120 results about "Real time modeling" patented technology

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention provides a carbon emission accounting method and system based on an artificial intelligence technology, and belongs to the field of carbon emission. According to the technical scheme, the method comprises the following steps: constructing a sensor network of an equipment operation state, obtaining real-time operation data, and preprocessing the real-time operation data through an edge computing node; establishing a dynamic prediction model of emission factor boundary parameters based on the real-time operation data; according to the dynamic prediction model, constructing a carbon emission propagation map, analyzing the spatial and temporal distribution trend of carbon emission intensity, and identifying the position of an abnormal emission source; according to the abnormal emission source position identification result, carbon emission trend prediction is executed, and emission early warning response is triggered in combination with a preset threshold value. The method has the beneficial effects that dynamic prediction of carbon emission factors, identification of emission abnormal sources and trend early warning response are realized, the real-time modeling and space-time propagation analysis capabilities of the system on carbon emission behaviors are enhanced, and the intelligent level and response efficiency of emission management are effectively improved.

The invention discloses a multi-dimensional digital twinning modeling method. The method comprises the following steps: S1, performing dynamic fusion and real-time modeling on multi-source heterogeneous data in a complex scene; and high-precision multi-dimensional data expression is formed. S2, based on a cross-domain knowledge graph and a semantic reasoning algorithm, establishing semantic association among multi-dimensional data; and the cross-domain data fusion problem is solved. And S3, performing real-time evaluation and early warning on the system state through an anomaly detection and trend prediction technology. The defects of a traditional digital twin system in multi-scene data fusion and predictive analysis are overcome.

The invention discloses an intelligent safety control method for an exhibition, and the method comprises the steps: multi-sensor data fusion, real-time modeling and prediction, intelligent obstacle avoidance and planning, emergency braking and safety protection, cooperative control and communication, risk assessment and early warning, learning and self-adaption, and monitoring and manual intervention. The motion states of actors and stage equipment can be monitored and predicted in real time, and collision accidents are effectively avoided. And meanwhile, through the arrangement of an emergency braking system and protective equipment, the safety in the exhibition process is further improved. Besides, the control system has learning ability and adaptive ability, can automatically adjust safety control parameters according to different performance contents and stage arrangements, and improves the flexibility and practicability of the system.

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, device and equipment based on field monitoring and real-time modelling, and the method comprises the steps: collecting a topographic data file and an earth surface data text of a target area to construct a sub-meter DEM digital elevation model; radar real-time base data, radar and ground rainfall historical observation data, brightness temperature data and geographical coordinate information of a target area are obtained to solve actual radar real-time base data, rain short-term forecast data and soil moisture data, and then the data are utilized to calculate a preset hydrological model to obtain a mountain torrent gully basin distributed hydrological model result; and establishing a hydrological-hydrodynamic coupling model according to the sub-meter DEM digital elevation model and the torrential flood gully basin distributed hydrological model so as to forecast the current flood state and the future flood state. Therefore, the problems that an existing mountainous area hydrological model cannot completely reflect the current state of a drainage basin, and the simulation error of a hydrodynamic model is large are solved.

The invention relates to the technical field of meteorological short-term and imminent forecasting, in particular to a meteorological short-term and imminent forecasting method based on multi-source data fusion and real-time modeling, and the meteorological short-term and imminent forecasting method based on multi-source data fusion and real-time modeling performs spatio-temporal interpolation through a time sliding filling method in combination with a DEM-containing trust propagation algorithm. And complementing the missing data and expanding meteorological data of other non-automatic station monitoring areas in the target area at the same time so as to obtain complete high-precision ground three-dimensional meteorological data (10 minutes per 10 minutes, 1 kilometer-level forecast precision) used for representing meteorological data in a three-dimensional space of the target area, and updating the UNet model in real time through an incremental learning strategy so as to realize real-time prediction of the meteorological data in the three-dimensional space of the target area. And secondly, a physical constraint module of an output layer of the UNet model ensures that a forecast result accords with a meteorological dynamics rule, and SIMD instruction optimization enables the fusion efficiency to be improved by 8 times, so that the real-time service requirement is met.

The invention discloses an intelligent optimization and resource dynamic redistribution method and system based on a graph neural network. The method comprises the steps of data acquisition and modeling, network topology feature extraction, congestion detection and prediction, data flow redistribution and intelligent optimization, and real-time monitoring and feedback mechanism. According to the invention, real-time modeling and intelligent optimization of a network topology structure and a data flow are realized through the graph neural network model, so that a potential congestion area can be accurately identified and positioned; in combination with a flow redistribution strategy of a maximum entropy principle, the system can dynamically adjust the distribution of data streams and resources, relieve congestion and improve the overall transmission efficiency; by introducing an improvement rule of cascade failure, secondary congestion caused by failure of the congested nodes is effectively prevented, and the stability of the network is enhanced.

