447 results about "Ventilation control" patented technology

The invention relates to the technical field of mine ventilation control, in particular to a mine fan ventilation control method and system based on digital twinning. The method comprises the following steps: carrying out data preprocessing on acquired mine monitoring data; performing feature extraction on the preprocessed mine monitoring data; utilizing a random forest algorithm to predict the relationship between the data characteristics and the fan air volume; a digital twinborn model is used for resolving the relation between the data characteristics and the fan air volume, and a fan ventilation control strategy is obtained; and generating a PLC control instruction based on the fan ventilation control strategy. And through multi-source data fusion and deep analysis, the real-time ventilation requirement of the mine can be accurately grasped. Based on random forest algorithm prediction and digital twinborn model calculation, dynamic and accurate adjustment of the air volume of the draught fan is achieved, it is ensured that the concentration of harmful gas in all underground areas is maintained within a safety threshold value, the oxygen content is sufficient, an ideal environment is created for personnel operation and equipment operation, and the safety and reliability of a ventilation system are remarkably improved.

The invention provides a mobile henhouse ventilation regulation and control method and system based on an environment model, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining original temperature data, original humidity data, a three-dimensional space wind speed component, an opening geometric distribution data set, and original latitude and longitude coordinate data and time data; calculating a dynamic environment weight coefficient matrix according to the original latitude and longitude coordinate sequence and the time data; generating a self-adaptive heat exchange flow field according to the dynamic environment weight coefficient matrix, the original temperature data and the original humidity data; according to the self-adaptive heat exchange flow field and the trepanning geometric distribution data set, a ventilation compensation amount demand curve is obtained through calculation; generating a ventilation intensity adjusting instruction set according to the ventilation compensation amount demand curve; and according to the ventilation intensity adjusting instruction set, outputting an optimal ventilation control parameter. According to the method, ventilation response self-adaptive accurate matching, airflow dead zone self-suppression and energy consumption-effect dynamic balance under the moving sudden change working condition are achieved.

The embodiment of the invention discloses a heating and ventilation control method and device based on reinforcement learning and a medium, and relates to the technical field of heating and ventilation control, and the method comprises the steps: determining predicted temperature data corresponding to a target building through a pre-constructed Mamba time sequence prediction model, and carrying out the reinforcement learning pre-training through the predicted temperature data and a preset space-time constraint matrix, determining an initial heating and ventilation control strategy; performing initial regulation and control on a heating ventilation air conditioning system in the target building according to the initial heating ventilation control strategy, and collecting real-time building data corresponding to the target building; according to real-time building data, performing dynamic partitioning on a target building, determining a plurality of dynamic control partitions, constructing partition state space description based on the dynamic control partitions and the space-time constraint matrix, and performing targeted online reinforcement learning training through the partition state space description by using a progressive exploration strategy, so as to improve the training efficiency. And generating a heating and ventilation control strategy corresponding to the target building so as to control the target building through the heating and ventilation control strategy.

The invention discloses an energy-saving control method based on a building group spatial form, which relates to the technical field of energy-saving control, and comprises the following steps: constructing an airflow track analysis region based on a region with hot pressure difference direction change in a spatial pressure covering sensing matrix, and collecting continuous wind direction and wind speed data; judging whether the airflow direction is abnormal or not according to the change of the flow direction consistency entropy; performing spatial coincidence processing on the spatial pressure shielding sensing matrix and the airflow direction abnormal region, and generating an air outlet ventilation and heat extraction efficiency evaluation index based on the heat conduction rate in the coincidence region and the stability of an airflow output path; and on the basis of the ventilation and heat removal efficiency evaluation index of the exhaust outlet, an exhaust response condition matrix containing a space pressure shielding parameter, an airflow disturbance level and a heat retention level is constructed, and a ventilation control judgment sequence is generated. According to the method, the problem of air exhaust failure under pressure shielding interference is solved, ventilation efficiency accurate judgment and energy-saving regulation and control optimization are achieved, and the ventilation stability and the energy utilization efficiency of the building group are guaranteed.

