789 results about "Warning system" patented technology

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention discloses a slope early warning method and system based on deep learning, and particularly relates to the technical field of slope early warning, and the method comprises the steps: S1, multi-source data collection, S2, dynamic graph construction, S3, meta-learning model initialization, S4, space-time fusion prediction, S5, dynamic risk assessment, and S6, graded early warning triggering. Through multi-modal data fusion, an innovative model architecture and an intelligent early-warning mechanism, the slope early-warning capability can be remarkably improved, multi-source data are fused, a cross-modal attention mechanism is utilized, the slope state is comprehensively and accurately reflected, the early-warning accuracy is improved, a dynamic graph structure is constructed to be combined with a meta-learning engine, different slopes are adapted, continuous optimization can be achieved, and the early-warning capability of the slope is improved. Meanwhile, a scientific grading early warning system is established, a historical case library and related equipment are linked, resources are efficiently allocated, life and property safety is guaranteed, and disaster losses are reduced.

The invention relates to the technical field of fire-fighting early warning, and discloses a fire-fighting early warning system based on image data relevance. The system comprises a multi-source image acquisition module used for acquiring fire-fighting scene multi-source heterogeneous image data; the correlation feature analysis module is used for performing cross-data-source correlation analysis on the data to generate feature vectors; the spatio-temporal dynamic modeling module is used for constructing a multi-dimensional feature fusion space to generate an associated spatio-temporal feature tensor; the resource optimization scheduling library is used for constructing a double-layer collaborative library; and the intelligent early warning control module generates a real-time fire early warning instruction and an emergency response decision through a hierarchical reinforcement learning framework. The system also has the functions of building structure deformation detection, smoke diffusion prediction and the like. Through cooperation of multiple modules, accurate fire-fighting early warning and efficient emergency response are realized, the fire prevention and control capability is improved, and life and property safety is effectively guaranteed.

The invention relates to the technical field of fire-fighting monitoring, in particular to a fire-fighting water system fault detection and early warning method and system based on the fire-fighting Internet of Things, and the method comprises the steps: 1, periodically collecting pressure data through pressure sensors disposed at a fire-fighting water pump outlet, a pipe network main pipe branch point and the most unfavorable tail end; 2, according to the current valve opening degree, the pump state and historical normal working condition data, theoretical pressure expected values and dynamic allowable deviation zones of all nodes are generated; 3, when actually measured pressure deviates from a theoretical pressure expected value and exceeds a dynamic allowable deviation band, marking abnormal nodes, reversely constructing a fault propagation tree along the topological model, and allocating weight factors for associated nodes according to fault types; and 4, triggering graded early warning based on the number of the abnormal nodes, the fault propagation path and the weight factor accumulated value. And the system can automatically take measures when a fault occurs through an equipment linkage function, so that the efficient operation of the fire fighting water system is guaranteed, and the efficiency and safety of fire emergency response are improved.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention discloses a fire-fighting early warning system and method based on artificial intelligence, and relates to the technical field of intelligent fire-fighting early warning, and the system comprises an environment sensing module which comprises a multispectral sensor, a temperature sensor, a smoke sensor and a high-definition camera, and is used for collecting fire characteristic spectral data and transmitting the data to a data analysis module; the data analysis module comprises an edge calculation unit and a data fusion unit, and is used for performing feature extraction and anomaly detection on the fire feature spectrum data through a deep learning model to generate a fire risk assessment result; the early warning decision module comprises a risk assessment unit, an early warning grading unit and a communication unit, and is used for determining an early warning grade according to a fire risk assessment result and triggering equipment of the execution feedback module; and the execution feedback module comprises a spraying system, an emergency lighting system and an alarm and is used for executing fire emergency measures according to the instruction of the early warning decision module. According to the invention, comprehensive improvement of fire prevention and control efficiency is realized through cooperative work of multiple modules.

