190 results about "Intelligent environment" patented technology

The invention discloses an elevator control system based on artificial intelligence, which belongs to the technical field of elevator control, and comprises a plurality of layers of architectures such as data acquisition, transmission, processing, intelligent decision, execution control and the like. The data acquisition layer collects multi-source data through a car inside and outside and operation state sensor group; the data processing layer provides support for intelligent decision-making through preprocessing, analysis mining and model training optimization. An elevator dispatching module of the intelligent decision-making layer carries out peak dynamic zoning and flat peak energy-saving dispatching according to information such as passenger flow; the safety protection module monitors and processes hidden dangers in real time; the personalized service module provides customized services based on passenger behaviors and demands. And driving of the control layer, a door machine and a car environment control system are executed, and stable lifting of the car, safe operation of a door and intelligent environment adjustment are achieved correspondingly. Experiments show that the system can remarkably improve the elevator operation efficiency, safety and passenger satisfaction and reduce energy consumption, and has important application value.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage scheduling prediction method and system driven by intelligent environment monitoring data. The method comprises the following steps: acquiring environmental parameters and processing to obtain an environmental data set; decomposing the environment data in a multi-level manner to obtain feature vectors and uncertainty indexes; inputting a hybrid deep network to obtain demand prediction distribution; constructing a robust optimization model to obtain a scheduling instruction sequence; performing decomposition execution through a layered architecture to obtain a running track; and carrying out deep reinforcement learning iterative optimization to obtain an energy storage scheduling strategy. According to the energy storage scheduling prediction method, the communication delay influence is overcome, the self-optimization capability is achieved, and efficient cooperative operation of the distributed energy storage system based on environmental data driving is achieved.

The invention discloses an artificial intelligence-based intelligent environment simulation method and system, and relates to the technical field of artificial intelligence environment simulation, and the method comprises the steps: forming a scene tensor based on an updated initial scene and a POMDP belief state set, using an optimized VAE model to generate an initial image sequence, using a U-Net diffusion model to optimize the initial image sequence, and using a U-Net diffusion model to optimize the initial image sequence; the method comprises the steps of calculating reconstruction loss of a VAE encoder and diffusion denoising loss of a U-Net diffusion model, calculating a joint loss function of the VAE encoder and the U-Net diffusion model, generating a high-quality image sequence by using the joint loss function through a Monte Carlo simulation method, and optimizing the high-quality image sequence through a conditional diffusion model to generate a final scene image sequence. According to the method, efficient fusion of multi-modal data is achieved, the defect that in the prior art, vision and semantics are inconsistent is overcome, task specific parameters are rapidly generated through inner circulation, meta-model generalization ability is optimized through outer circulation, a model can be rapidly matched with an edge scene, and the generation efficiency and fidelity of a rare scene are remarkably improved.

The invention provides an LCM module aging test method, device and equipment and a storage medium, and the method comprises the steps: obtaining the initial state data and initial environment parameter combination of an LCM module through intelligent environment simulation and initialization processing, and carrying out the multi-mode alternate aging test and real-time monitoring processing of the module through an intelligent environment simulation system, and obtaining performance data in a dynamic environment, and carrying out surface periodic scanning and working sound recording to obtain microstructure data and acoustic data. Through intelligent fault detection and deep analysis processing, fault features and reasons of the module are identified, predictive analysis is performed on the fault in combination with a machine learning algorithm, and a predictive fault trend and an optimized environmental parameter combination are generated. And performing comprehensive performance evaluation on the module according to the fault features, the reason analysis, the prediction trend and the optimization parameters. According to the invention, through combination of intelligent environment simulation, real-time monitoring and deep analysis, accurate performance evaluation and fault prediction of the LCM are realized.

The invention relates to the technical field of intelligent environment monitoring and early warning, and discloses an intelligent environment monitoring and early warning system based on multi-sensor fusion, which comprises an intelligent environment monitoring and early warning system. The intelligent environment monitoring and early warning system comprises an intelligent sensing layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer. According to the intelligent environment monitoring and early warning system based on multi-sensor fusion, multi-source monitoring means such as a fixed monitoring station, mobile monitoring equipment, a wearable terminal and satellite remote sensing are integrated through the intelligent sensing layer, and a multi-sensor fusion technology is matched, so that the dimension limitation of a traditional single monitoring mode is broken through; environmental parameters (pollutant concentration, meteorological factors, ecological indexes and the like) can be captured in all directions, and multi-dimensional and high-precision data acquisition in a complex environment is realized.