The invention belongs to the technical field of highway tunnel ventilation control, and provides a highway tunnel ventilation control method and system. The method comprises the following steps of multi-source heterogeneous data acquisition, multi-source data fusion, state estimation, traffic flow prediction, dynamic pollution index calculation and multi-target weight generation, rolling optimization with constraints, control execution, model updating and feedback correction. The state sensing precision is improved through multi-source data fusion, real-time accurate estimation and error reduction of a pollutant concentration field and a traffic flow state are realized through a heterogeneous sensor and a Rayleigh fusion technology, the problem of sensing deviation caused by single data in a traditional method is solved, future traffic flow is predicted based on a graph neural network, and the traffic flow sensing accuracy is improved. The fan power is optimized through model prediction control, the ventilation intensity is adjusted in advance, pollutants are prevented from exceeding the standard, the response speed is increased compared with threshold value control, and energy consumption is reduced.

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention relates to the technical field of medical care informatics, and discloses a laryngeal mask ventilation control method suitable for outpatient anesthesia, which comprises the following steps: before core analysis, injecting and monitoring a high-frequency signal to confirm the integrity of an original breathing waveform data stream; constructing a real-time ventilation waveform trajectory in a differential phase space only on the premise that a data stream is complete, and performing morphological comparison with an individualized baseline template; meanwhile, when a parameter adjustment event of the respirator is monitored, the baseline template is automatically re-calibrated, a collaborative analysis framework with front information source quality inspection and dynamic reference self-adaption capabilities is constructed, ventilation monitoring is converted from passive response to an isolated peak point into active insight to a system dynamics evolution trajectory, and the system dynamics evolution trajectory is optimized. Therefore, the risk of gradual deterioration of the ventilation state caused by tiny air leakage of the laryngeal mask or secretion accumulation can be clinically recognized in an early stage, and intervention time is won for anesthetists.

The invention discloses an outdoor workstation air quality monitoring and intelligent ventilation system and method, and belongs to the technical field of environment monitoring and intelligent ventilation control. The system is composed of an environment sensing module, an intelligent control unit and a ventilation execution device. And the environment sensing module realizes pollutant concentration field reconstruction through a multi-dimensional data fusion algorithm. The intelligent control unit constructs a dynamic ventilation model based on an improved fuzzy PID algorithm, and generates an optimal ventilation strategy in combination with meteorological prediction data and workstation structure parameters. And the ventilation execution device adopts a modular design to realize directional air supply and pollutant blocking. The method comprises four steps of real-time environment data acquisition, dynamic risk assessment, ventilation parameter optimization and execution mechanism linkage control, and remote intelligent regulation and control are realized through cooperation of edge computing and a cloud platform. According to the scheme, a passive response mode of a traditional ventilation system is broken through, and the technical advantages of accurate pollutant tracing, dynamic optimization of ventilation efficiency and intelligent adjustment of equipment energy consumption are achieved.

The invention discloses a mine intelligent ventilation combined optimal adjustment and optimal control method and system. The method comprises the steps that S1, multi-source mine ventilation data are collected through a distributed multi-mode sonic sensor by adopting a gridding deployment strategy; s2, performing fusion optimization on the multi-source mine ventilation data by using a trust function theory to obtain comprehensive state data; s3, on the basis of the comprehensive state data, obtaining optimal preliminary regulation and control actions of different mine equipment through a self-adaptive reinforcement learning model; and S4, based on the optimal preliminary regulation and control action, introducing a roadway structure safety degree as a dynamic constraint factor, combining with an improved pigeon inspired optimization algorithm to obtain a final regulation and control action, and realizing underground ventilation control based on the final regulation and control action. The safety degree of the roadway structure is incorporated into a fitness function, so that the safety of the roadway structure is fully considered while the ventilation effect and the energy consumption are optimized by the algorithm.