The invention discloses a TBM cutterhead blockage early warning system based on distributed temperature measurement, and belongs to the technical field of mechanical engineering, a multi-modal data fusion unit comprises an integrated multi-source sensor module and a data fusion analysis module, the integrated multi-source sensor module adopts a bionic cobweb topological structure to arrange a flexible fiber grating sensor, and the data fusion analysis module adopts a data fusion analysis module to analyze the flexible fiber grating sensor. The data fusion analysis module carries out fusion processing on multi-source data through a machine learning algorithm and constructs a multi-modal data input layer, and the data fusion analysis module also designs dynamic weight distribution through a process scheduling algorithm. A bionic cobweb topological structure is adopted to arrange the flexible fiber grating sensor, the strain self-compensation characteristic of the flexible fiber grating sensor is utilized to adapt to cutterhead deformation, a machine learning algorithm is utilized to fuse multi-source data, a multi-modal data input layer is constructed, dynamic weight distribution is achieved through a process scheduling algorithm, and the cutterhead deformation detection accuracy is improved. The problem of data islands of a traditional single sensor system is solved, and the monitoring precision and adaptability are improved.

The invention discloses a curtain wall structural adhesive damage detection method and device based on modal difference and digital twinning, and belongs to the technical field of digital twinning structural adhesive damage detection and evaluation. The problem that in the prior art, a traditional glass curtain wall structural adhesive damage detection and evaluation method based on the first-order inherent frequency is not sensitive to local boundary adhesive failure, and consequently the specific damage position cannot be positioned is solved. The method comprises the following steps: acquiring vibration data of a group of hidden framing glass curtain walls to be detected and a group of hidden framing glass curtain walls in a lossless state to obtain normal acceleration time history data; primarily screening second-order and third-order inherent frequencies, and judging whether the structural adhesive is damaged or not according to a difference constraint condition; further positioning the damage by using the boundary relative curvature modal difference, and judging whether the current measuring point is a damage point or not; and constructing a digital twinborn model and damage early warning, and outputting a parameter report and a visual damage evaluation result. The method effectively improves the boundary damage positioning precision, and can be applied to glass curtain wall structural adhesive damage detection.

The invention relates to the technical field of tunnel engineering safety monitoring, in particular to an autonomous controllable three-dimensional tunnel gushing analysis method and system. The method comprises the following steps: collecting multi-source heterogeneous geological, hydrological and construction data, fusing to obtain multi-source heterogeneous data, and constructing a dynamically updated three-dimensional geological-construction coupling digital twinborn model after alignment coding; carrying out multi-scale physical-AI hybrid modeling, correcting real-time data, carrying out water gushing evolution rolling prediction, and outputting prediction data; a three-dimensional visual platform is constructed, and multi-level linkage early warning is started based on a risk threshold value; the system comprises a data fusion module, a water gushing evolution rolling prediction module and a multi-stage linkage early warning module. By fusing geological data, hydrological monitoring data and construction parameters and combining three-dimensional visual rendering and deep learning fluid simulation technologies, the requirements for rapid early warning and timely decision making of water burst disasters in the tunnel construction process are met, and intelligent support is provided for engineering disaster prevention decision making.

The invention relates to a road section risk early warning method based on thunder-vision fusion perception, and the method comprises the following steps: S1, obtaining the high-precision space-time position information of a vehicle in a tunnel through the multi-source information fusion of a millimeter-wave radar and machine vision; s2, mapping the vehicle position under the global coordinate system to a coordinate system expanded along a road center line, and representing the vehicle position as a longitudinal projection position and a transverse offset along the road direction; s3, under the coordinate system, taking anti-collision time as a core index, combining lane judgment and relative speed, constructing a dynamic longitudinal risk grading model, and performing linkage triggering with a warning system; s4, constructing a regional risk integral model; and S5, speed limiting information is issued to all driving-in vehicles in real time through the variable speed limiting screen, and the rear-end collision risk is actively prevented and controlled. According to the method, the sudden risk response time can be shortened, the speed limit control response precision can be improved, the rear-end collision rate of the diversion and convergence areas can be reduced, false alarm interference can be reduced, and the tunnel driving safety and the information guiding efficiency can be improved.