The invention discloses an intelligent environment monitoring and regulation method for a marine ranch, and belongs to the field of environment monitoring, and the method comprises the steps: collecting water body parameters, employing a data fusion algorithm to generate a comprehensive environment state index, and solving the business problems of data dispersion, response lag and inaccurate adjustment in water body environment monitoring and regulation. A time sequence analysis model and an optimization algorithm are combined, potential anomalies are analyzed, a risk change trend is predicted, environment adjustment requirements are dynamically calculated, a precise regulation and control instruction set is generated and transmitted to equipment to be executed, and meanwhile, through deviation comparison between real-time feedback data and a preset threshold value, comprehensive indexes and regulation and control parameters are iteratively updated, so that environment stability is ensured. Through multi-level data processing and a self-adaptive adjustment mechanism, the timeliness and accuracy of water body environment regulation and control are remarkably improved, closed-loop management from monitoring to optimization is achieved, and efficient support is provided for intelligent decision making in a complex environment.

The invention provides an intelligent environment damage questionnaire generation and semantic analysis method and system, and the method comprises the steps: obtaining structured environment data through a government department interface, and capturing network public data in combination with an AI search engine; performing cleaning and normalization processing on the data, and constructing a standardized environment database; constructing a semantic model based on an NLP technology, dynamically generating a customized investigation problem according to an interview object type and an event scene classifier, and optimizing the priority of the problem; extracting a key entity from the interview text by adopting a pre-training model; mapping the entity to a preset pollution category through the environmental damage ontology library, and generating a dynamic causal association map; and verifying the integrity and consistency of the atlas by using a logic verification rule, and automatically outputting a structured survey report. According to the method, the defects that a traditional questionnaire is rigid in design, fragmented in information and insufficient in implicit association mining are overcome, the accuracy and efficiency of environmental damage investigation are remarkably improved, and intelligent support is provided for pollution traceability and responsibility definition.

The invention belongs to the technical field of laser flatness testing, and particularly relates to a modularized box room installation flatness testing device which comprises a fixed frame and a laser detector located in the fixed frame, and further comprises a moving mechanism arranged on the inner side wall of the fixed frame, and the laser detector is arranged on the moving mechanism. The motor is used for driving the laser detector to move multidirectionally; the first electric push rod is fixedly arranged at the bottom of the moving mechanism, a mounting plate is fixedly arranged at the moving end of the first electric push rod, and the laser detector is fixedly mounted on the lower surface of the mounting plate. Through multi-dimensional innovation of optical protection, a self-adaptive mechanical structure, intelligent environment control, automatic marking and the like, the precision and efficiency of modular box room installation flatness detection are remarkably improved, and manual dependence and environment interference are reduced through intelligent design.

The invention is suitable for the field of environment monitoring, and provides a sampling information management system for intelligent environment monitoring, which comprises a user authority management and control module for realizing multi-level authority management based on an RBAC (Role Based Access Control) model; the equipment full life cycle management module monitors the equipment state in real time through the Internet of Things and carries out early warning calibration; the standard dynamic adaptation module intelligently associates a standard with a sampling scene, and synchronously authorizes updating; the sampling closed-loop management module supports double-end cooperation, offline operation and block chain evidence storage; the data interaction and analysis module integrates the data and intelligently predicts the data; the auditing and tracing module records whole-process operation and gives an early warning for abnormity; and the multi-terminal collaborative interaction module is adapted to multiple systems. The monitoring efficiency and the data quality are improved, and support is provided for environmental protection decision making.

The invention relates to the field of robot perception and intelligent man-machine interaction, an equipment platform and a perception module, a system is provided with an Intel RealSense D435i depth camera which is used for acquiring an RGB-D image sequence containing depth information in real time, and meanwhile, an advanced YOLOv8-Pose deep learning model is embedded, so that the depth information of the RGB-D image sequence is acquired in real time. High-precision detection, natural interaction, a gesture guidance mechanism, direction inference and target candidate aggregation, interactive target screening and judgment, a robustness enhancement strategy, deep deletion and noise robustness and an incomplete target completion mechanism of human upper body skeleton key points in a front scene are realized; the man-machine collaborative target object identification and positioning method based on depth vision, attitude estimation and semantic understanding is mainly realized. The technology can be suitable for service robots, intelligent assistant robots and other practical application scenes with high requirements for autonomous perception and natural interaction, and man-machine co-fusion and intelligent environment construction are further promoted.