The invention relates to the technical field of laser deposition control, in particular to an unmanned aerial vehicle metal part ultrasonic field auxiliary laser deposition system which comprises the following modules: a data acquisition module for acquiring a molten pool thermal field signal, a sound field signal and a laser scattering signal, and performing sampling, filtering and synchronous processing to obtain molten pool original data; the micro-flow digital twinning module is used for establishing a molten pool micro-flow digital twinning model according to the molten pool original data and simulating molten pool micro-flow behavior to obtain micro-flow prediction data; and the microflow ultrasonic regulation and control module is used for generating an ultrasonic pulse sequence according to the microflow prediction data and applying the ultrasonic pulse sequence to the molten pool to regulate and control the flow field. According to the invention, through the molten pool microflow digital twinning module and the microflow ultrasonic regulation and control module, real-time modeling and regulation and control of the molten pool microflow structure in the complex geometric part deposition process are realized, the problem that local vortexes, shear layer differences and flow field anomalies cannot be identified in real time in the traditional technology is solved, and the molten pool microflow evolution can be actively reconstructed.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

The invention provides a space-time causal intelligent driving method based on an eVTOL aircraft, and the method comprises the steps: obtaining route information, and determining a take-off and landing place according to a terrain selection decision algorithm; according to a flight route preprocessing result, a preliminary route is preliminarily planned for the selected take-off and landing place; real-time modeling is carried out in the take-off and landing stages of the aircraft, real-time three-dimensional models near the take-off and landing places are constructed, and the aircraft is helped to achieve autonomous take-off and landing; using a space-time causal model to predict the occurrence probability and path of a dynamic obstacle in the route, and dynamically optimizing the initial route; when an air emergency occurs, a route is re-planned or a landing place is found nearby, and emergency landing is guided visually; and after the voyage is finished, the route information of this time is stored in the database in time, so that the accuracy of the next time-space prediction is improved. The method meets the operation requirements of complex and changeable urban air traffic and high flexibility.

The invention relates to a radar point cloud real-time modeling water conservancy earthwork measurement method and device, equipment and a medium. According to the method, a vegetation root system interference area is scanned and recognized through a dual-frequency polarization radar, a coordinate set of the vegetation root system interference area is obtained, an elevation compensation model is constructed based on dielectric constant difference and fractal dimension to generate a compensation parameter set, and original point cloud is processed by combining a bare soil reference curved surface and adopting a dynamic constraint particle filtering algorithm to obtain real ground point cloud. Then, a corrected terrain curved surface is generated through parasitic energy field constraint optimization, and finally, compensation parameters are fused to realize high-precision earth volume calculation in a root system interference environment, so that point cloud distortion and a terrain curved surface systematic lifting effect caused by a vegetation root system are greatly reduced, and an earth measurement result of a complex vegetation coverage area meets an engineering acceptance standard.

The invention provides a real-time modeling method and system for cooperative work of land and flight scanning devices, and relates to the technical field of scanning and modeling. The method comprises the following steps: preliminarily scanning a target area by using a land scanning device to generate three-dimensional point cloud data; a flight scanning device is used for conducting supplementary scanning on the complex area, and the coverage blind area of ground scanning is made up; fusing the land scanning data and the flight scanning data to generate a high-precision three-dimensional model; based on real-time data analysis, a flight path is dynamically adjusted to improve scanning efficiency and coverage rate. Through cooperative work of the land and the flight device, the modeling precision and efficiency are effectively improved, and the method is suitable for complex terrains and multi-scene application and has high real-time performance and reliability.

The invention discloses a sea-air target trajectory prediction method and device based on dynamic graph learning and a generative adversarial network, and a medium, and belongs to the technical field of sea-air target trajectory prediction, and the method comprises the steps: S1, data preprocessing; s2, dynamic graph learning and generative adversarial network (GAN) training are carried out, feature dimension relevance is modeled in real time through a dynamic graph structure, and a GAN module is utilized to generate adversarial trajectory features; s3, CNN-Transform hybrid model prediction is carried out, local feature extraction is carried out through a CNN module, global dependency modeling is carried out through a Transform module, and a decoder part is associated with a historical trajectory and future prediction through a cross attention mechanism; and S4, performing visualization and evaluation based on dimension reduction. According to the invention, the precision, robustness and interpretability of trajectory prediction in a complex scene are significantly improved.