The invention relates to the technical field of tunnel ventilation and environmental protection, in particular to an efficient and energy-saving tunnel ventilation and dust removal integrated system. Data analysis and decision making; ventilation control; dust removal operation; and sewage treatment. The intelligent sensing module is installed in the tunnel, environment parameters of different positions can be accurately obtained in real time and transmitted to the central control system, the efficient and energy-saving draught fan is selected, the frequency conversion and speed regulation device is installed, energy consumption is reduced, and by arranging the air filtering device and the dust falling device, the dust falling efficiency is improved. The dust collection device and the dust blocking device arranged at the tunnel portal effectively control the air environment quality in the tunnel, sewage purification and cyclic utilization are achieved through sewage treatment, and the system is high in intelligent degree, can be flexibly adjusted according to the actual situation and is suitable for popularization and application. The problems that a traditional ventilation and dust removal mode is high in energy consumption, poor in effect, lack of intelligent control and the like are effectively solved, the health of constructors is guaranteed, and pollution to the surrounding environment is reduced.

The invention discloses a ventilation control system and method based on the Internet of Things, and relates to the technical field of environment control and regulation, and the system comprises an environment data collection module, a central control module, a ventilation execution module, a communication module and a user interaction module. Temperature, humidity, carbon dioxide concentration and PM2.5 concentration are monitored in real time through the environment data acquisition module, the central control module calculates ventilation demand intensity according to a ventilation demand function, and the operation frequency and air volume output of ventilation equipment are dynamically adjusted through a formula, so that a set environment target value is met. The communication module is used for remotely transmitting data and instructions, and the user interaction module provides real-time data display and parameter adjustment functions. The system further comprises an energy-saving mode, and when the ventilation requirement is lower than a set threshold value, the operation frequency of the equipment is reduced so as to reduce energy consumption. In addition, the system has fault detection and alarm functions, and equipment abnormity is judged by monitoring operation parameters and a user is notified in time.

The invention relates to a tunnel ventilation system parameter optimization method based on neural network model driving, and the method comprises the steps: dividing a tunnel into a plurality of irregular grid nodes according to the three-dimensional geometrical characteristics, aerodynamic characteristics and pollutant distribution characteristics of the tunnel; constructing a nonlinear edge weight calculation function according to the spatial distribution data of the plurality of irregular grid nodes in combination with a pollutant diffusion coefficient, a real-time wind speed field and a spatial position relationship among the grid nodes, and generating an edge weight matrix representing interaction strength among the grid nodes; inputting the grid node information of the plurality of irregular grid nodes and the edge weight matrix into a space-time diagram neural network model to obtain multivariable space-time dynamic characteristics of the pollutants; according to the multivariable space-time dynamic characteristics, pollutant concentration overrun probabilities of the irregular grid nodes are determined; and according to the pollutant concentration overrun probability of the irregular grid nodes, ventilation regulation and control parameters of the tunnel ventilation system are optimized. The design of ventilation system parameters can be optimized.

The invention relates to a high-speed rail station building ventilation control method and system based on a physical information neural network, and the method specifically comprises the following steps: collecting related parameters of a high-speed rail station building, and carrying out the preprocessing; based on an electric field intensity theory, constructing a passenger space-time distribution function according to the preprocessed data to calculate dynamic heat source intensity; constructing a PINN model of 10 layers of MLP, and constructing a loss function according to physical constraints to train the PINN model; according to output of the PINN model, a control target is deduced in combination with ventilation parameters, and then ventilation of the high-speed rail station building is controlled; and comparing a derivation result with an actual condition, if the deviation is too large, performing fine adjustment on the PINN model, and correcting the output derivation result until the deviation is within a specified range. According to the method, related data are collected and converted into dynamic heat source items which can be identified by the PINN model, and the model is trained through physical constraints, so that the accuracy of a derivation result can be improved, and the ventilation control precision and the energy-saving effect of the high-speed rail station building are improved.

