5039 results about "Environmental monitoring" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention provides a sewage system traceability analysis and intelligent monitoring method, system and device based on a graph neural network, and a storage medium, and belongs to the technical field of environment monitoring and artificial intelligence. The invention aims to solve the technical problems of low efficiency, low precision, difficulty in processing multi-source data, poor monitoring network and the like of the existing sewage system pollution tracing method. The method comprises the following steps: constructing a sewage system knowledge graph fusing multi-source heterogeneous data such as water quality and water volume; adopting a multi-scale graph neural network model to learn pollution propagation characteristics based on the knowledge graph; after a pollution event occurs, pollution path backtracking is carried out in combination with physical models such as flow conservation so as to identify a pollution source; bayesian inference is introduced to carry out uncertainty quantification on a traceability result so as to assess the credibility of the traceability result; and finally, dynamically optimizing the layout of the monitoring points based on information gain and other criteria. According to the invention, rapid and accurate positioning of the pollution source can be realized, and the method is suitable for intelligent supervision of an urban sewage system.

The invention discloses an intelligent multi-dimensional automatic inspection operation and maintenance management method, device and equipment, relates to the technical field of network inspection, and aims to solve the problems that the inspection efficiency of network equipment is low and fault early warning is lagged. The method comprises the following steps: scanning equipment in a target machine room to establish an equipment registration file, wherein the registration file comprises an equipment type identifier and an associated inspection strategy template; collecting multi-dimensional data of each registered device according to the inspection strategy template, wherein the multi-dimensional data comprises a hardware operation state, a business middleware state and an environment monitoring parameter; determining abnormal data from the multi-dimensional data according to a preset dynamic threshold value, and generating and issuing graded alarm information according to the influence degree of the abnormal data; and creating a work order according to the abnormal data and automatically distributing the work order so as to process the abnormal data.

The invention relates to the technical field of sensor calibration, in particular to an environment adaptive multi-dimensional calibration method for an excavation type intelligent sensor, which comprises the following steps: acquiring temperature, humidity and gas data through environment monitoring, comparing with a threshold value, marking an abnormal state, evaluating the influence of each parameter on a sensor signal and analyzing fluctuation amplitude and intensity; combining sensor sensitivity and response rate to screen key factors, extracting factor fluctuation trend, optimizing signal disturbance weight and response path, correcting signals in real time, updating a calibration parameter set, monitoring signal values based on multi-dimensional parameters, and adjusting output to maintain a stable range. According to the invention, temperature and humidity and gas abnormity are dynamically identified through threshold comparison, a factor model is constructed based on sensitivity weight, a multi-dimensional calibration path is generated, a parameter set is updated in real time to decouple environment sudden change and sensor drift, signal self-adaptive convergence is realized through closed-loop control, multi-parameter interference errors are reduced, and the bottleneck of calibration delay is broken through. And the time-varying working condition data stability is ensured.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention discloses an underwater garbage detection method and system based on an MSD-YOLO network, and belongs to the technical field of computer vision and deep learning, and the method comprises the steps: S1, obtaining an underwater garbage data set, and carrying out the preprocessing; s2, configuring a model training environment; s3, a YOLO network model is improved, an MSD-YOLO network model is formed, an original C3k2 module is replaced by a C3k2-MSCB module, a C2PSA module is fused into an SCSA module, an original detection head is replaced by DynamicDCMv3Head, the MSD-YOLO network model oriented to underwater garbage detection is obtained, and a data set is input into an MSD-YOLO network for training; and S4, inputting a to-be-detected underwater image into the trained MSD-YOLO network model, and obtaining target category, position and confidence information. According to the method, higher accuracy is achieved with lower parameter quantity, and the efficiency and the performance are balanced. According to the invention, the efficiency and reliability of underwater target detection are significantly improved, and an efficient and robust technical solution is provided for marine environmental protection monitoring and underwater autonomous operation.

The invention discloses a multi-modal fusion real-time environment monitoring visual robot system, and relates to the technical field of real-time vision. The system comprises a multi-mode sensing module, and is equipped with various sensors such as a binocular stereo camera and a laser radar to collect environment data. The heterogeneous data preprocessing unit cleans and downsamples data such as images and point clouds; the space-time alignment fusion module realizes multi-source data space-time registration and synchronization; the environment semantic understanding engine constructs an environment semantic graph through a multi-branch network in combination with an attention mechanism; the abnormal event detection unit identifies abnormity based on a historical data model; the path planning and decision-making module integrates multiple targets to generate an optimal path; the autonomous movement execution module controls the robot to move and operate; and the cloud cooperative control center supports model updating and remote intervention. According to the invention, through cooperative work of all the modules, full-process intelligentization of environment monitoring data acquisition, processing, analysis and decision making is realized.

