342 results about "Risk map" patented technology

The invention relates to the technical field of agricultural information perception and decision making, in particular to a multi-source perception fused agricultural monitoring method and system. The method comprises the following steps: converting a multi-source heterogeneous agricultural sensing signal into a space-time tensor, constructing a semantic resonance field to simulate nonlinear coupling between modals, and driving multi-modal data to adaptively aggregate by using a gravitational evolution mechanism to form a fused semantic field; calculating non-linear response to generate an agricultural state emergence index, and according to the index, identifying a potential risk area and constructing a binary risk map; for the risk area, semantic disturbance is mapped into an agricultural variable disturbance vector through a modal decoupling matrix, a minimum intervention strategy is generated in combination with sparse optimization of an operation response matrix, and feasible operation suggestions are output after verification of an agricultural knowledge graph; a drift potential energy function is constructed based on strategy execution feedback, strategy parameters are dynamically updated through gradient descent, and closed-loop self-evolution optimization is achieved in combination with trend prediction. According to the invention, full-link adaptive optimization from multi-source sensing to regulation and control decision is realized.

The invention discloses an industrial network risk perception and collaborative early warning method based on a dynamic risk map, and relates to the technical field of industrial internet security, and the method comprises the steps: S1, multi-source perception deployment; s2, heterogeneous data fusion acquisition; s3, constructing a knowledge graph engine; s4, analyzing depth data; s5, performing dynamic risk assessment; and S6, intelligent early warning decision making. According to the industrial network risk perception and collaborative early warning method based on the dynamic risk map, through fusion perception of OT layer data such as equipment states and process parameters, the problems of single perception dimension, evaluation lagging and disjunction in the prior art are solved, the false alarm rate is extremely low, and the method is suitable for popularization and application. Particularly, a dynamic adjustment mechanism of a time-varying risk weight matrix is improved, novel attacks can be dynamically responded, meanwhile, cross-domain risk conduction analysis is achieved, the accuracy and response speed of industrial network security early warning are improved, and meanwhile a closed-loop mechanism of attack path prediction and disposal suggestions is constructed.

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

The invention discloses a goaf unmanned aerial vehicle inspection system based on monitoring and early warning, and the system comprises an observation data collection module which is used for collecting settlement and fracture main variables to form a time stamp observation set; the data fusion module is used for generating a unified monitoring data set by adopting a fractal small-world network coding consensus algorithm; the initial route planning module is used for constructing a route sequence based on a main variable evolution tensor and a risk map; the flight path execution module is used for collecting a flight path and environment data; the fault-tolerant control module triggers a fractional order sliding mode fault-tolerant and degradation mechanism based on the consistency error; and the closed-loop optimization module is used for dynamically updating the main variable tensor and the path cost function and outputting an optimized track and a structured early warning result. The system has high dynamic responsiveness and multi-source risk adaptability.

The invention relates to the technical field of soft robots and intelligent control, in particular to an intelligent control method based on the Internet of Things. The method comprises the following steps: carrying out multi-modal data acquisition and preprocessing on a contact process of a gripper and an object to obtain a time synchronization data set; constructing a tactile feature tensor including pressure, friction force and strain force according to the time synchronization data set; performing stress gradient calculation and mapping according to the tactile feature tensor to obtain a stress gradient field and a region threshold mapping table; performing stress gradient threshold adaptive adjustment according to the regional threshold mapping table and the stress gradient field to obtain an adaptive threshold distribution map; and performing stress risk prediction according to the adaptive threshold distribution map to obtain a stress risk map. According to the method, the potential damage risk is predicted by sensing the contact state of the gripper and stress concentration, the adaptive capacity, robustness and success rate of the grabbing process are remarkably improved, and the method is particularly suitable for grabbing tasks of fragile and irregular objects which are difficult to prejudge.

The invention provides a new energy automobile fire dynamic risk assessment method and system based on multi-modal spatial-temporal feature fusion. The new energy automobile safety monitoring and fault early warning method comprises the steps that a multi-source data acquisition layer obtains multi-source heterogeneous data, and space-time marking is carried out; the feature fusion preprocessing layer generates a space-time consistency data stream through a space-time alignment module and missing value filling; the deep learning evaluation layer extracts multi-modal features by using a dedicated network, and outputs feature scores through time-space dependence modeling fusion; and the dynamic early warning decision-making layer generates a dynamic risk score in combination with the thermal runaway probability, the risk gradient and the aging factor, constructs a five-level risk map and executes vehicle-cloud collaborative graded early warning. According to the new energy automobile fire dynamic risk assessment method and system based on multi-modal spatial-temporal feature fusion, the limitation of traditional single data static assessment is broken through, accurate dynamic early warning is achieved, the early warning time is longer than or equal to 15 min, the false alarm rate is reduced to 4.7%, and the thermal runaway prediction accuracy rate reaches 98.3%.