An integrated system for detecting dam breaches, as an early warning system and for evacuation assistance, consisting of: A geoinformatics analysis module comprising a modeling processor with a graphics processing unit, configured to: analyze spatial, hydrological, and topographic data of a dam and its downstream area; generate hydrological models using probable maximum rainfall (PMP) data to determine probable maximum flood discharge (PMF); generate hydraulic models to simulate flood propagation and determine flood depth, flood extent, flow velocity, and flood arrival time; create flood vulnerability models, vulnerability models, and flood hotspot models using a multi-criteria decision-making (MCDM) framework; and generate evacuation plans for high-risk zones based on the models. A sensor monitoring subsystem includes: a distributed fiber optic sensor (DFOS) configured to monitor the structural load and deformation of a dam, and a radar level gauge configured to measure the water level in the reservoir; a microcontroller unit configured to receive and process sensor data from the sensor monitoring subsystem, to detect dam breaches based on abnormal structural movements detected by the DFOS or sudden drops in water level below predefined thresholds detected by the radar level gauge; a communication module configured to transmit warning messages when dam breaches are detected; a warning system configured to: send SMS notifications to residents in downstream high-risk zones via a GSM communication module, generate real-time alerts via an IoT platform on user devices, and activate siren systems in villages to issue loud alarms and voice announcements; and an evacuation assistance platform configured to provide real-time evacuation assistance in emergencies due to dam breaks, displaying site maps with nearby emergency shelters, evacuation routes, hospitals, government buildings and aid centers, showing hydrological layers including flood extent, water depth, flow velocity and arrival time of the water, and enabling real-time location tracking in relation to safety zones and flood-prone areas.

A warning system according to the present disclosure includes: a character information generation unit that generates, by using a captured image of the inside or the outside of a mobile body, character information indicating a character representing an event appearing in the captured image; and a warning information notification unit that issues a notification of warning information corresponding to the character information generated by the character information generation unit. In a warning method according to the present disclosure, a computer: generates, by using a captured image of the inside or the outside of a mobile body, character information indicating a character representing an event appearing in the captured image; and issues a notification of warning information corresponding to the generated character information.

The invention relates to the technical field of motion detection, in particular to an old people falling early warning system based on motion trail analysis, which comprises a gravity center identification module, a trend identification module, an inertia pushing module, a phase judgment module and a grade response module. According to the method, the vertical coordinates of the key points of the waist and the two feet in the image frame are acquired, and the height difference change is continuously tracked, so that the potential falling initial state can be identified from the static posture, the vector included angles among the three groups of bone points of the hip knee, the knee ankle and the shoulder pelvis are subjected to sequence analysis, and whether synchronous disintegration occurs in the coordination action is identified; the identified trend sequence is mapped according to the risk level, a differential response mechanism is applied, a high-density corresponding relation is constructed between the behavior trend and the alarm level, graded response and time sequence triggering of the falling risk are achieved, the accurate identification capability of the falling precursor action is enhanced, the sensitivity and discrimination of the alarm response are also improved, and the safety of the falling risk is improved. And misjudgment and missed judgment are effectively avoided.

The invention relates to the technical field of aerospace, in particular to low-altitude traffic flow airspace-oriented real-time planning, and provides a centimeter-level precision detection network covering a low-altitude airspace by integrating multi-dimensional data sources such as radar electromagnetic feature recognition, ADS-B (Automatic Dependent Surveillance-Broadcast) automatic monitoring, Beidou or GPS (Global Positioning System) space-time reference positioning and the like. The three-dimensional trajectory and motion situation of the aircraft are solved in real time, intelligent reconstruction of an airspace sector and adaptive optimization of a flight corridor are realized by adopting a dynamic programming algorithm driven by reinforcement learning based on the real-time pose data of the aircraft, and a flight conflict prediction model constructed by combining a space-time convolutional neural network is used for predicting the flight conflict. According to the method, potential risks can be pre-judged, an optimal avoidance path can be generated, full-process digital management and control from identity verification to airspace authorization can be realized by constructing an aircraft digital identity authentication system and a dynamic access control mechanism, and modules such as a three-dimensional navigation information service module, a low-altitude digital communication private network module and an intelligent early warning and warning system module are integrated. And the guarantee of full-life-cycle service is provided for the low-altitude aircraft.

