41 results about "Air quality management" patented technology

The invention relates to the technical field of self-adaptive control, in particular to an operation period building energy-saving low-carbon control method and system based on artificial intelligence, and the method comprises the following steps: collecting and analyzing the room use condition and illumination intensity, and obtaining an activity type recognition result; according to the invention, based on real-time analysis of room use conditions and illumination intensity, use modes of different areas are identified, and in combination with natural light changes and indoor activity requirements, required illumination intensity and color temperature adjustment parameters are dynamically calculated, so that accurate matching of illumination environments is realized. A dynamic feedback mechanism is established by monitoring the adjustment state of the lighting equipment in real time in combination with the change of the room use mode, the lighting deviation is adaptively corrected, and energy waste is avoided. By introducing reinforcement learning, the ventilation strategy can be continuously optimized through historical data and real-time feedback, personalized air quality management is achieved, and single adjustment is not executed based on a fixed threshold value.

This preferred embodiment pertains to a system designed to manage and optimize indoor air quality within a certain space. It employs a processor, multiple sensors, air dampers, and highly sophisticated machine learning code to control air flow, consider energy consumption, and analyze air quality data to maintain optimal conditions for both health and energy efficiency.

The invention relates to an air quality management effect evaluation method based on a machine learning algorithm. The method comprises the following steps: S1, acquiring a pollutant local source emission intensity indication variable; s2, constructing a pollutant machine learning model, taking the pollutant meteorological standardized concentration as an indication variable of local pollutant emission intensity, and incorporating the indication variable into a machine learning training pollutant regression prediction model; s2, evaluating the contribution of pollutant emission change and meteorological conditions to environmental concentration change, determining a benchmark reference time period of pollutant concentration change of a to-be-evaluated time period, and performing pollutant concentration simulation under different emission and meteorological scenes based on a control variable method by using the pollutant machine learning model constructed in the step S2, the environmental concentration of the pollutant in the discharge intensity of the to-be-evaluated year under the meteorological condition of the benchmark reference year is obtained, the difference value between the predicted environmental concentration and the actual monitoring concentration of the evaluation year is used as contribution, and quantitative evaluation of the pollutant concentration change caused by pollutant emission reduction in the to-be-evaluated year compared with the benchmark year is obtained.

The present disclosure relates to a system and method for monitoring air quality and providing recommendation for managing air quality. The system comprises one or more environment monitors, a user device and a server in communication with the environment monitor. The system enables the user to input a contextual information to specify a space and / or use of space in which the environment monitor is placed. The system is configured to provide recommendation based on the designated contextual information. The system offers flexibility in selecting sensors and provide specific, actionable advice tailored to individual environments. The system integrates seamlessly with other smart home devices for automated air quality management. Furthermore, the system allows users to select sensors based on the materials they work with, the processes they apply, and the pollutants of concern within their workspace.

The invention discloses a passenger car AQS intelligent air quality management system and a control method thereof, and the system comprises an in-car PM2.5 sensor, an in-car CO2 sensor, an in-car VOCs sensor, an in-car temperature and humidity sensor and an in-car particulate matter particle size sensor. The in-vehicle PM2.5 sensor, the in-vehicle CO2 sensor, the in-vehicle VOCs sensor, the in-vehicle temperature and humidity sensor and the in-vehicle particulate matter particle size sensor are connected to the controller, and the controller is further connected with an air purification module, a ventilation fan and an air conditioning system. Dynamic cooperative monitoring of multiple pollutants (PM2.5, CO2, VOCs, temperature and humidity, ultraviolet intensity and particle size distribution) is achieved, the comprehensive air quality index in the vehicle is automatically adjusted according to monitored data, the air quality in the vehicle is greatly improved, six indexes such as PM2.5, CO2 and VOCs can be monitored in real time, energy is saved by 30% or above compared with a traditional scheme, CO2 response time is shortened by 82.2%, and the system is suitable for popularization and application. The PM2.5 purification efficiency is improved by 27.3%, an air conditioning system is adjusted according to needs, and more energy is saved.