The invention discloses an intelligent window ventilation control system and method, and relates to the technical field of window control, window position coordinate data, plant position coordinate data and plant information data are input into an indoor three-dimensional model, current indoor environment data are collected and input into a processing end, calculation is carried out according to the current indoor environment data, and a ventilation control result is obtained. According to the intelligent control system, the window is accurately controlled to be opened and closed according to plant requirements, it is ensured that plants obtain proper illumination and humidity, healthy growth of the plants is promoted, meanwhile, the living comfort and humanistic care are remarkably improved, energy waste is reduced through intelligent control, the indoor environment is optimized, and the intelligent control system is suitable for popularization and application. In addition, the system indicates that indoor environment regulation and control enter a new intelligent stage, convenient and efficient life experience is brought to residents, and the home intelligent level and the living quality are integrally improved.

The invention discloses a green energy-saving curtain wall and a construction transformation method thereof, and relates to the technical field of building curtain walls, the green energy-saving curtain wall comprises a glass frame, two ventilation box bodies and a photovoltaic frame, the two ventilation box bodies are installed at the upper end and the lower end of the glass frame respectively, and the photovoltaic frame is fixedly installed on the side wall of the glass frame; a clamping and fixing structure is installed in the glass frame, ventilation control structures are installed in the two ventilation box bodies, connecting structures are installed on the outer sides of the two ventilation box bodies, and an extending control structure is installed on one side of the photovoltaic frame. According to the green energy-saving curtain wall, through multi-structure innovative cooperation and multi-structure collaborative design, functionality, installation efficiency and energy utilization efficiency are optimized, thin film photovoltaic glass and an adjustable photovoltaic panel are adopted for collaborative power generation, the thin film photovoltaic glass is embedded in a curtain wall body, and the angle-adjustable photovoltaic panel extending out of a frame is combined, so that the curtain wall is more energy-saving and environment-friendly. And a dual power generation structure is formed.

The invention relates to the technical field of ventilation control, in particular to a multi-parameter real-time monitoring system of an indoor large ventilation system. The system uses a monitoring data acquisition module to acquire basic monitoring data. Kriging interpolation is carried out by using a spherical semi-variation function according to the collected temperature data to obtain an optimal unbiased estimated value under each coordinate, and an upflow tendency diagram is formed. And segmenting the real-time upflow tendency diagram to determine a plurality of connected domains, evaluating a pollution plume mode index of the current indoor environment by comparing particle concentration data and heat energy data, and further determining a control strategy mode. In each control strategy mode, regulation and control parameters can be determined according to the area of a connected domain or a pixel value in a real-time upflow tendency map. According to the embodiment of the invention, the real-time upflow tendency diagram is constructed through data monitoring and feature analysis, and indoor dynamic, low-disturbance and low-delay ventilation control of a large commercial room is realized.

The invention relates to the technical field of precise monitoring medical treatment, and discloses a breathing machine control strategy debugging method and system based on an EIT image, and the method comprises the steps: constructing a breathing machine control algorithm model based on deep learning through a case library, and representing a mapping relation between a case EIT image and a key index thereof and a breathing machine ventilation control parameter; acquiring current EIT images of a plurality of respiratory cycles of the patient, inputting the current EIT images into the respirator control algorithm model, matching the most similar case from the case library, and performing respirator ventilation control by using target respirator ventilation control parameters of the most similar case; operation is controlled to start, adjustment EIT images and key indexes of a plurality of respiratory cycles of the patient are obtained, and the adjustment EIT images and the key indexes are compared with the EIT images and the key indexes of the most similar cases for judgment; if the requirements are met, continuously controlling and updating the case library, and optimizing the model; otherwise, switching the control mode. Determination of breathing machine control parameters participated by a patient is reduced, trial and error times of clinical parameter adjustment are reduced, and patient safety and control accuracy are both considered.