The invention relates to a dimensionality reduction and self-coding fan temperature anomaly early warning method and system and a medium, and provides the dimensionality reduction and self-coding fan temperature anomaly early warning method for solving the problems of high false alarm rate and missing report rate in the prior art, and the dimensionality reduction and self-coding fan temperature anomaly early warning method comprises the following steps: data acquisition and feature construction: obtaining a sample feature matrix; feature dimension reduction and manifold construction based on manifold learning: performing dimension reduction on feature vectors, and mapping high-dimensional features to a low-dimensional manifold space; building an anomaly detection model based on an importance weighted auto-encoder; and acquiring operation data of the wind turbine generator in real time, inputting the operation data into the trained model to calculate an abnormal score, and if the abnormal score exceeds a set threshold value, triggering early warning. Real-time and accurate criteria are provided for wind turbine generator temperature state early warning, probability distribution of data can be estimated more accurately, effective processing and modeling of complex wind turbine generator temperature data are achieved, the adaptability and accuracy of an early warning system are improved, and the false alarm rate and the missing report rate are reduced.

The present invention belongs to the field of multi-equipment safety management and control for personnel and equipment clusters at an underground coal mine driving working face, and solves the problem of safety hazards caused by human-machine collaborative operations as the mechanization level of underground equipment increases. Provided are a system and method for safety monitoring and early warning of human-machine collaborative operation at a working face. The system comprises a safety management and control system for mobile equipment at a working face, which is used for performing in real time position, orientation and environmental sensing of individual equipment and behavior analysis of drivers; working-face collaborative intelligent wearable devices, which are used for environmental sensing of operating personnel and individual equipment and acquiring in real time the motion postures of the operating personnel; and a working face output control subsystem, which is used for identification and management of operating personnel entry and exit, position monitoring of personnel and individual equipment, operating personnel behavior identification and monitoring and working environment monitoring, and generating corresponding alarm instruction information. The present invention can realize human-equipment and equipment-equipment safety management and control during multi-equipment collaborative operation at a driving working face.

The invention discloses a foundation settlement early warning method and system based on an intelligent algorithm, and relates to the technical field of settlement early warning. During operation of the system, through combined use of various sensors, foundation settlement related data from multiple sources are collected and processed and fused in real time, foundation settlement is predicted through the intelligent algorithm, and the early warning effect is achieved. The settlement risk of different areas is evaluated by combining a space-time big data analysis technology, a settlement risk index GSI is calculated, an early warning grade is output, early warning decision making is carried out according to a prediction result and a set dynamic threshold value, the severity of the settlement risk is judged and classified, a system automatically generates an early warning report, and once early warning is triggered, the early warning is automatically carried out. The system automatically starts an emergency response scheme, dynamically adjusts reinforcement and intervention strategies according to the actual change of foundation settlement, provides a real-time visual interface of settlement data and early warning information, and displays the settlement state, the early warning level and historical data.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention relates to an automobile obstacle early warning system based on edge calculation. The system comprises a data acquisition module, an edge calculation module and an early warning module which are sequentially connected through a data transmission bus, the data acquisition module is used for acquiring the multi-source data of the surrounding environment of the vehicle acquired by the sensor module; the multi-source data comprises obstacle data, multispectral image data and environment data; the edge calculation module is used for performing fusion calculation on the multi-source data and performing obstacle recognition according to an obstacle recognition model to obtain an obstacle type and a corresponding obstacle danger degree; the early warning module is used for generating a corresponding early warning signal and an early warning execution instruction according to the danger degree of the obstacle; the early warning execution instruction is used for indicating the automobile early warning component to work according to the early warning signal. By adopting the system, the overall early warning response speed can be greatly increased, and the obstacle detection precision in a complex road environment can be greatly improved.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention discloses a full-life-cycle monitoring and risk early warning system for equipment leasing, particularly relates to the field of equipment leasing supervision, and is used for solving the problem of an illegal subleasing recognition blind area in the existing equipment leasing, and the system comprises the steps: extracting an operation period log corresponding to a bound account, and generating an operation behavior record; constructing an operation atlas according to the account control instruction sequence, and identifying behavior fragments with rhythm change and functional path mutation in the atlas as control mutation candidate sections; through structured regression comparison with a historical behavior template, screening out a non-regressive control path offset section which cannot be matched with a historical control mode; and in a plurality of continuous monitoring periods, counting time distribution of non-regressive behaviors, constructing a control jump distribution sequence, mapping a high-density section to a corresponding lease period, and completing illegal sublease risk early warning labeling in a contract database to realize whole-process risk monitoring of lease behaviors.

