109 results about "Air quality index" patented technology

The invention provides an energy-saving control method based on dynamic air conditioning operation data, and relates to the technical field of intelligent control and energy-saving optimization of an air conditioning system.The energy-saving control method comprises the steps that operation data, including temperature, humidity, air velocity and energy consumption data, of an air conditioning system are collected, and a comfort index, an air quality index and an energy efficiency index are generated; the method comprises the following steps of: generating an air conditioning index comprehensively, setting a control target parameter of a system, further generating an operation standard value, dynamically generating a control strategy which comprises the steps of adjusting the switching time of a cooling mode and a heating mode of a fan, optimizing the operation speed and time of the fan and adjusting the operation frequency of a compressor, and finally, if the generated air conditioning index exceeds the standard value, controlling the system to operate. And if not, control strategy adjustment is repeated until the air conditioning index is within the standard value. The control strategy is dynamically adjusted by collecting and analyzing the operation data of the air conditioning system, efficient operation of the system is achieved, and the comfort level of a user and the energy-saving effect are improved.

The invention provides an ecological environment evaluation method and device for a coal mine area and a mining area environment evaluation system. The method comprises the steps that a plurality of environmental indexes of a coal mine area are obtained, and the environmental indexes comprise one or more of soil quality, water quality index, vegetation coverage rate, air quality index, noise level, waste water discharge amount, waste gas discharge amount, biodiversity index, heavy metal content and greening coverage area; the relevancy between the environment indexes is obtained, and the relevancy is the changed relevancy degree between any two environment indexes; a first weight coefficient is determined according to the relevancy, and the first weight coefficient and the relevancy are in positive correlation; and performing weighted average at least according to the plurality of environment indexes and the first weight coefficients corresponding to the environment indexes to obtain an evaluation score. According to the scheme, the problem of low accuracy of the evaluation result of the coal mine environment in the prior art is solved.

The invention relates to an intelligent multi-dimensional air purification cloud intelligent control method and system, and solves the problem that the complexity of an indoor environment, such as temperature and humidity, concentration of other pollutants and factors of different sensitivities of a user to air quality, cannot be comprehensively considered due to the fact that operation of a purifier is adjusted simply according to a single air quality index. The method comprises the following steps: analyzing a voice control instruction to extract key information, analyzing the extracted key information, real-time air quality data and air quality trend information in a future preset time range, inputting the information into an intelligent decision algorithm model of a cloud end, and outputting operating parameters needing to be adjusted of the air purifier, forming a parameter adjustment decision instruction; the formed parameter adjustment decision instruction is issued to an air purifier terminal through a network; and the air purifier responds to the parameter adjustment decision instruction to adjust the operation parameters of the air purifier. The method has the following effects that the air quality is improved, and the requirement of a user for the indoor air environment is met.

The invention relates to the technical field of intelligent archival repository management, and discloses an intelligent archival repository management method and system based on multiple modules. The method comprises the following steps: acquiring environment monitoring data such as storehouse temperature and humidity distribution and air quality indexes, and generating an environment state feature vector through feature extraction; inputting the data into an archive storage risk assessment model, outputting a risk level, and generating an environment regulation and control instruction set containing temperature and humidity regulation and air purification parameters according to the risk level; monitoring access request data containing file numbers and access timestamps in real time, and constructing an access frequency matrix to calculate file thermodynamic diagram distribution; a warehouse partition regulation strategy is generated by combining the thermodynamic diagram and the regulation instruction set, and the high-frequency access file is dynamically migrated to an environment stable area; incremental learning is adopted periodically to update model parameters, and risk prediction precision is optimized. The method can improve the intelligent degree of warehouse management and guarantee the safety of file storage.

The invention relates to an air quality index prediction method based on an SARIMA-LSTM combined model, and belongs to the technical field of air quality prediction. The method comprises the following steps: firstly, acquiring air quality historical data including AQI data, pollutant concentration data and meteorological factor data; secondly, an SARIMA model is established, and AQI is predicted; thirdly, establishing an LSTM (Long Short Term Memory) model, and predicting the AQI; and finally, an SARIMA-LSTM combination model is established based on a time-varying weight method, and a more accurate AQI prediction value is obtained. Compared with a traditional prediction method, the method has remarkable advantages in the aspects of processing time sequence data, capturing a nonlinear relation, improving robustness and generalization ability, realizing real-time prediction and the like. Due to the advantages, the method has higher practical value and prediction precision in practical application.