The invention relates to the technical field of logistics storage management, and discloses a logistics storage system, which comprises an environment sensing module used for monitoring task points, path states, obstacle positions and dynamic order changes in a storage environment in real time, and generating path weight data; the master control module is connected with the environment sensing module and is used for receiving the order demand, generating a task point set, obtaining path weight data and carrying out global scheduling on the transfer robot according to the path weight data; and the path optimization module is connected with the master control module and is used for calculating the optimal path of the transfer robot based on the multi-agent game model. According to the technical scheme, the environment sensing module is adopted to perform real-time modeling on the storage environment, and the path weight data is generated, so that the technical effect of dynamically updating the path information is achieved, and the defect of path planning lag in the complex storage environment is overcome.

The invention discloses a stockyard inventory making method and system, and relates to the technical field of stockyard inventory making. The method comprises the steps of obtaining original point cloud data of a stockyard; filtering the original point cloud data to obtain filtered point cloud data; segmenting the filtering point cloud data to obtain target modeling point data and non-modeling point data; constructing a three-dimensional model of the material pile in the storage yard based on the target modeling point data; and the volume of the material pile is calculated according to the three-dimensional model, and stocktaking is completed. Based on the scanner and the server, the influence of environmental factors and flying dust interference on the point cloud data can be removed, and the optimal point cloud denoising effect is realized, so that a more accurate three-dimensional model can be established, and the stocktaking precision is improved. Besides, the scanner is installed on a riding track at the top of the greenhouse, the problem that the stocktaking function of an existing remote control system of the bucket-wheel stacker-reclaimer occupies the operation time of the bucket-wheel stacker-reclaimer can be solved, real-time modeling stocktaking is achieved, the refreshing rate is higher, and the stocktaking speed is increased.

The invention discloses a deep hole presplitting intelligent visiting and evaluating system and a method thereof, and belongs to the field of drilling peeping, and the system comprises a deep hole presplitting visiting bionic robot which is provided with a hydraulic dynamic reducing mechanism; the multi-modal fusion detection module is used for realizing omnibearing acquisition of monitoring data by adopting optical acoustic mechanics three-source sensing; the embedded real-time modeling module is internally provided with a large model and performs real-time three-dimensional modeling by programming a crack three-dimensional reconstruction algorithm; the edge calculation prediction module carries a heterogeneous calculation platform and is internally provided with a laser radar, and obstacle avoidance and accurate depth setting are carried out; and the communication and energy module adopts a dual-redundancy communication link, transmits high-broadband data by using an optical fiber composite cable, and transmits a key early warning signal. By adopting the deep hole presplitting intelligent visiting and evaluating system and the method thereof, the problems of low detection efficiency, single aperture adaptability and data dimension, insufficient analysis function and no prediction function of a traditional drilling peeping instrument are solved.

The invention relates to the technical field of unmanned aerial vehicle surveying and mapping, and discloses a pressure pipeline axonometric mapping system based on an unmanned aerial vehicle, and the system uses a pipeline anchoring relative navigation module to guide the unmanned aerial vehicle to fly along a line with a target pipeline as a reference; the outer diameter and three-dimensional center point coordinates of the pipeline are obtained in real time through an orthogonal contour scanning and parameter resolving module; a self-adaptive flight and measurement control module is used for adjusting the flight distance according to the measured outer diameter feedback, and adjusting sensor parameters according to the imaging quality feedback; meanwhile, the coordinates of the center point are incrementally constructed into a topological graph through an airborne real-time topological graph construction and node processing module, pipe fitting nodes are autonomously detected and recognized, and then a preset strategy is called to conduct refined scanning on the nodes. According to the invention, autonomous navigation independent of global positioning, self-adaptive closed-loop control of measurement and flight, and airborne integrated real-time modeling of pipe network topology and geometric information are realized, and the surveying and mapping precision, the automation level and the operation efficiency are remarkably improved.

The invention provides an intelligent control method and system for the wind speed of an electric fan self-adaptive to the environment temperature, and relates to the technical field of intelligent household appliance control. The method comprises the steps that the average environment temperature is calculated based on a first temperature value, a second temperature value and a third temperature value, and meanwhile the wind speed correction value is combined; generating an initial wind speed gear parameter according to a preset temperature-wind speed mapping rule; superposing the wind speed correction value to the initial wind speed gear parameter to generate a final wind speed gear control parameter; converting the final wind speed gear control parameter into a motor rotating speed control instruction, and adjusting the rotating speed of an electric fan motor in real time; wherein the temperature-wind speed mapping rule meets the requirements that the wind speed gear is increased when the temperature is increased and the wind speed gear is reduced when the temperature is reduced. Through a closed-loop process of synchronous acquisition, real-time modeling and dynamic correction, temperature change is quickly converted into a motor rotating speed instruction, and intelligent control over the wind speed of the electric fan is achieved.