The invention provides a vertical equidistant snakelike route method for monitoring air quality change by using an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicles. According to the technical scheme, the multi-unmanned aerial vehicle intelligent cooperative monitoring method for refined air quality management is constructed by fusing advanced technologies such as a knowledge graph, deep reinforcement learning, federal learning, a graph neural network and a generative adversarial network. According to the method, multi-source heterogeneous data are fully utilized, the unmanned aerial vehicle route and the sampling strategy are adaptively optimized, and the monitoring efficiency and precision are remarkably improved. Meanwhile, the generated high-quality air quality distribution diagram visually reflects the regional pollution condition, and visual decision support is provided for precise pollution control. The invention provides a new thought and method for the fields of wisdom environmental protection, pollution traceability and the like, and has a wide application prospect.

The present invention discloses an intelligent control method for gas flow organization based on air pollution detection, which relates to the technical field of air pollution detection. The method divides a laboratory into multiple monitoring areas and arranges air quality sensors to obtain pollutant concentration data in real time, extracts diffusion rate anomaly characteristics and peak fluctuation characteristics using pre-processed data, calculates the comprehensive pollution index of each area, and divides the area into different pollution degree levels according to the comprehensive pollution index and the set gradient standard threshold value, and adjusts the opening number of corresponding air vents and the air volume output strategy. The system also analyzes pollution changes within a fixed time period and dynamically adjusts ventilation parameters to ensure continuous optimization of indoor air quality. Through intelligent and dynamic monitoring and regulation, the instability and diffusivity of pollutants can be effectively dealt with, thereby achieving accurate and efficient air quality management.

The present disclosure relates generally to improving surgery safety, and more specifically to techniques for managing air quality associated with an operating room. An exemplary method for managing air quality associated with an operating room comprises: detecting a quantity of a substance in the air in the operating room; determining a surgical milestone associated with a surgery in the operating room; identifying a threshold associated with the substance and the surgical milestone of the surgery in the operating room; determining that the quantity of the substance exceeds the threshold associated with the substance and the surgical milestone of the surgery in the operating room; and in accordance with the determination that the quantity of the substance exceeds the threshold associated with the substance and the surgical milestone, outputting a notification that the quantity of the substance exceeds the threshold associated with the substance and the surgical milestone.

The invention belongs to the technical field of air quality management, and discloses a pollutant source analysis method and system for establishing coupling of an air quality mode and a receptor model, and the method comprises the steps: carrying out three-dimensional regional air quality mode simulation on the basis of an air pollutant emission list and a meteorological field based on meteorological mode simulation; through verification of observation data, an adopted three-dimensional regional air quality mode system comprising a meteorological mode and an air quality mode has reliable simulation performance; a source analysis module embedded in a three-dimensional area air quality mode is used for carrying out source analysis on atmospheric pollutants. According to the method, respective advantages of a three-dimensional regional air quality mode and a receptor model in the aspect of source analysis are integrated, the accuracy of refined source analysis of atmospheric pollutants composed of multiple components such as PM2.5, PM10 and VOCs in the atmosphere of a single site or multiple sites is remarkably improved, and refined and scientific development of urban and regional atmospheric pollution prevention and control is promoted; therefore, the health of people is better protected.

The invention provides a vehicle air quality management system comprising an air quality detection device which is configured to be suitable for detecting the air quality in a front cabin and the air quality in a passenger cabin of a vehicle; the front cabin ventilation module is configured to be suitable for executing ventilation operation on the interior of the front cabin; the passenger compartment ventilation module is configured to be suitable for executing ventilation operation on the interior of the passenger compartment; and the control device is configured to be suitable for controlling operation of the front cabin ventilation module and / or the passenger cabin ventilation module based on a detection result of the air quality detection device. According to the embodiment of the invention, flammable harmful gases such as propane and the like can be monitored and responded properly in real time aiming at the front cabin and the passenger cabin respectively, the concentration of harmful gases in the front cabin and the passenger cabin is reduced, and the vehicle use safety of a driver and passengers is guaranteed. The invention further provides a corresponding vehicle air quality management method, a computer program product and a vehicle.