The invention relates to the field of ventilation control, in particular to an intelligent ventilation control method and system based on an intelligent laboratory, and the method comprises the steps: obtaining a to-be-controlled air volume sequence and a multi-dimensional to-be-controlled turbulence characteristic sequence of any region in a preset sampling period, and calculating a turbulence compensation coefficient of a target dimension, the turbulence contribution degree of any region in the target dimension is calculated; calculating a comprehensive product, calculating a first difference value between the air volume at the marked moment and the comprehensive product, and taking a mean value of the first difference values of all dimensions as an air volume correction value of any region at the marked moment; and taking a result of weighted summation of the air volume correction values of all the areas as comprehensive air volume, and inputting the comprehensive air volume into a laboratory PID controller to complete ventilation control. According to the technical scheme, the precision and timeliness of the ventilation control result can be improved.

The invention belongs to the technical field of tunnel construction, and particularly relates to a tunnel construction ventilation intelligent regulation and control method based on electrical automation. Distributed sensor arrays are deployed on a tunnel driving working face, a haulage roadway and a fan station through a multi-source heterogeneous sensor network, ventilation loads, environmental parameters, equipment states and construction event signals are collected in real time, and real-time and accurate original data support is provided for the system; a three-dimensional fluid mechanics-particulate matter diffusion coupling simulation model of a tunnel ventilation system is constructed, then a digital twin is used as a virtual training environment, an optimization instruction is converted into an electrical control signal, and a closed-loop PID-fuzzy coincidence controller dynamically compensates execution delay, controls operation of a fan and an air valve, and finally performs dynamic simulation on the tunnel ventilation system. And an execution result is fed back to the digital twin body for model calibration, so that finer and more efficient ventilation control is realized, the use problem in a high-altitude and alpine region environment is solved, the continuous effectiveness of ventilation regulation and control is effectively guaranteed, and the overall performance is improved.

The invention provides a clean room pollutant concentration control method, equipment, a medium and a product, and relates to the technical field of clean room ventilation control. The method comprises the following steps: acquiring the real-time gaseous molecular pollutant AMC concentration of each region in a clean room according to the space region division result of the clean room; when the real-time AMC concentration of the target area is larger than or equal to a concentration threshold value, the target area serves as the center, and the wind speed correction value of the FFU of each area is adjusted according to the distance between the other areas and the target area; wherein the closer the distance to the target area is, the larger the corresponding wind speed correction value is; when the AMC concentration of the target area is smaller than a concentration threshold value, the current AMC concentration value of each area is obtained, and an FFU wind speed optimal solution is determined according to the current AMC concentration value of each area, clean room FFU layout information and a particle swarm optimization algorithm PSO; and adjusting the wind speed of each FFU according to the optimal solution of the wind speed of the FFU. According to the invention, global monitoring, dynamic optimization and accurate regulation and control of pollution concentration can be realized.

A method for detecting the operation of a tunnel fan, a fan analyzer, a medium and a product, which relate to the field of equipment detection, and the method comprises: generating a detection request according to the equipment identification information of the fan analyzer and the target detection fan, and sending the detection request to the ventilation control system; receiving the real-time vibration data, real-time displacement data and historical maintenance data of the target detection fan returned by the ventilation control system; determining the vibration spectrum characteristics of the target detection fan based on the real-time vibration data; determining the axis trajectory characteristics of the target detection fan based on the real-time displacement data; inputting the vibration spectrum characteristics, the axis trajectory characteristics and the historical maintenance data into an operation evaluation model to obtain the operation status score of the target detection fan; generating a maintenance recommendation report for the target detection fan based on the operation status score. Implementation of this application can monitor the operation of tunnel fans and reduce the dispatching cost of maintenance personnel.