The invention discloses an air quality index prediction method based on CNN-GRU, and belongs to the technical field of environment monitoring and air quality prediction. The method comprises the following steps: (1) acquiring historical air pollutant concentration data and meteorological data to form a data set; (2) preprocessing the data set, and dividing the preprocessed data set into a training set and a test set; (3) constructing a parallel CNN-GRU double-branch architecture; (4) performing model training and optimization on the parallel CNN-GRU double-branch architecture based on the training set; (5) operating the optimal parallel CNN-GRU double-branch architecture based on the test set, and outputting a pollutant concentration prediction basic value; and (6) carrying out reverse normalization on the predicted basic value of the pollutant concentration, reducing the predicted basic value of the pollutant concentration into an actual concentration unit to obtain the pollutant concentration, calculating a subindex IAQI of the pollutant concentration, and taking a maximum value as a final AQI. According to the method, the parallel double-branch architecture is constructed, so that the parameter quantity is reduced, the prediction speed is increased, and the prediction result accuracy is improved.

The invention discloses an air quality forecasting method fusing monitoring image data. The method comprises the following steps: constructing an LSTM single-channel deep learning network for feature extraction of historical air quality data, meteorological data and time data; a CNN-LSTM single-channel deep learning network used for monitoring image feature extraction is constructed; integrating two single-channel networks, and constructing a dual-channel integrated deep learning model for air quality forecasting; the method comprises the following steps: collecting multi-source data such as monitoring images, air quality and weather, and constructing an air quality multi-source data set; generating a sequence sample for the constructed air quality multi-source data set by using a sliding window method and a'T1: T2 'mode; training the dual-channel integrated deep learning model by using the sequence sample training set to obtain a multi-step multivariable output regression model with forecasting capability; and inputting to-be-detected sequence sample data into the regression model, and outputting a plurality of air quality index forecast results at a plurality of moments in the future. According to the method, the two single-channel neural networks are combined, a relation model among the air pollutant concentration, the monitoring image, the weather and the time is constructed, time sequence forecasting of the air quality is achieved, outdoor monitoring image data are fused, and the forecasting precision of the air quality is improved.

The invention discloses a system and a method for monitoring olfactory smell in a pension community based on Internet of Things, and relates to the technical field of pension, a TVOC sensor and an ammonia gas sensor are adopted to capture volatile organic compounds released by sweat in real time, and temperature, humidity and air fluidity data are combined to calculate a dynamic air quality index, so that the olfactory smell in the pension community is monitored. The defect that a traditional method only depends on temperature and humidity or CO2 concentration monitoring is overcome; secondly, in the air quality state analysis process, AI analysis is adopted for sweat and odor concentration prediction and pollution trend modeling, a long-short-term memory network LSTM is introduced for time sequence analysis, in combination with air quality change rate and humidity anomaly detection, it is guaranteed that air pollution can be timely and early recognized and predicted, and the problem of monitoring blind areas is avoided; and in the aspect of air optimization regulation and control, the air volume and the purification intensity of the air purification equipment are dynamically adjusted through ventilation control in combination with an air quality optimization instruction and an old people fitness activity mode.

The invention discloses an atmospheric environment exercise health monitoring method and system, and the method comprises the steps: S1, generating a pollution level distribution diagram based on air quality index data collected in real time, S2, determining an air quality risk value of a current region according to the pollution level distribution diagram, and S3, determining the air quality risk value of the current region according to the air quality risk value. S3, based on the air quality risk value, calculating a dynamic movement duration threshold value adaptive to human health; S4, regulating and controlling the actual movement duration of the user according to the dynamic movement duration threshold value so as to prevent pollutant inhalation from exceeding the standard; according to the atmospheric environment movement health monitoring method and monitoring system, the movement duration is regulated and controlled according to the real-time fluctuation of the air quality index, so that the problem that pollutants inhaled by the human body exceed the standard is solved.