The invention belongs to the technical field of indoor and in-vehicle environment detection and exposure evaluation, and particularly relates to a VOC concentration prediction and human body exposure estimation method based on an actual environment. According to the method, the VOC release concentration prediction model at the dynamic environment temperature is constructed, and key exposure ways such as human body oral inhalation and skin absorption are introduced, so that the concentration change of pollutants in actual indoor and in-vehicle environments and the potential influence of the pollutants on human health can be reflected more truly. Compared with the limitation that a traditional model is only suitable for a constant working condition and neglects human body factors, the method can dynamically predict the VOC concentration under the temperature change condition, synchronously estimates the actual exposure level of a human body, provides more scientific and comprehensive technical support for environmental health risk assessment, material selection and indoor and in-vehicle air quality management, and is suitable for popularization and application. And the method has remarkable practical significance and popularization value.

The invention relates to the technical field of vehicles, in particular to a baby management system, a vehicle and a baby management method, and the method comprises an air quality management module which is used for collecting air quality data in the vehicle and purifying air in the vehicle; the temperature and humidity management module is used for collecting the temperature and humidity in the vehicle and the body surface temperature of the baby and adjusting the temperature and humidity in the vehicle; the emotion management module is used for collecting images and sounds of the baby and pacifying the baby; the safety management module is used for collecting seat data of the baby and locking a door lock and a vehicle lock of the vehicle; and the controller is used for generating at least one management instruction according to at least one of the air quality data, the temperature, the humidity, the body surface temperature, the image, the sound and the seat data, and controlling the corresponding module to execute a target action based on the management instruction. Therefore, the problems of low system integration level, insufficient baby management degree and the like in related technologies are solved.

The present invention is an air management device equipped with a temperature control unit and an air purification unit. The temperature control unit (30, 30') and the air purification unit (40) are installed in a main body (10) and are shielded by a door (20). A detection unit (70) is positioned in the middle of the upper front of the door (20) to detect information about the space for air quality management. Based on the information detected by the detection unit (70), the position and angle at which air is discharged from the temperature control unit (30, 30') and the air purification unit (40) are adjusted to form various discharge airflows.

The invention belongs to the technical field of environmental protection, and relates to a pollutant emission reduction and concentration response fitting and optimization regulation method and system, and the method comprises the steps: S1, collecting pollutant data and meteorological data of a target region in real time; s2, based on the data, determining a response relationship between emission reduction of precursors in the pollution source and concentration change of pollutants in the air by utilizing a method of combining machine learning and numerical simulation; s3, when the concentration of the pollutants in the air exceeds a threshold value, an air quality optimization regulation and control model is started, and the air quality optimization regulation and control model takes minimization of the total cost of emission reduction as a target function and takes an air quality improvement target as a constraint condition; s4, the air quality optimization regulation and control model is solved through an optimization algorithm, and an optimal emission reduction proportion scheme is obtained; and S5, executing corresponding emission reduction control measures according to the optimal emission reduction proportion scheme. According to the method, accurate prediction and efficient regulation and control of atmospheric pollutants can be realized, the air quality management efficiency is remarkably improved, and the treatment cost is reduced.

This disclosure relates to an air quality and thermal comfort management system. The system may include a blower configured to perform a fresh air exchange on a managed area, an indoor sensor module in the managed area, having at least one sensor configured to measure at least one air quality or thermal comfort parameter, and an outdoor sensor module positioned outside the managed area, having at least one sensor configured to measure at least one air quality / thermal comfort parameter. The system may include a controller having a processor in communication with a memory, configured to receive at least one air quality / thermal comfort signal from at least one of the indoor and the outdoor sensor modules, the air quality / thermal comfort signal being based on the parameters measured by the sensor modules, and in response to these signals, transmit a control signal configured to modify an operating state of the blower.

1. The name of the design product: air quality management graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the electronic equipment. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the graphical user interface is used for users to view real-time air quality data and statistical information of air quality changes in the room. 7. The human-computer interaction mode of the graphical user interface: design 1 front view and design 2 front view are air quality management interfaces, which are used to display air quality information. Design 3 front view is an air quality management interface. The user slides the screen at the right end of design 3 front view to the left, and the interface is switched to design 3 interface change state diagram. At this time, the user can select to switch to view the air quality information of other room equipment on this interface.