The invention relates to the technical field of grain storage ventilation control, discloses an air volume dynamic adjusting method and device of a grain storage ventilation system, and aims at solving the problem that an existing grain storage ventilation control mode is poor in accuracy. Comprising grain layer average temperature gradient, average grain temperature, average grain temperature difference between an air inlet layer and an exhaust layer, grain pile balance desorption absolute humidity under instant grain temperature and instant atmospheric absolute humidity; the state index of each grain pile partition is calculated in real time, the corresponding target ventilation quantity is determined, and the corresponding air valve is dynamically adjusted to the target opening degree corresponding to the target ventilation quantity; and when the grain pile partition is ventilated according to the target opening degree, the ventilation wind speed is calculated in real time, the moisture evaporation rate of the grain pile partition is calculated in real time based on a Penman-Monteith equation, and the target ventilation quantity is dynamically corrected according to the moisture evaporation rate. The accuracy of grain storage ventilation control is improved, and the method is particularly suitable for granaries in complex climate areas.

The invention relates to a reinforcement learning-based granary ventilation energy consumption optimization system and method, in particular to the field of granary ventilation energy consumption optimization, and can effectively improve the intelligent level of granary ventilation control by setting a data acquisition and fusion module, a state space construction module, a reinforcement learning training module and a real-time decision and feedback module. Accurate perception and decision-making of a dynamic environment are realized through cooperative work of multiple modules, the system adaptively optimizes a ventilation strategy by using a reinforcement learning technology, energy consumption is remarkably reduced, meanwhile, it is ensured that the temperature and humidity in a granary are stabilized in a safe range, the grain mildew or qualitative change risk is avoided, and the system has high practicability. The online learning ability of the method can continuously adapt to external weather changes and grain condition dynamic states, closed-loop optimization is formed, and finally energy consumption minimization is achieved on the premise that grain storage safety is guaranteed.

The present invention discloses an indoor and outdoor ventilation control system and a control method. The indoor and outdoor ventilation control system includes a floor slab, an overhead floor provided below the floor slab, and a makeup air device provided below the ground. At least a part of the overhead floor is located below the ground. An air supply channel and a return air channel communicating with the atmosphere above the ground are opened in the part of the overhead floor located below the ground. The makeup air device is communicated with the air supply channel and the return air channel. The present invention solves the problems existing in the prior art, such as poor ventilation and moisture-proof effects and inconvenient building structure layout.

The invention discloses a linkage system of a roadway air distribution device and a dust removal device and a control method, and belongs to the technical field of safety and environmental protection of mine / tunnel construction. The system comprises a long-pressure air control system, a short air draft dust collection system and an intelligent control module, the long-pressure air control system forms an airflow air wall through a long-distance air distribution device to restrain dust diffusion, the short-air-draft dust collection system captures dust through a short-distance dust collection cover, and the intelligent control module dynamically adjusts operation parameters of the long-pressure air control system and the short-air-draft dust collection system based on parameters such as dust concentration and air speed. The control method comprises the stages of dust suppression, capture, weak wind area removal and intelligent adjustment, and efficient dust removal is achieved through airflow collaborative optimization and a self-adaptive control algorithm. The dust collecting rate can be increased by 50% or above, energy is comprehensively saved by 20% or above, potential safety hazards in weak wind areas are eliminated, and the device is suitable for dust-producing scenes such as driving working faces and belt conveying roadways and has the advantages of being efficient, safe, capable of saving energy and wide in applicability.

The invention belongs to the technical field of building ventilation, and particularly discloses an intelligent building ventilation energy-saving system based on AI dynamic adjustment, and the system is characterized in that the system comprises a sensing layer which is used for collecting indoor and outdoor environment parameters in real time; the data transmission layer transmits various data acquired by the sensing layer to a central control system in real time through an Internet of Things communication technology, and is provided with a data caching mechanism; the central control system comprises a data processing module and is used for preprocessing the received data; the AI analysis module is used for establishing an indoor air quality prediction model, a personnel activity mode prediction model and a ventilation system energy consumption model by applying a machine learning algorithm; the control decision module generates an optimal ventilation control strategy; and the execution layer comprises a fresh air fan and an exhaust fan which adopt a frequency conversion technology, and an air supply outlet and an exhaust outlet which are provided with electric air valves, and further comprises a function of receiving a control instruction of the central control system and controlling ventilation equipment in real time.

