31932results about "Measurement devices" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

The invention discloses a reservoir flood control monitoring system and method based on digital twinning, and relates to the technical field of reservoir flood control monitoring, and the system comprises a multi-source data collection module which is used for building a digital twinning model according to the real-time monitoring data of the multi-source data collection module, and transmitting the digital twinning model to a reservoir flood control monitoring module. The coupling module is used for receiving the fusion data set provided by the digital twin module, performing dynamic simulation on the fusion data set, and providing dynamic prediction results of dam seepage, deformation and flood routing; the dynamic early warning module comprises a dynamic threshold adjusting unit and an early warning unit; compared with a traditional early warning system with a fixed threshold value, the system can adapt to changes of dam body states and environmental conditions, identify potential abnormal events in advance, provide sufficient time for flood control of a reservoir to perform emergency response and scheduling decision making, and improve the flood control efficiency. Therefore, the disaster risk is effectively reduced, and the safety of reservoir operation is ensured.

The invention provides a data processing method based on AI environment monitoring and a server, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining an environment data set which is collected by a plurality of sensor nodes in real time in a target monitoring region and comprises air quality, noise intensity and meteorological parameters, and dynamically dividing monitoring sub-regions according to the data space distribution characteristics; a data feature extraction rule is allocated to each sub-region, so that abnormal fluctuation indexes and region association features are extracted, a sub-region feature set is generated, a pre-trained dynamic anomaly analysis model is used for carrying out multi-dimensional association analysis on the abnormal fluctuation indexes, the types and influence ranges of environment abnormal events are determined, and an environment optimization strategy set is generated; and executing the environment optimization strategy set and sending a regulation and control instruction, and updating a model parameter weight according to feedback data, thereby realizing intelligent processing and analysis of environment data, effectively coping with environment abnormity, and improving the environment monitoring and treatment level.

The invention discloses an asphalt pavement health state monitoring system and method, and belongs to the technical field of traffic infrastructure monitoring. The system comprises the following modules: a multi-source data acquisition module used for realizing real-time acquisition of pavement state parameters; the data processing module is used for performing space-time alignment, quality evaluation, exception handling and standardization on the acquired multi-source data; the multi-scale feature extraction module is used for extracting features from the microscopic diseases, the road section states and the network level, and constructing a multi-scale feature representation system; the dynamic graph analysis module is used for constructing a layered dynamic graph structure and realizing unified modeling and analysis of the road network health state; the health assessment module is used for realizing real-time assessment, deterioration trend prediction and risk early warning of the road surface health state; and the decision visualization module is used for displaying the monitoring data, the prediction result and the early warning information, and automatically generating maintenance decision suggestions based on the evaluation result.

The invention provides an inspection data monitoring method and server applied to mountain road and bridge early warning, and the method comprises the steps: firstly obtaining road and bridge remote sensing monitoring data containing current topographic distribution characteristics, road and bridge structure state parameters and historical disaster records, and then, according to the current topographic distribution characteristics, calling a topographic feature extraction model to segment a target mountain road section, generating inspection subareas and topographic feature tags, generating a dynamic flight path containing inspection nodes and flight parameters for the unmanned aerial vehicle through a path planning model according to the topographic feature tags and road and bridge structure state parameters, and then controlling the unmanned aerial vehicle to inspect according to the path. According to the method, multi-dimensional monitoring data such as surface deformation data, road and bridge crack images and slope displacement tracks are collected, finally, the multi-dimensional monitoring data and historical disaster records are input into a risk early warning model, risk level signals and disposal suggestion information are generated, comprehensive intelligent monitoring and early warning of mountainous area roads and bridges are achieved, and the monitoring accuracy and efficiency are improved.

The invention relates to an electrical cabinet state monitoring and intelligent inspection system based on multi-source data fusion, and aims at solving the problems that a traditional electrical cabinet monitoring method is low in efficiency, poor in real-time performance, limited in coverage range and the like. A monitoring platform with high reliability and high real-time performance is constructed by deploying a multi-source sensor network, adopting an edge and cloud cooperative computing model and combining a secure communication protocol design. According to the system, through comprehensive application of a dynamic path planning algorithm, a multi-modal data fusion diagnosis technology and an abnormity diagnosis and early warning model, the inspection efficiency and the fault diagnosis precision are effectively improved. The system not only shows significant advantages in the aspects of data fusion precision, dynamic path planning efficiency and robustness of an anomaly diagnosis model, but also can provide accurate and real-time monitoring and response in a complex industrial environment, and provides reliable guarantee for safe operation of an electrical cabinet.