The invention provides a fresh air system control method and device and a computer readable storage medium. According to the scheme, under the condition that a fresh air system is started, the number of people, the carbon dioxide concentration, the formaldehyde concentration and the current fresh air ventilator frequency in the current environment are obtained; target air supply volume is determined based on the number of personnel, and initial fresh air ventilator frequency is determined according to the target air supply volume and the current fresh air ventilator frequency; under the condition that the carbon dioxide concentration is larger than a first concentration threshold value and / or the formaldehyde concentration is larger than a second concentration threshold value, the target fresh air ventilator frequency is determined based on the carbon dioxide concentration, the formaldehyde concentration and the initial fresh air ventilator frequency, and the first concentration threshold value and the second concentration threshold value are set based on the human health comfort degree index; and a fresh air ventilator of the fresh air system is controlled based on the target fresh air ventilator frequency. According to the scheme, the problem that in the prior art, the air quality management efficiency of a fresh air system is low is solved.

The invention provides an automobile interior air quality management method and system.The method comprises the steps that when it is detected that an automobile is started in real time, a preset sensor array in the automobile is started in real time, and a plurality of different types of sensors are contained in the preset sensor array; the air inside the vehicle and the air outside the vehicle are sampled at preset time intervals through a preset sensor array, so that a first air sample and a second air sample are correspondingly obtained; performing real-time comparison treatment on the first air sample and the second air sample through a preset air analysis device so as to detect same factors and different factors correspondingly generated between the first air sample and the second air sample in real time, and judging whether the different factors are in a preset pollutant range or not in real time; and if yes, an air purification device is started immediately, so that the air purification device purifies air in the vehicle in real time. The purifier can be reasonably controlled, and the use experience of a user is improved.

The invention discloses an indoor air quality management and control system based on negative ion monitoring, and relates to the technical field of air quality management and control, the system comprises a negative ion monitoring module, a first negative ion sensor carries out negative ion monitoring on an indoor high-altitude area, and a second negative ion sensor carries out negative ion monitoring on an indoor low-altitude area; acquiring first monitoring data and second monitoring data according to the negative ion monitoring module; inputting the first monitoring data and the second monitoring data into an illumination catalysis control module; and the air quality management and control module is used for outputting catalysis control parameters according to the illumination catalysis control module and carrying out indoor air quality management and control according to the catalysis control parameters. The technical problems that in the prior art, indoor negative ion monitoring is not comprehensive, and an illumination catalysis device cannot be precisely regulated and controlled to improve the air quality are solved, and the technical effects of saving energy, improving efficiency, precisely monitoring negative ion distribution in areas with different heights indoors and efficiently improving the indoor air quality are achieved.

The invention relates to the technical field of atmospheric environment monitoring, in particular to an air quality index prediction method and system based on decomposition and recombination. According to the method, a multi-source data matrix is constructed by acquiring air quality data and meteorological observation data of a target area; channel decomposition is carried out based on a CEEMDAN decomposition algorithm, through a least square method, channel components are listed, channel component prediction is carried out by adopting a random forest model or a CNN-BiLSTM-Attention model to obtain a weight combination set, the channel component prediction error sum of squares of each weight combination is calculated, an optimal weight parameter is obtained, and the optimal weight parameter is obtained; and performing component prediction on each channel component in the multi-channel component data set to obtain prediction component data of each channel, and summing the prediction component data of each channel to obtain a final air quality index prediction result. A new view angle and method are provided for the field of AQI prediction, and a prediction method which is more accurate, stable in performance and universally applicable is provided for air quality management.

The invention discloses a method for rapidly predicting stable and standard reaching of urban air quality. The method comprises the following steps: acquiring PM2.5 monitoring concentrations and EMI indexes of a to-be-analyzed city from a plurality of historical years to the current year; whether the PM2.5 concentration of the current year under the adverse meteorological condition and the PM2.5 concentration of the current year under the average meteorological condition are smaller than or equal to the target PM2.5 concentration value of the city to be analyzed or not is predicted and judged, and if yes, it is predicted that the air quality of the city to be analyzed in the next year can stably reach the standard; according to the method, whether the air quality of a city in the next year can stably reach the standard or not can be predicted, and the influence of meteorological conditions and emission reduction measures on the air quality is comprehensively considered, so that whether the air in the future time can stably reach the standard or not can be judged, and air quality management measures can be taken conveniently.