The invention relates to the technical field, in particular to a multi-mode classroom teaching optimization system integrating space intelligent management and somatosensory annotation. According to the technical scheme, the system comprises a space intelligent management module which dynamically adjusts the environment through a genetic algorithm to enable the comfort level index to be optimal, a somatosensory annotation interaction module which is used for recognizing gesture tracks of teachers and students and automatically controlling teaching equipment according to the intention of a user, and a multi-modal data fusion module which is used for carrying out data fusion on the teaching equipment according to the intent of the user. The module improves the accuracy of data analysis and the robustness of system interaction, and the optimization scheduling and resource allocation module optimizes teaching resource allocation based on a Lagrange multiplier method and intelligently schedules resources. Through intelligent environment management, multi-modal data fusion, high-precision gesture recognition and an optimization algorithm, the intelligent level of classroom teaching is improved, and more advanced technical support is provided for future intelligent education.

The invention belongs to the technical field of intelligent control, and discloses a multi-mode fusion intelligent inductive switch control system. Comprising the steps of collecting multi-modal sensing data in real time; performing feature extraction and fusion processing on the multi-modal sensing data to obtain a time sequence representation vector sequence of the user activity mode; semantic analysis is carried out on the time sequence representation vector sequence of the user activity mode, and the behavior state and the emotional state of the user are accurately recognized; dynamically generating a light parameter configuration scheme based on a pre-constructed intelligent response decision engine in combination with the behavior state, the emotional state and the multi-modal perception data; the lamplight parameter configuration scheme is converted into a lamplight control instruction sequence, and the lamplight control instruction sequence is sent to lamplight control equipment through a KNX bus; according to the invention, not only are the intelligence and comfort of illumination improved, but also the privacy of a user is effectively protected, and the adaptability and robustness of the system are enhanced, so that the requirements of modern families for personalized and intelligent environments are met.

The invention relates to the technical field of environment monitoring, and discloses an intelligent environment monitoring and GIS (Geographic Information System) data fusion system, which comprises the following steps: a data fusion algorithm module, performing deep fusion on preprocessed environment data and GIS information through space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method to obtain a GIS data fusion module; according to the intelligent environment monitoring and GIS data fusion system, space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method mathematical method are adopted, and the preprocessed environment data and GIS information are deeply fused. The environmental data set with spatial distribution characteristics is generated, a scientific basis is provided for environmental condition evaluation, trend prediction and anomaly detection, the environmental condition and the association between the environmental condition and the geographic position can be more comprehensively understood through data fusion, and the efficiency and accuracy of environmental management are improved.

The invention discloses an intelligent interaction method and system based on AI digital humans, and relates to the technical field of AI digital humans, and the intelligent interaction system comprises an environment perception analysis module, an AI digital human processing module, a control execution output module and an evaluation maintenance interaction module which are operated in sequence. The technical key points are as follows: the intelligent interaction system successfully constructs an AI digital human capable of intelligently controlling a lighting strategy by combining computer graphics and an artificial intelligence technology, and realizes the technical effects of intelligent environment perception and state judgment, autonomous formulation and execution of the lighting strategy and efficient voice instruction recognition and response; according to the moving speed of the infrared signal source, the system builds and operates the dynamic adjustment model, and generates the brightness increment value of the lamp A corresponding to each adjustment, thereby achieving the dynamic adjustment of the illumination strategy, guaranteeing that pedestrians can clearly see the road conditions, and avoiding the influence of too high brightness on stage actors.

The invention discloses a highway service area intelligent environment monitoring and management system based on the Internet of Things, and relates to the field of environment monitoring and management, and the system comprises an acquisition module which is used for uploading the carbon emission source data of each energy consumption unit of a target service area, synchronously acquiring the carbon concentration in the environment of the target service area and the associated meteorological parameters of the same time interval, and transmitting the acquired data to a cloud server; constructing a carbon footprint basic data set; the traceability module is used for receiving the carbon footprint basic data set, constructing a traceability link based on the data set, analyzing carbon emission key nodes, and identifying a carbon emission ratio and a generation path of each node; according to the method, carbon emission related data and associated meteorological information are collected, a complete carbon footprint data basis is constructed, carbon emission key links and formation paths are carded out through reasonable analysis, the emission reduction potential of each link is calculated in combination with the actual operation condition of a service area, and links needing to be regulated and controlled are determined according to double judgment standards.