The invention relates to the technical field of data processing, and discloses a bridle machine fault monitoring method and system based on the Internet of Things. The method comprises the following steps: collecting multi-source operation data through a sensor; performing filtering segmentation processing on the data to obtain feature data; analyzing the feature data to obtain an abnormal identifier and a fault type; establishing a component association map to position a fault source and predict a development trend; calculating a maintenance priority to generate a maintenance scheme; and recording the actual maintenance result and the prediction deviation to update the fault library and the knowledge parameters. Through multi-source data fusion, intelligent fault diagnosis, predictive maintenance and knowledge self-adaptive updating mechanisms, early recognition and accurate positioning of faults of the bridle machine are realized, the non-planned downtime is effectively shortened, and the equipment reliability and the production efficiency are improved.

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

The invention discloses an automatic instrument fault prediction system and method based on big data analysis, and belongs to the technical field of fault detection. The system comprises the following modules: an intelligent data processing and normalizing module which collects multi-source heterogeneous data of an instrument and an environment sensor in real time and performs data cleaning, standardization and quality evaluation; the working condition environment characteristic analysis module is used for identifying the current working condition state and quantitatively evaluating the influence degree of environmental factors on instrument operation; the multi-monitoring-parameter coupling analysis module is used for calculating and analyzing the mutual influence relationship among the monitoring parameters of the instrument and evaluating the coupling strength and influence links among the monitoring parameters in real time; the dynamic threshold calculation module is used for dynamically calculating and adjusting an early warning threshold system of each monitoring parameter; the fault prediction decision module is used for comprehensively evaluating various monitoring parameters and calculating a fault risk probability; and the early warning output and feedback module optimizes early warning output through an intelligent filtering mechanism, and collects early warning effect feedback for continuous optimization.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention provides a power transmission and transformation project construction intelligent management system based on a digital twinborn technology. The system comprises a digital twin model construction unit, a data acquisition unit, a data processing and anomaly detection unit, a construction process intelligent optimization unit and a control unit. The digital twin model construction unit is responsible for constructing a three-dimensional model of a power transmission and transformation project. The data acquisition unit is configured at a construction site, collects sensor data about construction progress and quality, and maps and compares the sensor data with the digital twinborn model. The data processing and anomaly detection unit analyzes real-time data, identifies construction anomaly and potential safety hazards, and carries out positioning and analysis through a digital twin model. And the construction process intelligent optimization unit optimizes a construction path and resource allocation in real time, and simulates and verifies on a digital twin model. And the control unit provides a user interface, so that a user can monitor the construction progress in real time, receive an alarm and make a decision.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention relates to the technical field of engineering data monitoring, in particular to a hydraulic engineering data intelligent monitoring method and system based on digital twinning, and the method comprises the following steps: collecting real-time operation data through physical sensors disposed at different nodes of a hydraulic engineering; a self-adaptive digital twinborn model capable of being automatically adjusted is generated through a meta-learning method, and the self-adaptive digital twinborn model dynamically adjusts the internal structure of the self-adaptive digital twinborn model according to the change of real-time data; and on the basis of the adaptive digital twinborn model, constructing a simulation module of a plurality of target scenes, and performing decision simulation in a virtual environment in real time. According to the method, the accuracy and the stability of the digital twinborn model in a complex environment are improved, the model can quickly adapt to real-time changes in the operation process of a water conservancy project through the self-adaptive mechanism, and the prediction precision and the response of the model in a dynamic complex scene are improved.