The invention discloses a field soil heavy metal pollution health risk dynamic assessment and intelligent early warning system, and relates to the technical field of soil environment monitoring. The system comprises a multi-source data acquisition unit, a data preprocessing unit, a risk calculation engine and a visual interaction terminal. The key technical point is that a dynamic field evolution analysis module and an adaptive grid rendering control module are introduced; the dynamic field evolution analysis module constructs a pollution potential energy field matrix representing a pollutant migration trend based on soil heavy metal concentration and hydrogeological parameters, and calculates a space-time gradient change vector of the pollution potential energy field matrix; and the latter dynamically adjusts the grid local density according to the gradient vector module value, and automatically encrypts the computational nodes in the region with severe risk change. In cooperation with a time sequence prediction deduction and feedback correction mechanism, the method can simulate the dynamic evolution of the pollution plume in the porous medium in real time, solves the problems that a migration rule is difficult to capture and the calculation efficiency of a uniform grid is low in traditional static evaluation, and achieves three-dimensional dynamic risk early warning with high precision and low calculation power consumption.

The invention relates to the technical field of environment monitoring, and discloses a completion process data-based environment monitoring parameter comparison evaluation method, which comprises the following steps of: obtaining completion process data and environment monitoring data, and constructing a multi-dimensional data graph, mapping the completion process data and the environmental monitoring data to generate a standardized data set containing a construction feature vector and an environmental parameter vector; calculating a dynamic weight coefficient through a multi-objective optimization model based on the construction feature vector and the environment parameter vector in combination with the construction progress and the environment sensitivity, and outputting a weight matrix; the intelligent contract automatically triggers an evaluation process, generates a dynamic evaluation report containing an overproof event identifier, a compliance repair suggestion and a digital signature, and links an AR visualization system to be superposed to a BIM model to generate a three-dimensional thermodynamic diagram; and in combination with anomaly detection and a time sequence prediction model, identifying potential risks, generating a dynamic risk early warning map, and finally outputting a predictive evaluation report. According to the invention, the accuracy and real-time performance of environment monitoring evaluation can be improved.

The invention discloses a nuclear power plant radiation environment monitoring method and system, and the method comprises the steps: collecting the real-time monitoring data of a nuclear power plant radiation environment, and obtaining an environment monitoring real-time data set; performing dynamic threshold comparison on the environment monitoring real-time data set to generate an abnormal identifier or a normal feature vector; under the condition that the abnormal identifier is not generated, inputting the normal feature vector into a pre-trained environment trend prediction model, and outputting a radiation environment change trend curve and a potential abnormal probability in a future preset time period; and based on the change trend curve and the potential abnormal probability, generating graded early warning information, and finally outputting a monitoring early warning result including an early warning grade, prediction time efficiency and a coping strategy. According to the embodiment of the invention, the method can achieve the intelligent monitoring of the whole process of the radiation environment from the abnormal recognition to the prediction intervention, and improves the early warning accuracy, timeliness and decision support capability.

The invention relates to the field of environment monitoring, discloses an ecological protection red line holographic dynamic monitoring and early warning method and system, and aims to solve the defects of an existing monitoring and early warning mechanism in the aspects of timeliness, data integration and intelligent analysis. According to the method and the system, multi-source heterogeneous data are acquired and fused, a space bottom line digital model is constructed, space-time situation awareness analysis and change abnormity identification are performed, risk assessment early warning triggering is performed, and early warning issuing and processing feedback are managed. According to the invention, high-precision holographic acquisition and fusion of multi-source data can be realized, the accuracy and timeliness of change abnormity identification are improved, passive early warning is changed into active early warning, a monitoring disposal closed loop is formed, supervision intelligence and treatment efficiency are improved, and ecological safety is guaranteed.