The present invention relates to the technical field of air quality prediction, and more specifically to a smart park management system and method, comprising: real-time collection of park environmental parameters and reference data; evaluating the authenticity of the current environmental parameters, determining the number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, and then calculating the prediction error of the current environmental parameters; predicting the environmental parameters at the next moment after the current moment based on the actual value of the current environmental parameters and the prediction error; and determining whether the prediction result is abnormal to monitor and warn of air pollution in the park, thereby achieving air quality management in the park. The present invention can effectively identify interference data, improve the accuracy of air quality prediction, and provide reliable decision support for park management.

The invention belongs to the technical field of air pollution monitoring, and discloses a method for realizing satellite remote sensing column concentration inversion VOCS by using machine learning; the method comprises: preprocessing multi-source data to obtain a multi-source feature data set; performing feature hierarchical extraction and correlation analysis based on the multi-source feature data set to obtain a feature correlation graph; performing dynamic feature embedding and relationship modeling based on the feature correlation graph to obtain a component feature space; constructing a multi-task deep learning inversion model based on the component feature space, and performing weight optimization to obtain an optimized inversion model; performing spatiotemporal interpolation and uncertainty analysis on satellite remote sensing column concentration data based on the optimized inversion model to obtain a high-resolution VOCs component inversion graph; performing multi-scale verification and dynamic correction based on the high-resolution VOCs component inversion graph and ground VOCs component monitoring data to finally obtain a VOCs component inversion result, thereby improving the refinement level of regional air quality management.

The invention relates to the technical field of robots, in particular to a classroom air quality management and epidemic disease early warning robot system and a dynamic regulation and control method. Comprising a mobile monitoring module, an intelligent analysis module, an early warning decision module and a dynamic regulation and control module. The mobile monitoring module is used for collecting air quality data in a classroom and feeding back the air quality data to the intelligent analysis module; the mobile monitoring module carries a micro gas chromatography module, a laser particle counter, a non-dispersive infrared CO2 sensor, an infrared thermal imager, a temperature and humidity sensor and a gas sensitive sensor array; the intelligent analysis module carries out fusion processing on the air quality data collected by the mobile monitoring module and constructs an aerosol diffusion model, and the intelligent analysis module and the early warning decision module realize data interaction through a shared memory; the disease transmission risk can be reduced through intelligent regulation and control.

The application provides an intelligent air quality management method and system for an ammonia production plant, which monitors the oxygen concentration and ammonia concentration in the ammonia production plant through a gas monitoring unit; compares the oxygen concentration and ammonia concentration with preset oxygen concentration safety threshold and ammonia concentration safety threshold through a control unit, generates and issues a control instruction according to the comparison result, so as to control the oxygen production unit and the ventilation circulating unit in the ammonia production plant to operate according to the corresponding load of the control instruction; and connects a lightweight breathing terminal with the oxygen output interface of the oxygen production unit, uses the lightweight breathing terminal to obtain oxygen, realizes the establishment and maintenance of a dynamic balance system for the oxygen and ammonia concentrations in the ammonia production plant within the preset safety threshold range, and provides a convenient, sustainable and non-dependent mobile gas cylinder continuous oxygen supply mode for the staff in the ammonia gas leakage emergency state.

The invention relates to the technical field of vehicle thermal management control, and discloses an in-vehicle air conditioner control method and device and a vehicle. The method is applied to an air conditioner controller of a vehicle and comprises the steps that under the condition that an in-vehicle air conditioner is started and a circulation mode is a non-internal-circulation mode, vehicle front information, in-vehicle air quality information, vehicle temperature information, vehicle running speed and front windshield temperature and humidity information are obtained; according to the vehicle front information, the in-vehicle air quality information, the vehicle temperature information, the vehicle running speed and the front windshield temperature and humidity information, the tail gas prediction risk and the glass fogging risk of the vehicle are determined; and under the condition that the vehicle has the tail gas prediction risk and does not have the glass fogging risk, an air quality management system is started, and the circulation mode is controlled to be internal circulation. By means of the method, it can be guaranteed that air in the vehicle is fresh, and drivers and passengers have good riding experience.

