination of the fire confidence coefficient, the temperature rise trend slope and the smoke diffusion speed; running a self-adaptive parameter adjustment engine, and generating a sensing layer calibration coefficient and a decision rule optimization instruction; the parameter optimization instruction is issued to the sensing device, the calibration effect is verified, the influence of dust, electromagnetic interference and other factors is effectively avoided through the collected anti-interference data, the response is determined in combination with temperature rise and delay diffusion, the probability of false triggering is effectively reduced, and the accuracy of fire detection is improved.

The invention discloses a water pollution source tracking and atmospheric particulate distribution analysis method based on machine learning, comprising the following steps: S1, collecting and preprocessing multi-source data to generate an environment data set; s2, pollution diffusion characteristics are extracted, pollution sources are reasoned, and a water pollution tracking result is generated; s3, constructing a deep Bayesian neural network model, and outputting particulate matter concentration prediction and a confidence interval; s4, introducing a zebra optimization algorithm, and jointly optimizing a model structure and hyper-parameters; s5, fusing the water pollution tracking result and the environmental characteristics, and outputting a prediction result; s6, collecting real-time monitoring data, calculating a prediction residual error and feeding back the prediction residual error to the optimization model; and S7, generating a pollution thermodynamic diagram and a trend prediction diagram, and outputting a pollution level and an early warning suggestion. According to the invention, by fusing water pollution source tracking and atmospheric particulate distribution modeling, intelligent linkage analysis of pollution propagation path identification and air quality accurate prediction is realized.

The invention relates to the field of smoke detection, in particular to a portable smoke detection device easy to store, which comprises a smoke particulate matter light scattering detection module, an electrochemical gas sensing detection module and a central processing linkage optimization module, the smoke particulate matter light scattering detection module is used for acquiring a particle concentration signal value in to-be-detected air and realizing a particle concentration preliminary result through a particulate matter concentration algorithm; the electrochemical gas sensing detection module is used for acquiring a gas concentration signal value in air, and calculating and outputting a characteristic gas risk factor through a gas concentration algorithm; and the central processing linkage optimization module integrates and calculates a smoke comprehensive risk index through a linkage optimization algorithm according to the particle concentration preliminary result and the characteristic gas risk factor, and dynamically adjusts algorithm parameters according to real-time monitoring data to form closed-loop self-adaptive optimization.

The deep-sea mining pulp concentration and granularity measuring device comprises a main pipeline and an optical detection unit, a branch pipeline is arranged on the main pipeline, the optical detection unit comprises a light source and an image acquisition module arranged corresponding to the light source, and the light source and the image acquisition module are located on the two sides of the branch pipeline respectively; an array type ultrasonic sensor is further arranged on the main pipeline; and the image acquisition module and the array type ultrasonic sensor are connected with the control system. According to the invention, the concentration and granularity information of particles coexisting in three-phase media of tuberculosis particles, sediment particles and seawater is measured in a combined manner by adopting a plurality of measurement modes combining ultrasonic wave, light scattering and image methods, the detection accuracy is high, and the concentration (particle volume fraction) and granularity information of particles in ore pulp can be detected at the same time; the detection result is high in accuracy and the detection speed is high.

The invention relates to a method and device for determining the deposition rate of corrosion products in a reactor coolant. The method comprises the following steps: acquiring the particulate matter concentration of a structural material corrosion product in a liquid metal coolant; determining the deposition rate of the particulate matters in the corrosion product according to the particulate matter concentration and the stopping distance of the corrosion product; the stop distance is used to characterize the distance at which the corrosion product particles move to deposit with the coolant. By the adoption of the method, the stopping distance of the corrosion product can be considered when the deposition rate of the particulate matter is determined, and compared with a deposition rate determining mode that the corrosion product is assumed to be completely deposited at the release position of the structural material and the deposition lag condition is ignored in the related technology, the deposition rate can be determined more accurately. In addition, the deposition rate of the corrosion product particulate matter in the liquid metal coolant is determined in a one-dimensional solution mode, the deposition rate of the particulate matter in the corrosion product can be determined more efficiently, and the method is more suitable for engineering practical application.

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 relates to the technical field of factory dust removal, and discloses a factory dust removal energy efficiency optimization system based on digital twinning. The multi-source data sensing module obtains multi-dimensional operation data of target dust removal equipment, wherein the multi-dimensional operation data comprises air volume distribution, particulate matter concentration and energy consumption characteristic data. And the energy efficiency analysis decision module performs dynamic energy efficiency mapping processing on the data and inputs the data into the optimization strategy generation layer. The optimization strategy generation layer comprises a data cleaning module and a strategy construction module, the data cleaning module is used for cleaning and calibrating data, and the strategy construction module is used for generating an energy efficiency optimization strategy through three-layer collaborative modeling of state perception, energy efficiency optimization and strategy execution. The state sensing layer processes an operation sequence to obtain working condition associated feature data, the energy efficiency optimization layer performs modeling to determine optimization strategy feature data, and the strategy execution layer performs multi-level decision making according to the optimization strategy feature data. The system can accurately obtain equipment operation data, scientifically analyze and generate an optimization strategy, improve the energy efficiency of the dust removal equipment, guarantee the dust removal effect, improve the reliability and stability of the equipment, and reduce the energy consumption and the production cost.