The invention relates to the technical field of mine restoration, and discloses an intelligent surface mine environment monitoring and treatment system, which comprises an environment monitoring and multi-modal data acquisition module, a data processing module and a data processing module, the data acquisition module is used for acquiring various types of environmental data such as air quality, noise, water pollution, soil erosion and geological disasters through a sensor network, an unmanned aerial vehicle, satellite remote sensing and other detection devices, and performing local data processing; and the environment optimization and decision-making module based on edge computing and deep learning is configured to deploy computing resources at a data acquisition end close to a mining area by utilizing an edge computing technology and perform real-time processing and complex multivariate analysis on the multi-source environment data through a deep learning algorithm. Through multi-modal data acquisition and an environment optimization and decision module based on edge calculation and deep learning, multi-dimensional environment data from a mining area can be rapidly processed.

The invention relates to an intelligent environment color recognition method, which realizes accurate control of a color recognition technology under the working condition of a sea-land dual-purpose sound and temperature hidden transfer device, and comprises the following steps that: an image acquisition module acquires various kinds of environment image data; the preprocessing module optimizes the image quality and the data format; the color feature extraction module extracts multi-dimensional features by using a deep learning algorithm; the deep learning model module learns a mapping relation through mass data training; the color recognition module feeds back and accurately recognizes colors based on the training model and the confidence coefficient; and the result output module displays the result in a plurality of visual modes. The environment color recognition method improves the color recognition accuracy and adapts to complex environment illumination and color combination; the method has good adaptability and is suitable for various scenes; reliable data processing is guaranteed, real-time performance, stability and personalized control are achieved, and user experience and working efficiency are improved.

The invention provides an intelligent environment monitoring device integrating NB-IoT (Narrow Band Internet of Things) and an embedded system, which comprises a sensor module comprising a temperature, humidity and air quality sensor unit, and an embedded processing module comprising a data receiving, processing and integrating unit, an NB-IoT communication module and a power supply module. The device collects environment data, processes and fuses the data, and sends the data to a remote monitoring end through NB-IoT. According to the invention, real-time acquisition, processing and remote transmission of environment data can be realized, and the accuracy and efficiency of environment monitoring are improved.

The invention relates to the technical field of flue gas treatment, and particularly provides a coal-fired power plant intelligent environmental protection island platform management system, which comprises a sensing layer, an application layer and a decision-making layer, and is characterized in that the sensing layer comprises a plurality of data acquisition devices and is used for collecting data of all working devices in an environmental protection island; the application layer comprises a flue gas management and control module, and the flue gas management and control module establishes a flue gas management and control prediction model based on the data collected by the sensing layer and historical data records; and the decision-making layer determines an optimal collaboration strategy of the environmental protection island based on the model and data established by the application layer. According to the environment-friendly island platform management system provided by the invention, parameter information is obtained through the sensing layer, modeling is performed through the application layer, emission prediction is realized, the application layer uploads a prediction model and sensing layer parameters to the decision-making layer, the decision-making layer performs particle swarm global optimization algorithm on output parameters and each model, and the minimum cost is taken as a target. And the range of the parameters and the pollutant emission limit value are adjusted as constraint conditions, and a control strategy of the optimal system is obtained.

The invention discloses an unmanned aerial vehicle GNSS-free relative positioning method and device and a medium, and relates to the technical field of unmanned aerial vehicle autonomous navigation and intelligent environment perception. According to the method, a traditional PnP pose estimation framework and dependence on the physical size of an identifier are abandoned, the relative geometric center position of the visual identifier in the image is extracted by using deep learning, and the real height reference provided by the millimeter wave radar and the real-time attitude information of the unmanned aerial vehicle are combined; and a scale-adaptive, analytic and non-iterative relative position calculation model is constructed, and high-precision estimation of the three-dimensional relative position of the unmanned aerial vehicle relative to the landing mark center is realized.