The invention relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The invention belongs to the technical field of rail transit intelligent monitoring, and particularly relates to an intelligent monitoring method and device for a rail transit air conditioning system. According to the method, the sensor array with the self-adaptive sampling frequency is used for collecting the multi-modal operation data, then the collected multi-modal operation data is preprocessed, the feature degradation track atlas is established, powerful data support is provided for subsequent fault early warning and diagnosis, and the fault diagnosis accuracy is improved. Historical abnormal events are introduced to dynamically correct the health state evaluation base line, the timeliness and accuracy of the evaluation base line are ensured, in comparative analysis of real-time operation data and the dynamic evaluation base line, a health degree scoring and dynamic threshold mechanism is adopted, quantitative evaluation of the health state of the air conditioner system is achieved, and the evaluation accuracy of the health state of the air conditioner system is improved. According to the method, a decision graph containing fault diagnosis and predictive maintenance suggestions is generated by analyzing the propagation path and time sequence relevance of abnormal parameters in the air conditioning system and combining an equipment topological relation graph, so that the troubleshooting and repairing efficiency is improved.

The invention discloses a livestock breeding house environment detection method and system, and relates to the technical field of livestock environment intelligent perception and regulation, and the method comprises the steps: collecting original thermal image data, original audio signals and original gas data, and generating a space-time alignment multi-modal data set; performing space-time gridding processing on the multi-modal data set, extracting temperature gradient, voiceprint MFCC characteristics and gas concentration space distribution, and constructing a temperature-voiceprint-gas three-dimensional incidence matrix; based on the temperature-voiceprint-gas three-dimensional incidence matrix, dynamically compensating the body surface temperature of the animal in combination with the temperature and humidity index, inputting the gas concentration as a weight factor into the LSTM time sequence prediction model, and outputting a stress risk level and a pollution risk level; and according to the stress risk level and the pollution risk level, generating and executing a corresponding environment regulation and control strategy. According to the invention, high-precision monitoring and dynamic regulation and control of the animal house environment state can be realized, the animal health guarantee level is improved, and the breeding risk and the operation and maintenance cost are reduced.

The invention discloses a municipal road state sensing method and system based on sensor fusion, and the method comprises the steps: collecting multi-modal road information data from a sensing network along a municipal road, and generating a standardized road feature vector; the method comprises the following steps: extracting frequency domain characteristics of pavement bearing capacity through time-frequency analysis, establishing a coupling model of vehicle load and pavement response, and calculating various road state indexes; outputting a structure health index and marking a thermodynamic diagram of a key damage position; real-time traffic flow data and meteorological environment parameters are fused, and an early warning level adaptive adjustment model based on fuzzy reasoning is established; and when a risk entropy value output by the early warning level adaptive adjustment model exceeds a dynamic threshold value, triggering a multi-level collaborative maintenance decision scheme and generating a visual road state digital twinborn body. According to the invention, the problem of insufficient road state sensing precision in a complex environment is solved, and dynamic evaluation and intelligent early warning of the full life cycle health state are realized.

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention discloses a water turbine state intelligent prediction method which comprises the following steps: collecting multi-source sensor data of a water turbine, including vibration, temperature, pressure, flow and electrical parameters; performing space-time alignment preprocessing on the multi-source sensor data to generate a space-time associated data set; extracting and fusing the features of the space-time associated data through a multi-modal space-time diagram network, and generating a joint feature vector; performing health state prediction on the joint feature vector based on a dynamic digital twinborn model, and outputting a health score, a fault probability and a confidence interval; analyzing a fault propagation path by using a causal reasoning module, positioning a fault root cause and generating an interpretable report; and triggering an early warning or maintenance decision according to the prediction result. According to the technical scheme, the technical problems that multi-source heterogeneous data fusion is difficult, fault coupling and propagation are uncertain, real-time performance and computing resources are contradictory, and interpretability and reliability are insufficient are solved.

Provided is an intelligent monitoring system for an oil development platform. The system includes external environment monitoring of a well platform, internal environment monitoring of the well platform and platform construction, where the external environment monitoring of the well platform includes real-time monitoring of natural environment conditions around an offshore oil development platform, the internal environment monitoring of the well platform includes collection and analysis of various indexes inside the oil well platform, and the platform construction is based on a three-layer structural design including a sensing layer, a transmission layer and an application layer. The sensing layer collects environmental data of ocean and the well platform through an Internet of Things technology, the transmission layer rapidly transmits data to ensure real-time updating of information, and the application layer processes received data to realize fusion and visualization of the data.