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 provides a multi-parameter fusion filtering equipment health monitoring method and system. The method comprises the following steps: acquiring a filter membrane pressure harmonic component and a particulate matter concentration change rate, and aligning according to a time window to generate a characteristic matrix; calibrating a pressure component in a matrix through real-time data of a honeycomb array layout pressure sensor, and judging an effective blockage characteristic when the node detects that the pressure is abnormal; a health score and a blockage risk thermodynamic diagram are generated through probability inference in combination with the particulate matter concentration time sequence trend; and calculating a distribution deviation between the thermodynamic diagram and a real-time abnormal region, and dynamically adjusting a pressure harmonic weight coefficient and a topology verification threshold value, so that the thermodynamic diagram approaches actual distribution, and closed-loop monitoring is formed. According to the invention, dynamic sensing and closed-loop optimization of the blockage risk of the filter membrane are realized, and the multi-parameter fusion monitoring precision and the micro-blockage early warning timeliness are improved.

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 discloses an omnibearing evaluation method and system for a high-cleanliness filter, and relates to the technical field of filter evaluation, and the main scheme is that the method comprises the steps: collecting the filtering efficiency data, pressure drop data, particulate matter concentration data and working environment data of the filter, and the operation state data of a new energy vehicle; the method comprises the following steps: firstly, respectively calculating a corresponding filter filtering efficiency health index, a filter pressure drop health index, a filter particulate matter concentration index and a filter operation correlation health index, further calculating a filter comprehensive performance index, finally presetting a comprehensive threshold set, comparing the filter comprehensive performance index with the comprehensive threshold set, and calculating the filter operation correlation health index according to the preset comprehensive threshold set. Therefore, the current energy efficiency grade of the filter is judged.

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 relates to the technical field of heavy metal detection, and discloses a device and method for collecting heavy metal particles in waste gas, and the method comprises the following steps: introducing the waste gas into a detection area based on an induced draft fan, collecting the air inlet rate of unit time at the inlet of the induced draft fan, and starting a rotating assembly arranged in the detection area; the initial rotating speed of the rotating assembly is determined according to the air inlet amount, air flowing speeds at multiple positions in the detection area are collected, the distribution condition of waste gas in the detection area is judged according to the air flowing speeds, and whether the initial rotating speed is adjusted or not is judged according to the distribution condition; putting the bipolar electrostatic rod into a detection area, and determining the collection frequency of the bipolar electrostatic rod according to the particulate matter concentration in the detection area; the heavy metal concentration is obtained according to the content of multiple groups of heavy metal particles after collection. The problem that the detection precision is influenced by uneven airflow in a traditional method is solved, and the waste gas heavy metal particle collection efficiency and the detection accuracy are improved.

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 full-automatic operation, and discloses a central automatic disinfection spraying system, which comprises a spraying main station, a valve group, a nozzle, a sensor group, a monitoring device and a control device, the spraying main station is connected with the valve group and is used for storing and mixing a disinfectant; the valve group is connected with the nozzle and is used for controlling the flowing of the disinfectant; a plurality of nozzles are arranged; the sensor group comprises a plurality of temperature sensors, humidity sensors and particulate matter concentration sensors; the nozzle, the sensor group and the monitoring device are all arranged in a dairy product processing workshop, and the monitoring device is used for monitoring personnel condition data; the control device is connected with the spraying main station, the valve group, the nozzles, the sensor group and the monitoring device, and the control device comprises a determination module, a judgment module, an adjustment module and a storage module. According to the invention, full-automatic disinfection can be realized, each corner of a workshop is effectively covered, and the uniformity and thoroughness of the disinfection effect are ensured.

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 dust removal equipment, in particular to a two-dimensional airflow efficient electric dust remover. The two-dimensional airflow high-efficiency dust remover comprises an acquisition module used for acquiring particulate matter concentration data at a flue gas outlet of the two-dimensional airflow high-efficiency dust remover; and the control module is used for controlling the working state data of the two-dimensional airflow high-efficiency electric precipitator according to the particulate matter concentration data at the flue gas outlet of the two-dimensional airflow high-efficiency electric precipitator. The power supply voltage of the corona wire is adjusted in real time according to the particulate matter concentration data at the flue gas outlet of the two-dimensional airflow high-efficiency electric precipitator while the dust removal effect of the two-dimensional airflow high-efficiency electric precipitator is guaranteed, energy consumed by the electric precipitator is reduced to a great extent, electric energy and enterprise cost are saved, and the economic benefit is increased. And the intelligent degree of the two-dimensional airflow high-efficiency electric dust remover is greatly improved.