The invention provides a chip testing method based on an ATE platform, and relates to the technical field of chip manufacturing and testing. According to the method, multiple advanced test technologies and intelligent algorithms are creatively integrated, test environment parameters are accurately controlled through an intelligent environment regulation and control system, radio frequency and digital signals are efficiently processed in cooperation with a test engine, a test execution stream optimizes a high-speed interface test by using a chip built-in Loopback function and an adaptive algorithm, and the test efficiency is improved. The intelligent control and analysis system dynamically adjusts a test process and accurately predicts chip defects based on a federal learning framework, and realizes Chiplet interconnection verification and closed-loop data analysis feedback optimization of a test program at the same time. The chip testing efficiency and precision are effectively improved, the testing cost is reduced, dependence on expensive equipment is reduced, the method can be widely applied to various chip testing scenes, and remarkable economic benefits and technical advantages are brought to chip manufacturing enterprises.

The invention relates to the technical field of exercise training, in particular to an intelligent environment adaptability exercise training system and training method. The system comprises an environment simulation module, an intelligent wearable device, a data processing and analysis platform and an augmented reality technology, and the environment simulation module simulates a plateau training mode by accurately controlling indoor environment parameters to help athletes adapt to a high-altitude environment. The intelligent wearable device monitors the physiological data and the motion trail of the athlete in real time, and provides real-time feedback and decision-making assistance. The method comprises the steps of environment simulation setting, intelligent wearable device monitoring, data processing and analysis, augmented reality technology training and training scheme generation. The invention aims to improve the environmental adaptability of athletes by simulating extreme environmental conditions such as high altitude, high temperature, low temperature and the like, so that the competitive performance is enhanced, and the adaptive capacity and competitive level of the athletes in actual competitions are enhanced.

The invention provides an intelligent environment-friendly clean room, and relates to the technical field of clean rooms. The intelligent environment-friendly clean room comprises a clean room body, and an air inlet device is fixedly installed on the inner wall of the clean room body. According to the clean room, through the application of the air quality monitor, the switching assembly, the communicating pipe and the filtering assembly, air can be efficiently filtered under the action of the filtering assembly, the filtered air enters the clean room body through the switching assembly and the air inlet pipeline, and at the moment, the air quality monitor can monitor the conveyed air in real time; when the filtering assembly in use fails to filter, the air quality monitor can monitor that conveyed air is abnormal, and at the moment, the controller can immediately recognize and control the switching assembly to block the communicating pipe connected with the failed filtering assembly, and the other filtering assembly is quickly switched; therefore, the air purity in the clean room is maintained in a seamless connection mode, and continuity and stability of the environment of the clean room are guaranteed.

The invention discloses a natural gas pipeline failure risk assessment system and method based on a multi-factor coupling model, and relates to the technical field of natural gas pipeline safety, and the system comprises an intelligent environment monitoring module, a dynamic technical parameter library, a block chain management assessment module, a coupling calculation engine, an enhanced visualization platform and a self-adaptive early warning module. According to the method, the risk coupling degree is calculated by adopting the risk coupling algorithm improved based on the nonlinear characteristic and the synergistic effect, so that risk coupling quantification is realized; the adaptive capacity of the natural gas pipeline evaluation system is improved through technical innovation integration, full-chain closed-loop management is formed through combination of technologies such as block chain evidence storage, machine learning defect prediction and three-dimensional GIS rendering, model parameters and early warning strategies can be dynamically adjusted according to real-time data, and conversion from passive response to active prevention is achieved.

The invention discloses an intelligent camera system for a bicycle, and belongs to the technical field of image data processing, and the system comprises a riding vehicle data obtaining module which is used for obtaining the related data information of a preset bicycle in the riding process in real time, and the related data information comprises the riding speed and riding positioning information of the preset bicycle; the environment image acquisition unit is used for acquiring image information of objects around the preset bicycle according to the riding positioning information; the environment image acquisition unit comprises a main calculation module, an auxiliary calculation module and a judgment module, and the main calculation module is used for calculating the distance between the preset bicycle and the surrounding object based on the riding positioning information. Through intelligent environment perception, dynamic risk assessment and precise early warning functions, early warning can be performed in advance before a potential collision risk occurs, the occurrence probability of a riding accident is effectively reduced, the safety of bicycle riding and user experience are improved, meanwhile, data support is provided for riding safety, and early warning logic is optimized.