The invention provides a monitoring method of a locomotive air source system and related equipment, and relates to the technical field of locomotives. The method comprises the steps that the air quality index of compressed air, the first operation index of a compressor and the second operation index of a dryer are monitored, and when any one or more of the air quality index, the first operation index and the second operation index meet abnormal conditions, alarm information is output. The locomotive air source system can be monitored in real time in all directions, the states of the compressor and the dryer in the locomotive air source system can be monitored in real time, and the air quality of compressed air output by the locomotive air source system can also be monitored in real time.

The invention discloses an efficient and energy-saving production workshop ventilation system and method, and relates to the technical field of production workshop ventilation, the workshop ventilation system comprises an environment detection module, a quality evaluation module, a ventilation heat exchange module and an energy-saving regulation and control module, the environment detection module comprises an acquisition unit, the acquisition unit is used for acquiring air multi-dimensional detection data in the workshop main body and air temperature outside the workshop main body; the invention also discloses a workshop ventilation method, and the method comprises the steps: obtaining the air quality indexes and the temperature and humidity indexes of different regions in the workshop main body through separately monitoring the different regions of the workshop; and when the air quality index or the temperature and humidity index of a certain area reaches a critical value, an optimal ventilation and heat exchange regulation and control scheme with the highest ventilation and heat exchange efficiency and the least energy consumption is formulated for the area, so that the effect of improving the ventilation and heat exchange response rate and the energy utilization rate is achieved under the condition that the workshop air quality and the environment comfort degree are maintained. And the effects of saving energy and reducing the maintenance cost are achieved.

The invention relates to the technical field of intelligent control, and discloses an air quality evaluation method and device, air purification equipment and a storage medium, and the method comprises the steps: obtaining original monitoring data of various pollutants; performing format conversion on each piece of original monitoring data to correspondingly obtain standard data, which comprises the following steps of: respectively determining air quality standard limit values of various pollutants; respectively dividing the original monitoring data of various pollutants by corresponding air quality standard limit values to correspondingly obtain standard data; respectively determining the current weight of each standard data, and calculating an air quality index by utilizing all the standard data and the corresponding current weight; and determining the current quality grade corresponding to the air quality index, and controlling the air purification equipment to perform corresponding work based on the current quality grade. According to the invention, format conversion is carried out on the original monitoring data, the current weight of the data is determined, the air quality index is calculated, the quality grade corresponding to the air quality index is determined to control equipment work, air quality can be comprehensively quantified, and the evaluation precision is improved.

The invention relates to the technical field of intelligent equipment, discloses a fresh air linkage control method and system for household appliances, and relates to the steps of obtaining working state data and indoor air quality parameters of the household appliances, calculating a load index and an air quality index according to the data, and obtaining a fresh air linkage control result. When the load index exceeds the threshold value and the air quality does not reach the standard, the fresh air system is started; by dynamically adjusting the air volume adjusting value, the working state of the fresh air system can be optimized in real time according to the indoor and outdoor air quality difference and the load index change; meanwhile, an adaptive control strategy is adopted, the air volume and the working mode are intelligently adjusted based on the historical air quality preference of the user, and the energy efficiency of the household appliances and the indoor air quality are improved. The system has a high intelligent level, and can effectively improve the air quality, save energy and reduce emission.

The embodiment of the invention provides an environment quality evaluation method based on a three-dimensional grid, and is applied to the technical field of environment quality evaluation and monitoring. The method comprises the following steps: acquiring an atmospheric environment measured data sequence of a target grid at the current moment; calculating an air quality index at the current moment according to the measured data sequence of the atmospheric environment at the current moment; determining an environment quality state at the current moment according to the air quality index at the current moment; comparing the environment quality state at the current moment with the environment quality probability sequence at the current moment; the environment quality probability sequence at the current moment is obtained by carrying out environment quality probability prediction according to the environment quality state at the previous moment and diffusion influence data of the target grid; and if the environment quality state at the current moment falls into the maximum probability range in the environment quality probability sequence at the current moment, predicting the environment quality probability sequence at the next moment according to the environment quality probability sequence at the current moment. In this way, the accuracy of environment quality assessment can be improved.