The invention relates to the technical field of water conservancy and hydropower engineering, in particular to a leakage early warning system for water plant activated carbon pool construction. The pouring early warning module is used for determining the leakage risk grade of the concrete for construction of the activated carbon pool of the water plant according to the anti-permeability characterization value and judging a corresponding strategy according to the leakage risk grade; comprising a water level detection unit for monitoring the real-time water level of the activated carbon pool in a preset monitoring period and an infrared detection unit for obtaining an infrared image of the outer wall of the activated carbon pool; the method is used for secondarily judging whether the construction of the activated carbon pool meets the preset standard or not according to the water level change rate under the condition that the construction of the activated carbon pool does not meet the preset standard according to the wetting characterization value of the outer wall of the activated carbon pool. The construction efficiency of the water plant activated carbon pool is improved.

The invention relates to the technical field of fire early warning, in particular to an early fire early warning system based on AI image recognition, and the system comprises an image collection module which is used for obtaining the video stream data of a monitoring area in real time; an AI image analysis module which is in communication connection with the image acquisition module and is used for receiving the video stream data and carrying out real-time analysis on video frames based on a pre-trained fire identification model so as to extract visual features related to the fire; and the early warning judgment module is in communication connection with the AI image analysis module and is used for receiving an analysis result of the visual features. According to the early fire early warning system and method based on AI image recognition, through video image analysis, the system can recognize weak flame or smoke characteristics at the initial stage of a fire and when naked eyes do not obviously see the characteristics, and the delay problem that a traditional smoke-sensing and temperature-sensing detector needs to wait for physical parameters to be diffused to the detector to be triggered is solved.

The invention discloses an air conditioner energy consumption adaptive management system and method based on dynamic feature selection, and relates to the technical field of air conditioner energy consumption management, and the method comprises the steps: building a system energy consumption digital twin model as a theoretical optimal energy consumption baseline; operating parameters are collected in real time, and key parameter subsets are screened through working condition recognition and feature importance dynamic evaluation; inputting the key parameters into the model to obtain theoretical energy consumption, and comparing the theoretical energy consumption with a measured value to generate an energy consumption deviation rate; smooth processing is carried out on the deviation ratio sequence, recognition is carried out in combination with a dynamic threshold value and various anomaly detection algorithms, and grading early warning is triggered; the system comprises four core modules, namely a digital twinborn model construction module, a key parameter dynamic screening module, an energy consumption deviation calculation module and a grading early warning triggering module. The method can adapt to different working conditions, accurately capture energy consumption abnormities, and effectively improve the intelligent level and accuracy of energy efficiency management of the air conditioning system.

A mobile track safety device (1.1), in particular a track worker warning system, for securing a work area (2) on a track section (3.1, 3.2) comprises a signal processing unit (19) for processing digital information, wherein the signal processing unit (19) has at least one interface (6) for receiving a position of at least one moving hazard object (8, 12), in particular a rail vehicle (12), from a tracking device (13.2, 17), and for receiving a position of at least one moving protection object (8, 16), in particular a track worker (16), from a tracking device (13.2, 17) and for issuing a safety command to secure the work area (2), wherein the signal processing unit (19) is configured to determine a hazard potential by comparing the positions of the at least one hazard object (8, 12) and the at least one protection object (8, 16) and to issue the safety command via the interface (6) depending on the determined hazard potential. A method for securing a work area (2) on a track section (3.1, 3.2), in particular by means of such a track safety device (1.1).