The invention provides a control method and device for a fresh air system of a clean room, a medium and a computer program, and relates to the technical field of clean room ventilation control. The method comprises the steps that a system collects air quality data of different detection points of a clean room in real time, and an original data sequence is generated; key feature data is extracted from the original data sequence, and dimensionality reduction is performed on the key feature data by using PCA to form a dimensionality reduction feature matrix; respectively inputting the dimension reduction feature matrix into ARIMA and LSTM to obtain prediction results, and performing weighted fusion to obtain a fusion prediction result of the air quality; and based on the fusion prediction result and the detection point position, marking a potential pollution source position, obtaining a diffusion path simulation result according to the diffusion model, and based on the fusion prediction result, the potential pollution source position and the diffusion path simulation result, generating a real-time dynamic control strategy to control the operation state of the fresh air system. Efficient and energy-saving operation can be achieved while it is ensured that the air quality reaches the standard.

The invention discloses a method and system for adjusting the self-adaptive ventilation air volume of a factory building, and the method comprises the steps: carrying out the optimization of a quantization factor and a scaling factor of a ventilation PID controller through a parameter optimization algorithm, obtaining an optimal quantization factor and an optimal scaling factor, and carrying out the feedback adjustment of a ventilator based on the optimal quantization factor and the optimal scaling factor; therefore, compared with the traditional technology, the selection of the PID control parameters does not depend on artificial experience, the most reasonable control parameters can be optimized by utilizing an algorithm, and on this basis, the PID control precision can be improved, so that the adaptive adjustment of the air volume in each working area in the underground powerhouse can be more accurately realized, and the control precision of the underground powerhouse can be improved. And therefore, the dust removal effect on each working area in the plant is ensured.

The present invention discloses a green energy-saving curtain wall and a construction and renovation method thereof. The present invention relates to the field of building curtain wall technology, comprising a glass frame, two ventilation boxes and a photovoltaic frame, the two ventilation boxes being respectively installed at the upper end and the lower end of the glass frame, the photovoltaic frame being fixedly installed at the side wall of the glass frame, a clamping and fixing structure being installed inside the glass frame, a ventilation control structure being installed inside the two ventilation boxes, a connecting structure being installed outside the two ventilation boxes, and an extension control structure being installed on one side of the photovoltaic frame; the present invention provides a green energy-saving curtain wall, which optimizes functionality, installation efficiency and energy utilization efficiency through multi-structure innovation collaboration and multi-structure collaborative design, adopts thin-film photovoltaic glass and adjustable photovoltaic panels for collaborative power generation, the curtain wall main body is embedded with thin-film photovoltaic glass, and combined with the angle-adjustable photovoltaic panels in the extension frame, a dual power generation structure is formed.

The invention discloses a coal mine underground environment intelligent sensing and dynamic ventilation linkage control system and method, and is applied to the technical field of coal mine ventilation. The system comprises an environment intelligent sensing module used for collecting environment data of an underground coal mine; the data transmission module is used for realizing data transmission among different modules; the underground data processing module is used for processing and analyzing the environmental data, and performing real-time ventilation early warning and real-time ventilation equipment regulation and control based on the environmental data; the ground linkage control module is used for predicting environment data of a future time node and setting a preset scheduling scheme of ventilation equipment in combination with a ventilation network model simulation result and an underground operation plan; and the dynamic ventilation regulation module is used for regulating the ventilation condition of the underground coal mine based on the preset scheduling scheme and the real-time regulation and control scheme. According to the method, prediction is carried out by collecting the environment data of the underground coal mine, the scheduling scheme is generated in advance according to the operation plan, and the hysteresis problem of a real-time ventilation regulation and control scheme is avoided.