The invention discloses a plot pollution monitoring, predicting and early warning system and method, and relates to the technical field of environment monitoring and pollution early warning, and the method comprises the steps: obtaining multi-source data of a target plot; performing preprocessing, feature extraction and fusion on the multi-source data; inputting the multi-source data into the dynamic pollution diffusion model to obtain a pollutant concentration field of the target plot; jointly inputting the fusion data and the pollutant concentration field into a land parcel pollutant concentration prediction model for prediction to obtain a pollutant concentration prediction value of the target land parcel in a future target time period; comparing the pollutant concentration predicted value of the target land parcel in the future target time period with the early warning threshold values of all levels, and generating an early warning instruction of the corresponding level according to a comparison result; generating corresponding early warning information according to the early warning instruction; and executing corresponding early warning measures according to the early warning information. According to the invention, through multi-dimensional data integration and model optimization, scientific assessment and hierarchical management and control of pollution risks can be realized.

The invention relates to the technical field of environment monitoring, in particular to an intelligent acquisition method based on environment monitoring data fusion, which comprises the following steps: S1, constructing a multi-sensor distributed monitoring network, and acquiring atmosphere, water quality, soil and meteorological environment data; s2, performing data preprocessing, including smoothing, anomaly detection, interpolation and time alignment; s3, carrying out data source, feature and decision three-level fusion, and outputting an environment quality level; s4, constructing a quality index system, monitoring data quality and adaptively optimizing fusion parameters when the data quality is abnormal; s5, performing environment trend prediction and pollution tracing based on a fusion result, and generating early warning information; and S6, constructing a cross-modal causal diagram, reasoning a multi-source causal path, and identifying pollution key factors and source responsibility subjects. According to the invention, through multi-source environment data fusion and cross-modal causal reasoning, high-precision early warning of environment abnormity and intelligent traceability identification of pollution sources 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 discloses a blasting operation field behavior compliance identification method, system and device and a medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a multi-source video stream and environment monitoring data of a blasting operation site; identifying a current operation stage according to the multi-source video stream; identifying a scene risk factor according to the multi-source video stream and the environment monitoring data; inputting the multi-source video stream into a pre-trained deep learning model, and analyzing identity information, a behavior sequence, an equipment wearing state and position information of each operator; calling a compliance rule set matched with the current operation stage and the scene risk factor in a pre-constructed security rule library; based on the compliance rule set, performing compliance verification on the identity information, the behavior sequence, the equipment wearing state and the position information; and if the violation behavior exists through verification, generating a violation judgment result containing the violation type, the risk level, the associated personnel and the spatial region identifier. According to the invention, intelligent monitoring and violation identification of the whole process of blasting operation can be realized.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention relates to the technical field of remote sensing, discloses a geological environment monitoring method and system based on multi-source remote sensing, and aims to solve the monitoring problems of heterogeneous multi-source remote sensing data, difficulty in dynamic capture, disjunction of geological mechanism and interpretation and the like. According to the method and the system, multi-source remote sensing data is acquired and standardized, multi-modal geologic features are extracted, through cross-modal deep fusion and space-time modeling, abnormity is analyzed and identified, risks are evaluated, and visualization and decision support are realized. According to the technical scheme, comprehensive, accurate and high-timeliness monitoring of the geological environment can be realized, the premonition of the geological disaster can be effectively identified, the trend can be predicted, and support is provided for prevention and control of the geological disaster.

The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention, which relates to the technical field of ecological environment monitoring and intelligent management, discloses a real-time monitoring and early warning management system based on an ecological environment, comprising a multi-modal data acquisition module for acquiring and preprocessing multi-medium environment data, a multi-medium risk conduction modeling module for constructing a directed graph, and a dynamic modeling risk conduction process. The cross-medium risk conduction chain effect evaluation system has the advantages that the problem of cross-medium risk conduction chain effect evaluation deficiency is effectively solved, multi-modal data provides a data basis, the modeling module constructs a directed conduction relation graph, and the model construction module constructs a three-dimensional model and simulates pollutant migration and transformation and risk effects, and the intelligent early warning module generates early warnings, so that the cross-medium risk conduction chain effect evaluation system has the advantages that the cross-medium risk conduction chain effect evaluation deficiency problem is effectively solved; a graph neural network is used for dynamic modeling, a conduction path is presented, a digital twin engine simulates pollutant migration and transformation and a risk conduction effect, an intelligent early warning module carries out multi-level evaluation and timely early warning, a decision intervention module generates an optimal intervention scheme, and the system realizes accurate evaluation and effective response of cross-medium risk conduction.