The invention provides an oil level monitoring and leakage early warning system for a hydraulic system of a dredger, which relates to the technical field of hydraulic system monitoring and comprises a hydraulic oil tank, an actuating mechanism assembly connected with the hydraulic oil tank through a pipeline, a data processing unit, a multi-source sensing module, an early warning module and a control module, the multi-source sensing module comprises an integrated liquid level sensor, an actuating mechanism displacement sensor group, an attitude sensor and a temperature sensor, and is used for collecting hydraulic oil level, displacement of each actuating mechanism, ship trim / heeling angle and oil temperature data in real time; the dynamic compensation calculation module is connected with the data processing unit; and the intelligent early warning module is connected with the data processing unit. Normal change and leakage can be distinguished by calculating the oil level change caused by the action of the executing mechanism and the posture of the ship, early warning is conducted in time, pollution and economic losses are prevented, and the situation that when workers inject hydraulic oil, the hydraulic oil splashes, bubbles are generated, and a hydraulic element is damaged can be prevented.

The invention relates to the field of coal mine geological disaster monitoring, in particular to a coal mine surface slope remote sensing data fusion monitoring method and system, and the method comprises the steps: collecting multi-source data through a satellite-unmanned aerial vehicle-ground three-stage network, and carrying out the cleaning, correction and standardization preprocessing; utilizing an improved LSTM model which introduces an attention mechanism and a geological parameter correction item to predict slope displacement and risk levels; distributing monitoring resources through a PSO (Particle Swarm Optimization) algorithm, dynamically adjusting an equipment state through a fuzzy PID (Proportion Integration Differentiation) algorithm, and fusing multi-source data through a Bayesian algorithm to generate a high-dimensional matrix; and finally, storing data based on the alliance chain and performing early warning based on a dynamic threshold value. The system correspondingly comprises six core modules. The problems of narrow coverage, poor timeliness and low data utilization rate of traditional monitoring are solved, millimeter-level precision monitoring is realized, the early warning response is less than or equal to 5 minutes, the cost is reduced by more than 40%, and the method is suitable for safety and ecological monitoring of various coal mine slopes.

The invention provides a geological disaster monitoring system based on intelligent hidden danger judgment, and relates to the technical field of geological disaster monitoring. The geological disaster monitoring system based on intelligent hidden danger judgment comprises a multi-source heterogeneous data acquisition module, a data fusion module, a dynamic threshold self-adaption module, a digital twinning and real-time early warning linkage module and an edge-cloud cooperative computing architecture module. Through multi-source data fusion, dynamic threshold self-adaption, digital twin linkage and edge-cloud collaborative architecture, precise recognition and real-time early warning of geological disasters are realized. According to the system, in a certain landslide monitoring scene, the early warning accuracy rate reaches 89%, the response time is shortened to 10 minutes, the geological disaster prevention and control capacity is remarkably improved, and through hardware standardized deployment, model refined training and process automatic linkage, it is ensured that the system stably operates in a complex geological environment, and the requirements for real-time monitoring and emergency response are met.

The invention discloses an AI algorithm-based road section violation behavior accurate identification and automatic warning system, and the system comprises an image acquisition module which is used for collecting a road monitoring image and carrying out the standardization processing; the feature extraction module is used for extracting semantic and structural information in the image based on a visual self-distillation network; the behavior modeling module is used for constructing a traffic behavior characteristic probability distribution model by using an improved multi-dimensional Gaussian modeling method; the anomaly detection module is used for comparing the current image features with the normal behavior model and outputting an anomaly score and a regional thermodynamic diagram; the warning judgment module judges whether warning is triggered or not according to the abnormal score and the state of the signal lamp; and the warning execution module is used for carrying out acousto-optic prompt, voice broadcast and image display through edge equipment. According to the method, image processing and AI identification technologies are fused, automatic identification and real-time warning of road section violation behaviors are realized, and the method has the advantages of high precision, fast response and easy deployment.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.