The present invention relates to an air quality management effectiveness evaluation method based on a machine learning algorithm, comprising: S1, obtaining an indicator variable of the emission intensity of local pollutants; S2, constructing a pollutant machine learning model, taking the meteorological standardized concentration of pollutants as an indicator variable of the local pollution emission intensity, and incorporating the pollutant regression prediction model into a machine learning training model; evaluating the contribution of pollution emission changes and meteorological conditions to environmental concentration changes, determining a benchmark reference period for pollutant concentration changes in the proposed evaluation period, using the pollutant machine learning model constructed in step S2, simulating pollutant concentrations under different emission and meteorological scenarios based on a control variable method, obtaining the environmental concentration of the pollutant emission intensity in the year to be evaluated under the meteorological conditions of the benchmark reference year, taking the difference between the predicted environmental concentration and the actual monitored concentration in the evaluation year as the contribution, and obtaining a quantitative evaluation of the pollutant concentration change caused by pollution emission reduction in the proposed evaluation year compared with the benchmark year.

The invention discloses an air quality monitoring and purification regulation and control system and method based on a sensor network, and relates to the technical field of environmental data monitoring processing and intelligent control. According to the invention, environmental data are collected through a heterogeneous sensor network, and abnormal values are cleaned; constructing a dynamic air flow field directed weighted graph based on time sequence correlation among sensor nodes, and reconstructing a global air quality thermodynamic diagram and positioning a pollution source by using a flow field corrected Kriging algorithm; introducing a graph neural network to construct a prediction model, and combining historical and real-time data to predict a future air quality trend; based on a prediction result, solving a dynamic cost function containing multiple targets of quality, energy consumption and noise by using a quadratic programming algorithm to obtain an optimal control sequence; fresh air, a purifier and doors and windows are cooperatively dispatched to execute strategies such as positive pressure blocking or negative pressure pollution discharge; according to the method, the problems that blind areas exist in traditional monitoring, control response lags behind and equipment lacks collaboration are solved, and active and comfortable air quality management based on prediction is achieved.

A CNN-LSTM model-based ozone prediction method fused with meteorological spatial features belongs to the technical field of air quality prediction, solves the problem of how to more accurately predict high-concentration ozone pollution, adopts a convolutional neural network to perform spatial feature extraction on meteorological data, inputs the extracted spatial features into a long-short-term memory neural network, and performs high-concentration ozone pollution prediction. An ozone prediction model based on a convolution-long short-term memory neural network is established, hidden state vectors output by the long short-term memory neural network pass through a full-connection layer, the full-connection layer is responsible for feature distribution and output, hidden states output by LSTM are mapped to an ozone concentration prediction value, and finally an average ozone concentration prediction value of eight hours in the maximum day is output; the technical scheme of the invention is of great significance in understanding the complex relationship between ozone pollution and a multi-scale meteorological process and making and implementing an accurate and effective air quality management policy.

A system (100, 200) for air purification in a building, the system comprising a plurality of air quality sensor arrays (120) installed in a plurality of zones in the building (114) to provide air quality data from one or more zones in the building, an IoT-managed control device (102) configured to receive sensor data from said plurality of air quality sensor arrays (120) and process the same to provide one or more command signals, wherein the IoT-managed control device (102) processes the air quality sensor data from each zone in the building (114) and generates a command signal for the supply of a corresponding amount of purified air at a desired temperature to each of the zones, an air handling unit (116) configured to receive the one or more command signals from the IoT-managed control device (102) regarding the quality of air in the one or more zones, and provide purified air to said one or more zones through air circulation lines, wherein the air handling unit (116) receives vent air (118) at a first temperature as input and provides purified air at a second temperature, wherein the IoT-managed control device (102) comprises an AI / ML module (106) configured to predict air quality within the one or more zones of the building based on the processing of the sensor data, at least one piece of forecast data received from one or more sources over a network (122), and historical data, and optimizes the energy consumption within the building (114) based on predicted data.