The invention relates to the technical field of agricultural environment monitoring, and discloses a cultivated land quality monitoring method. The method comprises the following steps: acquiring soil multi-parameter data, and carrying out sensor network deployment and anti-interference processing; performing exception filtering, standardization and missing value interpolation on the data; cloud storage data are generated through sliding window segmentation and encryption compression; generating a monitoring report based on the multi-dimensional analysis and the dynamic chart; adopting a plurality of intelligent algorithms to identify abnormal modes and constructing an early warning strategy library; and finally, real-time monitoring and early warning grade division are realized. According to the invention, full-process automatic monitoring and intelligent early warning of the cultivated land quality are realized, and the monitoring precision and the decision-making efficiency are obviously improved.

The invention relates to the field of environment monitoring and ecological restoration, in particular to a farmland and river ecological collaborative monitoring and regulation system and method. The system comprises a first-stage barrage, a second-stage barrage, a gabion revetment, front-end acquisition equipment, execution equipment and a server, wherein the first-stage barrage and the second-stage barrage are arranged at intervals; the execution equipment comprises irrigation equipment, a first gate, a second gate and an ecological flocculant putting device; the server comprises a data acquisition module, a data storage module, an analysis processing module and a control execution module, inputs control parameters including a first gate opening degree, a second gate opening degree, an irrigation equipment electric valve opening degree and an ecological flocculant putting amount through an LSTM model, and predicts state variables including an upstream water level, a downstream flow, an irrigation water supply amount, a total phosphorus concentration and a total nitrogen concentration; and a multi-target optimization model including agricultural water demand, ecological flow, flood control safety and water quality purification targets is solved in combination with an optimization NSGA-II algorithm, target control parameters are acquired, and a control instruction is generated to drive execution equipment.

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention relates to the technical field of agricultural intelligent equipment, and discloses a multi-mode sensing fusion control system for an aerogel greenhouse heat preservation quilt. An environment multi-mode monitoring module of the system collects internal temperature and humidity distribution, illumination intensity gradient, soil moisture content profile and external meteorological change data of a greenhouse in real time to generate an environment comprehensive monitoring data set; based on the data set, the thermal insulation mode decision module analyzes the difference between the thermal energy loss rate and the thermal insulation demand of different areas in combination with the physical characteristic parameters and historical regulation and control records of the aerogel thermal insulation quilt, matches a dynamic thermal insulation strategy and generates a multi-stage partition regulation and control instruction; the cooperative parameter optimization module calculates a combination scheme of the unfolding angle, the coverage area and the thickness of the heat preservation quilt, and adjusts a partition cooperative control parameter set; the multi-mode execution controller generates a heat preservation quilt dynamic regulation and control map; the anomaly fusion diagnosis module monitors anomalies and generates a set of regulation error correction factors. The system realizes fine, dynamic and intelligent regulation and control of greenhouse heat preservation.

The invention relates to the technical field of soil environment monitoring, and discloses a soil remediation construction site environment monitoring method based on edge computing cloud collaboration. The method comprises the following steps: deploying an edge computing node at a restoration construction site, and collecting real-time data of soil humidity, heavy metal concentration, volatile organic compound content and meteorological parameters through a multi-source sensor; inputting the data into a spatio-temporal feature extraction network, and generating a spatio-temporal fusion feature matrix by means of hierarchical convolution and a cross-channel attention mechanism; on the basis of the matrix, the index anomaly probability is calculated by using a depth probability network, and a risk feature sequence with uncertainty measurement is generated; constructing a dynamic risk field model in combination with repair process parameters, and predicting a pollution diffusion path through a space-time propagation algorithm; and in combination with an equipment operation state, an edge side local regulation and control instruction and a cloud global optimization strategy are generated by a collaborative decision engine, and efficient and accurate environment management requirements of a construction site are met.

The invention discloses a high-precision pressure sensing and self-calibration system, and relates to the technical field of pressure sensing, the system comprises a pressure sensing module, an environment monitoring module, a data processing module, a digital twinning module and a self-calibration module, the pressure sensing module collects and converts pressure signals through a high-precision sensor, and the environment monitoring module obtains environment parameters in real time; the data processing module carries out amplification, filtering, analog-to-digital conversion and timely frequency domain analysis on the initial electric signal, and outputs preprocessed data in combination with environmental parameter dynamic compensation. The digital twin module constructs a sensor virtual model, and simulates and outputs an ideal pressure predicted value; and the self-calibration module compares the predicted value with the preprocessed data, generates a final pressure value through deviation calculation, iterative learning and data fusion, and can also calculate the health index of the sensor and perform early warning maintenance. The system realizes high-precision pressure induction and real-time self-calibration, improves the measurement accuracy and prolongs the service life of the sensor.