The invention discloses an AR glasses real-time object scanning and modeling system, and the system comprises a hardware layer which comprises an AR glasses main body, and a high-precision camera, a depth sensor, a processor and a storage module which are integrated on the AR glasses main body; the data acquisition layer is used for carrying out multi-angle shooting on an object at a high frame rate based on the high-precision camera to obtain a two-dimensional image sequence, synchronously obtaining depth information of each point on the surface of the object by the depth sensor, and carrying out accurate time and space alignment on data of the depth information and the depth information; the algorithm layer comprises a feature extraction algorithm, a point cloud generation algorithm and a three-dimensional reconstruction algorithm; and the display layer is used for rendering the constructed three-dimensional model into a display interface of AR glasses in real time and displaying the model and a real scene in a fused manner. Real-time modeling is achieved, details are rich, a display layer renders in real time, various interactions are provided, and operation is convenient and visual. And a hardware synchronous calibration module and a parallel computing technology further improve the system performance.

The invention discloses an interactive behavior feature extraction method and device, equipment, a medium and a product, and relates to the technical field of computers, the method comprises the following steps: obtaining original live broadcast data generated in a live broadcast room, the live broadcast room comprising at least one interactive object; determining multi-modal live broadcast behavior data of the original live broadcast data in the target time window based on the multi-modal behavior data of the interactive object; dividing the multi-modal live broadcast behavior data into a plurality of behavior subsequences; based on the behavior data corresponding to the behavior subsequences, determining behavior cooperation features between the behavior subsequences, behavior interaction features between the interaction objects and time evolution features; and determining a target feature of the live broadcast room based on a fusion result of the behavior cooperation feature, the behavior interaction feature and the time evolution feature. By implementing the technical scheme of the invention, real-time modeling for the multi-behavior sub-sequence of the live broadcast room is realized, and the global feature of the live broadcast room level can be accurately extracted.

The invention relates to the technical field of automobile part conveying visual path correction, in particular to a servo motor conveying system for correcting a driving path through a visual AI algorithm, and the system comprises a path visual extraction module, a path characteristic calculation module, a dynamic region recognition module, a disturbance parameter modeling module, a servo path correction module and a path priority adjustment module. According to the method, a high-precision path boundary feature matrix is generated through real-time image data acquisition and edge feature extraction, accurate data support is provided for path analysis, path feature analysis is combined with gradient change intensity and a continuity index, the integrity and accuracy of path trend expression are enhanced, and the accuracy of the path trend expression is improved. According to the dynamic region division, through comprehensive analysis of curvature changes and texture indexes, the region recognition precision is improved, the illumination gradient, the texture ratio and the obstacle offset are modeled in real time, servo signal adjustment is optimized, path correction is faster, and the method adapts to complex dynamic scenes.

The present invention discloses a hysteresis modeling method for a pneumatic soft robot arm based on a P-I model, including: weighted superposition of play operators under different thresholds to form a P-I model; an asymmetric hysteresis model in a pneumatic soft robot arm is formed by a play operator, n rising operators, and n falling operators; and parameters in the asymmetric hysteresis model in the pneumatic soft robot arm are identified. Based on the basic P-I model, the present invention derives an MPI hysteresis model, and uses the properties of the P-I model and the operators of the MPI model at the state change point to obtain the P-I model and the weight calculation matrix of the MPI model. The parameter matrix is ​​universal for the normalized hysteresis model, and the model parameters can be easily obtained through the matrix, providing a solution for real-time modeling of soft robot arms and the like.

The invention provides an industrial robot anti-collision early warning method and system. The method comprises the following steps: environment perception and multi-source data fusion; predicting a dynamic obstacle track; robot posture real-time modeling is carried out; carrying out collision risk joint assessment; dynamically dividing early warning levels; intelligent planning of an obstacle avoidance path; dynamically compensating joint torque; multi-stage early warning collaborative response is carried out; performing iterative evaluation on the early warning effect; and self-evolving the anti-collision knowledge base. The three key technical problems in the industrial robot anti-collision field are solved, dependence on high-cost physical sensors (such as a torque sensor and a laser radar) is eliminated, collision detection of any position of the whole robot is achieved, and the cost is reduced by 60% or above. The limitation of a static obstacle model is overcome, and real-time track prediction and risk grading of dynamic targets such as moving personnel and conveying belt materials are achieved. Through multi-source data fusion and a risk probability model, the false alarm rate is reduced to 5% or below, and meanwhile it is guaranteed that the missing report rate is 0.