The invention relates to the technical field of virtual reality, in particular to a rhinitis symptom aggravation early warning method based on an air quality index, which comprises the following steps: acquiring PM2.5, PM10 and O3 pollutant concentration time sequences per hour unit, performing differential operation to obtain a concentration change rate, and calculating a concentration standard deviation in a target moving time window. According to the method, concentration data of multiple pollutants such as PM2.5, PM10 and O3 are obtained in real time, the fluctuation amplitude of the pollutant concentration is converted into an exposure risk visually perceived by a user in a differential operation and dynamic standard deviation analysis mode, and the diffusivity of a regional environment is quantified through geographic parameters such as urban digital elevation and building density; and the GPS real-time position and wind speed data of the user are further fused, and the environment diffusion potential and the real-time meteorological condition are comprehensively processed to construct an air quality index capable of accurately reflecting the real entity feeling risk of the user individual.

The invention relates to the technical field of AI family climate control, in particular to an intelligent AI family climate control device which comprises a data acquisition module, a data processing module, an execution control module, a user interaction module and a cloud server. The data acquisition module is used for acquiring indoor environment parameters in real time, including temperature, humidity, air quality index, carbon dioxide concentration and outdoor weather information; the data processing module is internally provided with a climate regulation algorithm based on artificial intelligence, the algorithm compares and analyzes the collected environmental parameters and comfort preference parameters preset by a user, and predicts the environmental change trend in a period of time in the future through a machine learning model; the execution control module is connected with control interfaces of climate adjusting devices such as an air conditioner, a humidifier, an air purifier and a dehumidifier and automatically adjusts the working state and the operation mode of each device according to the adjusting instruction output by the data processing module.

The invention discloses a comprehensive air sterilization and purification method in a dynamic air duct, and the method comprises the steps: arranging a sensor array in a first flow channel, sampling and monitoring air flowing through the first flow channel, collecting the concentration value of pollutants in the air, and building an air quality prediction model through a machine learning method. An air quality index prediction value is output through pollutant concentration detection, and whether the air quality reaches the standard or not is judged. Activated carbon purification and ultraviolet sterilization methods are comprehensively applied, and the air purification rate is increased; an air quality prediction model is established by using a decision tree algorithm, air quality data in an air duct can be collected in real time and processed and predicted, dynamic sterilization and purification are realized, and the method has the characteristics of short time consumption, high efficiency, high accuracy and the like.

The invention discloses an air conditioner control method and device, a medium and an air conditioner, air conditioner environment parameters are obtained, a comprehensive air quality index is calculated according to the air conditioner environment parameters, corresponding operation modes and control modules are triggered in a graded mode based on the index, and accurate sensing and dynamic response to indoor and outdoor air states are achieved. And meanwhile, the limitation that a traditional air conditioning system depends on a single temperature or humidity parameter to conduct simple mode switching is broken through, and the flexibility and diversity of control strategies are improved.

The invention discloses an intelligent building monitoring system and method, belongs to the technical field of building environment control systems, and solves the problems of energy waste and emergency response lag caused by the fact that an existing system is difficult to dynamically coordinate multi-region environment parameters. According to the technical scheme, the method comprises the steps that building space multi-dimensional environment parameters are collected through a distributed infrared sensor array, a temperature sensor array and a carbon dioxide concentration sensor array; the central controller normalizes the data, constructs a space model of a temperature gradient distribution diagram, a personnel density thermodynamic diagram and an air quality index matrix, generates an air conditioner linkage instruction based on a preset temperature difference threshold value to adjust the air supply volume ratio of adjacent areas, and generates an emergency evacuation channel opening instruction based on a personnel density threshold value; the equipment control module executes air conditioning, lighting and security linkage operation, and the lighting unit regulates and controls the brightness in a grading mode according to the curvature radius of the personnel track. The system is mainly used for realizing building environment precise regulation and control and intelligent security and protection collaborative management.

The invention belongs to the field of vehicles, and discloses a whole vehicle air quality pre-diagnosis method and system and electronic equipment, and the method comprises the steps: obtaining a whole vehicle part list; determining a pollutant emission index of each part in the whole vehicle part list according to a preset pollutant database; and according to the pollutant emission index of each part, the air quality index of the whole vehicle is calculated, and the air quality grade of the whole vehicle is determined according to the air quality index of the whole vehicle. According to the method, the in-vehicle air quality can be predicted and evaluated in advance, so that the potential air quality problem can be identified before mass production, measures can be taken in time in the design and production process, the high rectification cost caused by the air quality problem in the later period is reduced, and the production efficiency is improved. And meanwhile, the pollutant emission index of the part is accurately calculated, so that a key pollution source can be more accurately identified and controlled, emission of pollutants in the vehicle is reduced from the source, and the health and comfort of the environment in the vehicle are improved.