The invention relates to the technical field of bridge structure safety monitoring and maintenance, and discloses a road bridge real-time monitoring and maintenance system based on intelligent perception, which comprises a data acquisition module used for acquiring multi-dimensional data of a bridge operation state through a deployed multi-source sensor, the multi-source sensor comprises a vibration sensor, a strain sensor, a temperature and humidity sensor and image acquisition equipment; the data preprocessing module is used for carrying out cleaning, synchronization and feature extraction on the collected multi-dimensional data to obtain dynamic features, environment features and visual features; and a data fusion module. According to the method, the comprehensiveness and accuracy of bridge monitoring are improved through multi-source data fusion, the reliability of an evaluation result is ensured in combination with physical constraint optimization, health trend prediction and early warning functions are realized based on a time sequence model, scientific decision support is provided for bridge maintenance, and the monitoring and management efficiency is improved.

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention discloses a full-life-cycle-based medical equipment monitoring analysis system and method, and relates to the technical field of medical equipment monitoring, and the method comprises the steps: collecting medical equipment data in real time, and dynamically constructing a full-life-cycle digital twin model of medical equipment; constructing a medical equipment knowledge graph based on the equipment type, the function association and the spatial distribution; when the medical equipment node detects abnormal data, an early warning signal is sent to a full-life-cycle digital twin model associated with the medical equipment in the medical equipment knowledge graph in combination with the medical equipment knowledge graph; preliminarily judging fault causes and fault location, and generating an analysis report; performing multi-dimensional verification on the diagnosis result in the digital twin environment, comprehensively evaluating the risk coefficient of the scheme, and outputting an optimal maintenance strategy; in the maintenance process, the maintenance process is recorded in real time, and maintenance data and equipment state updating are synchronously fed back to the equipment full-life-cycle digital twin model.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent construction site management method and system based on the Internet of Things. The method comprises the following steps: acquiring construction site environment monitoring data in real time based on multiple Internet of Things sensor nodes; performing real-time environment state change mining on the construction site environment monitoring data, performing global environment perception modeling, and constructing a construction site digital environment perception map; obtaining a construction site omnibearing monitoring video and construction site construction equipment state monitoring data; carrying out target dynamic optical flow tracking on the omnibearing monitoring video of the construction site one by one, carrying out dynamic operation state evolution, and constructing a real-time behavior portrait model of each worker; and performing operation state equipment temperature change trend analysis and dynamic load change evolution according to the state monitoring data of the construction site construction equipment, and generating comprehensive state characteristics of each piece of equipment. The construction site management efficiency and the construction site construction safety are improved.

The invention relates to the technical field of road surface monitoring and control, and discloses a deformation monitoring and control method and system for a road surface and a storage medium. The method comprises the following steps: carrying out multi-level sensing data acquisition on a road surface; preprocessing the data to obtain a time series data set; inputting an LSTM network to predict a deformation risk; determining a dominant factor generation grading strategy; converting the instructions into execution instructions and responding according to priorities; and constructing a knowledge graph based on feedback, modeling as a Markov process, and training a deep Q network to realize adaptive regulation and control. According to the method, accurate identification of the pavement deformation state, advanced prediction of the deformation trend, accurate analysis of the deformation reason and adaptive optimization of the control strategy are realized, so that the road maintenance efficiency is improved, the service life of the road is prolonged, and the driving safety is guaranteed.

The invention provides a passenger and goods elevator roof bearing capacity evaluation method with a self-adaptive early warning function, which comprises the following steps of: acquiring data of a multi-source sensor, and performing data acquisition and preprocessing by adopting a dynamic synchronous sampling optimization algorithm; extracting time-frequency characteristics, separating load modes by adopting a multi-dimensional time-frequency analysis algorithm, and constructing a dynamic load spectrum; establishing multi-scale state mapping, calculating system response by adopting a generalized dynamic response analysis method, and evaluating structural risk; and constructing a self-adaptive early warning system, dynamically optimizing an early warning threshold value, and generating an optimal decision scheme. According to the method, the sampling strategy can be adaptively adjusted, the load characteristics can be accurately recognized, multi-scale state evaluation is achieved, early warning decisions are dynamically optimized, and the accuracy and reliability of people and goods elevator top plate bearing capacity evaluation are remarkably improved.