The invention belongs to the field of power transmission line tree obstacle detection, and particularly relates to a power transmission line tree obstacle detection method based on thunder-vision fusion, and the method comprises a data collection module which comprehensively collects the original data of the surrounding environment of a power transmission line through a laser radar, a camera and POS equipment. The advantages are obvious. Laser radar and camera data are fused, data registration and feature extraction technologies are applied, tree obstacles are accurately identified and positioned, the danger degree is accurately judged, and the detection precision is far higher than that of a single sensor. Quasi-real-time data transmission is achieved through the point cloud technology, and hidden dangers can be found in time in cooperation with real-time modeling and rapid distance measurement. The intelligent device integrates multiple sensors, automatically analyzes tree barrier risks, reduces manual intervention and reduces cost. In addition, detailed detection reports can be generated, support is provided for operation and maintenance, multi-element perception fusion enhances environmental adaptability, and stable operation of the power transmission line is guaranteed.

The invention provides a multi-source fusion heterogeneous data extraction method and system and a storage medium, and the method comprises the steps: constructing a dynamic hypergraph neural network and a dynamic hypergraph structure, carrying out the feature aggregation, and obtaining a space-time enhancement feature representation; performing causal reasoning and meta-learning optimization to generate robust feature representation; and by executing data reconstruction and fusion, outputting a heterogeneous data extraction result after multi-source fusion. According to the method, a dynamic hypergraph neural network, a sliding window and an attention mechanism are combined, data entity spatio-temporal evolution is modeled in real time, and the problem of correlation lag of a traditional static hypergraph is solved; then, a causal element learning confrontation module integrates causal reasoning, element learning and confrontation training to improve model robustness; finally, data reconstruction, meta-learning and adversarial loss are subjected to weighted fusion through a joint optimization strategy, the feature expression ability is improved, and the generalization performance of the model in a small sample scene is ensured.

The invention discloses a lithium battery recycling and disassembling method based on AI + 3D vision, and relates to the technical field of battery recycling, and the method comprises the steps of S1, multispectral polarization image acquisition, S2, polarization parameter dynamic calibration, S3, image fusion, S4, deep learning denoising optimization, S5, 3D point cloud reconstruction and feature extraction, and S6, robot motion planning and execution. According to the method, a dynamic situation field model is combined with an impedance control principle, so that the robot can sense obstacle distribution in real time in a dynamic environment and autonomously adjust a motion strategy, and meanwhile, the contact force between an end effector and a target object is accurately controlled; a rejection and attraction field is modeled in real time, so that the sudden mechanical interference risk in the disassembling process is effectively avoided; the impedance coupling mechanism ensures that the robot maintains stable interaction force in contact operation through force feedback closed-loop adjustment, and battery damage or tool failure caused by sudden change of external force is avoided.

The invention relates to the technical field of computers, discloses a panoramic situation intelligent emergency decision management and control method and system based on cloud computing, and aims to solve the problems of serious information island, response lag, weak intelligent decision capability, disjunction of resource scheduling and emergency strategies, man-machine interaction separation and the like in an existing emergency management system. The method comprises the steps that multi-source heterogeneous data are collected through a global data access engine, and a unified data lake is formed through data fusion and cleaning; constructing a dynamic three-dimensional digital twinborn model to realize panoramic situation real-time modeling; and carrying out risk identification and graded early warning by using a deep neural network. According to the technical scheme, global situation second-level perception, risk active prediction, scientific auxiliary decision making and integrated collaborative management and control are realized, and the emergency response efficiency and the decision making intelligence level are remarkably improved.

The invention discloses an AI-driven vehicle-mounted sound field real-time modeling and voice separation method, and relates to the technical field of voice signal processing. A main control unit comprising a time sequence synchronizer, a resource scheduler and a health monitor is constructed. 3D sound field modeling is carried out by adopting a lightweight STCN + bidirectional LSTM network, adaptive updating of the model is realized through EWC incremental learning, and a CNN-LSTM noise classification network and targeted suppression algorithms such as ANF / spectral subtraction are developed. The voice separation module adopts an improved Conv-TasNet architecture, 3D spatial constraint and a multi-task loss function are fused, and low delay is realized under INT8 quantization and pipeline processing. The system dynamically optimizes parameters through a real-time regulation and control unit, supports scene self-adaption, finally achieves a separation effect in a mixed noise scene, reduces the delay of the whole system, and effectively improves the definition and stability of vehicle-mounted voice interaction.