A whole building air quality control system includes an indoor air quality (IAQ) component having at least one control state, a plurality of sensors configured to measure a plurality of building conditions of a building space, and a controller communicably coupled to the IAQ component and the plurality of sensors. The controller includes memory storing a desired air quality index (AQI). The AQI includes a categorical variable. The controller is configured to iteratively modify a control state of the IAQ component using a machine learning algorithm until the plurality of building conditions of the building space satisfy the desired AQI.

The invention, which relates to the field of illumination adjustment, discloses an environment sensing illumination adjustment system applied to an intelligent lamp post, comprising a data acquisition module, an air data analysis module, an image data analysis module, a model training module, an instruction generation module and an adjustment module. The data acquisition module is used for acquiring related data; the air data analysis module obtains an air influence value by analyzing the air quality index and the visibility; the image data analysis module analyzes the image data to obtain a personnel influence value and a crowd illumination value; the model training module generates a use illumination adjustment model according to the historical illumination data set; the instruction generation module generates an illumination adjustment instruction according to a comparison result of the target illumination parameter and the actual illumination parameter; the adjusting module adjusts the intelligent lamp pole according to the illumination adjusting instruction; according to the invention, through comprehensive analysis of various factors, the adjustment accuracy of the intelligent lamp post can be improved.

The invention relates to the technical field of intelligent household appliance control, in particular to a mode self-adaptive control method, device and equipment of an air conditioner and a medium. Performing filtering processing on the indoor living environment parameters and the outdoor environment parameters based on a moving average filtering method to obtain a filtering processing data set; enhancement processing is conducted on the filtering processing data set based on the air conditioner energy consumption and a roulette selection method, so that an enhanced data set is obtained; performing iterative optimization on the enhanced data set based on a preset velocity component updating formula to obtain an optimized data set; based on a preset air quality index threshold condition and the optimization data set, a comprehensive evaluation result vector is obtained through calculation; performing mapping operation according to the comprehensive evaluation result vector and a preset intelligent control mode library to obtain a first intelligent braking mode; decision accuracy is improved through data filtering, enhancement and optimization, energy consumption is reduced in combination with dynamic mode scheduling, energy utilization efficiency is improved, and comfort and air quality are guaranteed.

The invention relates to the technical field of sound insulation equipment, in particular to an intelligent sound insulation bin environment perception and equipment regulation and control system and a control method. Comprising an environment sensing module, a data processing module, an equipment regulation and control module, an identity verification module, a human body existence sensing module and a communication module, the environment sensing module is used for collecting environment parameters in the sound insulation bin in real time, and the environment parameters comprise the temperature, the humidity, the noise intensity and the air quality index; the identity verification module adopts a binocular living body identification camera, an IC card identification unit, a password input unit and a code scanning unit, personnel identity verification is realized based on TCP / IP, WIFI or LAN4G communication modes, the face capacity of the identity verification module is 20000, the card capacity is 20000, and the password capacity is 20000; the data processing module receives the environment parameters collected by the environment sensing module, analyzes and processes the environment parameters, and sends a regulation and control instruction to the equipment regulation and control module according to an analysis result; according to the invention, real-time sensing of multi-dimensional environment parameters and intelligent regulation and control can be realized.

The invention provides a car window control method and device, electronic equipment and a storage medium, and belongs to the technical field of cars. The method comprises the following steps: in response to a starting operation of an automatic window lifting function of a vehicle, identifying vehicle types of vehicles around the vehicle; under the condition that the vehicle type of any surrounding vehicle is the target type, an air quality sensor of the vehicle is started, the air quality sensor is used for detecting the external air quality of the vehicle so as to obtain an external air quality index indicating the external air quality, and the target type is the vehicle type which is prone to generating dust in the driving process; and under the condition that the external air quality index is larger than the first preset index, if a vehicle window of the vehicle is in an open state, the vehicle window of the vehicle is closed. According to the method, the influence of poor air quality outside the vehicle on the body health of a driver and passengers in the vehicle can be avoided, the driver does not need to manually close the vehicle window, the driver can concentrate on driving, and the driving safety is improved.