The invention discloses an intelligent environment monitoring system based on image analysis, and belongs to the technical field of intelligent environment monitoring systems. Comprising the steps of collecting dormitory environment data, collecting the sanitation condition and light-out state data of a dormitory, performing preprocessing such as transcoding and cutting on the dormitory environment data, establishing a dormitory environment model by utilizing the dormitory environment data, evaluating the trained model by utilizing a part of reserved dormitory environment data, and performing adjustment according to a result. A trained model which is qualified in evaluation is deployed in actual monitoring, further movement approaching is carried out according to a feedback result, through establishment of a dormitory environment model, in the monitoring process, uncertain factors can be predicted according to a monitoring result, so that the position of a camera is adjusted again for determination, the monitoring effect is improved, and the user experience is improved. And meanwhile, repeated monitoring of the determined area can be reduced, unnecessary movement is reduced, and the monitoring efficiency is improved.

The invention relates to the technical field of data processing and intelligent environment control, in particular to a sleep environment feedback control data processing method based on cognitive behavior therapy logic, which comprises the following steps: a reference model construction step: acquiring a non-contact multi-mode time sequence signal, and reconstructing a reference signal balance curve to construct a target signal manifold model; a simulation flow generation step of converting the preset interference characteristics into signal modulation operators and injecting the signal modulation operators into the model to generate a simulation signal flow; a double-track difference calculation step: executing double-track difference operation to respectively extract residual features representing system deviation and theoretical interference; a similarity judgment and control step: calculating the topological similarity of the residual features in a time-frequency domain, judging a deviation dominant type, generating a corresponding control instruction, and driving an execution mechanism to act; according to the method, psychological anxiety and environmental discomfort are effectively distinguished by using a double-track differential mechanism, and accurate attribution analysis of sleep interference sources is realized.

The invention relates to a field programmable gate array (FPGA)-based intelligent environment monitoring system in the field of environment monitoring systems, which comprises an FPGA master control module used for processing and analyzing monitoring data of each partition to obtain a partition state and an environment parameter change trend of the partition in the next one hour, and selecting a corresponding processing strategy according to the partition state; the partition management module is used for setting the type of each partition and judging and associating similar partitions; the control module is used for controlling related equipment in each partition to execute a preset processing strategy; the data acquisition module is used for data acquisition of each independent partition; the self-adaptive processing module is used for adjusting the range of the basic threshold value, the scene threshold value and the alarm threshold value; and the abnormity evaluation module is used for evaluating the abnormity probability according to the environment change trend and the threshold range, and the system can associate similar areas by monitoring the environment parameters of each area in real time and comprehensively evaluating the similarity of the areas so as to realize efficient environment monitoring and management.

The invention discloses an environment gas leakage intelligent detection method based on big data analysis. The method comprises the following steps: collecting monitoring data and constructing a sensor network space-time diagram fused with meteorological conditions; predicting the background concentration of each node by using a sequence decomposition attention model; calculating a concentration residual error and constructing a space-time residual error map; extracting an abnormal feature vector through a physical information enhanced graph attention network; calculating an abnormal score in combination with the normal mode memory bank and generating a leakage source probability distribution diagram; when the conditions are met, calling a computational fluid dynamics model to simulate a theoretical concentration field; and determining a leakage event through spatial correlation analysis. According to the invention, the false alarm rate of gas leakage detection in an open environment is obviously reduced, and the leakage source positioning precision is improved.

The invention relates to the technical field of coal mine safety ventilation, in particular to a coal mine ventilation and exhaust device and a control system thereof. According to the technical scheme, the coal mine ventilation and exhaust device and the control system thereof comprise a ventilation pipeline outer cavity, a ventilation fan, an air volume induction module, a filtering device, a gas detection module, a control module, an air suction machine and a protection sieve plate, the ventilation fan is arranged in the ventilation pipeline outer cavity, and the air volume induction module is arranged on one side of the ventilation pipeline outer cavity; a filtering device is arranged on the other side of the outer cavity of the ventilating pipeline; according to the invention, deep integration of multi-source data and dynamic environment modeling are realized through the intelligent environment prediction module, the temporal-spatial resolution of environment monitoring and the comprehensiveness of risk assessment are remarkably improved, safety early warning is upgraded from passive threshold judgment to active coupling analysis, the false alarm rate is greatly reduced, potential risks are intervened in advance, and the safety of the mine environment is guaranteed.