The present application relates to air conditioning technical field, specifically provide a kind of control method of air conditioner, to solve the problem that the function of the bacteria removal device in prior art is relatively single, cannot remove odor.For this purpose, the control method of the present application includes: obtaining the air quality index of outdoor environment, determine the interval where the air quality index is located, obtain the number of people in the space where air conditioner is located, based on the interval where air quality index is located and the number of people, control the operation mode of first ultraviolet module and / or second ultraviolet module.The present application controls the operation mode of first ultraviolet module and second ultraviolet module according to the interval where air quality index is located and the number of people, so as to control the operation mode of air conditioning device in real time according to the air quality of outdoor environment and the number of people in indoor space, so that it can operate according to different actual situation to achieve different sterilization and odor removal intensity, better improve the air condition of indoor space.

The invention provides an air quality index prediction method and device, equipment and a storage medium, and relates to the technical field of air quality prediction. The method comprises the following steps: acquiring pollutant concentration data from the starting moment of the day to the current moment, meteorological data of the day, historical meteorological data, corresponding historical pollutant concentration data and a target air quality index level of the day; according to the pollutant concentration data and the target air quality index level, the hour residual concentration of each pollutant in the residual time period of the day is determined; according to the current day and historical meteorological data and the corresponding historical pollutant concentration data, predicting the per-hour concentration change rate of each pollutant in the current day; according to the hourly concentration change rate, the hourly predicted concentration and the hourly distribution concentration of each pollutant in the remaining time period of the day are determined, and then the air quality index prediction result of the day is determined. The method can predict the air quality index after pollution prevention and control on the current day based on the air quality improvement target.

This invention provides a method for filtering air quality forecast results based on sliding optimal matching, relating to the fields of environmental air quality forecasting and data science technology. The method includes: acquiring multiple predicted air quality index data obtained from various air quality prediction models; acquiring multiple measured air quality index data at multiple times within a past first preset time period; setting a time window; filtering target air quality prediction models from among the multiple air quality prediction models; and obtaining air quality index forecast data. According to this invention, predicted air quality index data and measured air quality index data can be acquired, and error statistics can be performed based on the time window to filter target air quality prediction models. The sliding optimal matching algorithm enables real-time evaluation and automatic filtering of model performance, overcoming the technical bottlenecks of traditional manual operation and static integration, improving the real-time performance and efficiency of air quality prediction model filtering, and providing high-precision and high-efficiency forecast support.

The invention discloses an air purification system and method for a pig farm, and relates to the technical field of air purification of farms. A preliminary design scheme, customized based on the evaluation result, of the multi-stage filtering system, the photocatalytic oxidation device and the intelligent monitoring system is obtained; based on a preliminary design scheme, a multi-stage filtering system, a photocatalytic oxidation device and an intelligent monitoring system are actually arranged and connected by adopting an installation and integration method, and functions of data acquisition and control command transmission are realized; the method comprises the following steps: performing inspection and initialization setting on equipment, initializing an intelligent monitoring system and setting basic parameters based on equipment layout, starting an air purification system by adopting an operation and monitoring method based on the set basic parameters, monitoring air quality indexes in real time, and adjusting operation parameters through an intelligent control system.

The application discloses an intelligent operating room air quality prediction and supervision system based on AI empowerment, which comprises a multidimensional data perception layer, an AI analysis engine and an intelligent supervision platform. The multidimensional data perception layer comprises a particle monitoring network and a parameter acquisition module. The particle monitoring network is arranged in layers around the operating bed to collect multi-particle size particle concentration data through sensors. The parameter acquisition module acquires environmental parameters. The AI analysis engine comprises a time series database, a multi-factor correlation analysis model and a prediction and early warning module. The time series database constructs a four-dimensional data storage structure. The multi-factor correlation analysis model establishes the correlation between the air quality index and the environmental parameters. The prediction and early warning module predicts the particle concentration trend based on the four-dimensional data storage structure and triggers a hierarchical early warning. The intelligent supervision platform generates control instructions according to the hierarchical early warning and collects data to optimize the model. The application realizes accurate prediction, active control and closed-loop optimization of the air quality in the operating room, and improves the control efficiency and medical safety.