The invention discloses a building construction process risk assessment method and system based on a fault tolerance mechanism, and particularly relates to the field of building construction process risk assessment, and the method comprises the steps: building a measurement point hierarchical index of a building construction process, synchronously arranging time sequence displacement data, and executing difference calculation to obtain a space gradient evolution sequence; mapping a trend change interval set extracted by sliding window processing into a path node set, and constructing a topological connection relation of the path node set to form an initial risk path map; the data state of each path node in the risk path map is subjected to anomaly identification, the access control effect of the abnormal node in the path is analyzed, and whether the path fracture risk is formed or not is judged in combination with the sensitive monitoring area. By constructing a risk map structure based on a trend path and combining a path interruption node identification and alternative path dynamic deduction mechanism, continuous assessment and map self-correction of the risk trend under the condition of measuring point data failure are realized.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The invention discloses an adaptive neural network flood routing simulation and risk assessment method. The method comprises the following steps: step 1, generating multi-source hydrometeorological preprocessing data; 2, constructing a river network topological graph, generating node feature vectors and edge feature vectors, and forming time-space diagram input data; 3, inputting the liquid state time constant neural network to execute continuous time state updating, and generating a node hydrological state prediction vector set; step 4, injecting hydrodynamic physical consistency residual errors to generate a physical constraint hydrological state prediction vector set; 5, executing adaptive lag compensation, and generating a lag compensation hydrological state prediction vector set; 6, mapping to generate flow, water level and flood peak arrival time prediction data; and step 7, outputting a risk grading graph and early warning threshold list data. According to the invention, accurate prediction of the flood process and dynamic generation of the risk map are realized, and early warning precision and response efficiency are improved.

Disclosed in the present invention are a low-altitude air route risk map construction method and system based on hexagonal grid cells. The method comprises: on the basis of management and control requirements for different types of airspace, forming low-altitude airspace three-dimensional hexagonal hierarchical grids by means of multi-scale partitioning of an airspace horizontal plane and multi-scale partitioning of an airspace vertical plane; constructing an unmanned aerial vehicle flight risk assessment indicator system for the low-altitude airspace three-dimensional hexagonal hierarchical grids, and after optimization, constructing an unmanned aerial vehicle operational risk assessment model based on a dynamic Bayesian network, so as to generate multi-scale grid risk values; and combining the multi-scale grid risk values with geographic location information of a region, designing a hexagonal grid code index, and generating a dynamic multi-scale low-altitude air route three-dimensional hexagonal risk map. The present invention can improve the accuracy and real-time performance of unmanned aerial vehicle flight risk assessment of grids, quickly obtain low-risk grids of an unmanned aerial vehicle in a target region, and improve the safety of air route planning.

The invention relates to the technical field of pipeline monitoring and artificial intelligence, in particular to a multi-source data fusion pipeline monitoring method and system. The method comprises the steps of performing field sorting, structure unification and risk segmentation processing by obtaining pipeline line data, historical operation archives and strategy update configuration records, and generating a session primary key configuration table; a multi-source acquisition time window is configured, an acquisition task is issued, time anchor point registration and field aperture unification are completed, and a multi-source session data packet set is generated; performing session and risk unit association, performing multi-modal feature extraction and cleaning aggregation based on artificial intelligence, and constructing a pipe network risk map structure by using a map structure data model; and calling a multi-task reasoning model and a rule component based on the atlas, and performing risk type reasoning and grade judgment to obtain a risk assessment result and a strategy updating record. According to the invention, intelligent fusion of multi-source data and closed-loop optimization based on machine learning can be realized, and intelligence and reliability of pipeline safety management are effectively enhanced.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention provides a pile foundation pore-forming quality control method under complex karst geology, which comprises the following steps: generating a karst cave distribution feature set and a stratum abnormal point set based on karst geology survey data; then, determining a karst cave development trajectory sequence set, carrying out risk partitioning, and identifying a high-risk karst cave development region set; according to the method, a high-risk hole site set and a high-risk stratum node set are determined, and then a karst geological risk map is constructed. And based on the map, generating a pore-forming process path set and a pore-forming risk thermodynamic diagram, and superposing the pore-forming process path set and the pore-forming risk thermodynamic diagram to the pile foundation construction plan to form a pile foundation pore-forming risk thermodynamic diagram for visually displaying construction risks and guiding pile foundation construction. According to the method, the quality and safety of pile foundation hole forming under the complex karst geological condition can be improved, and the construction risk is reduced.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention relates to the technical field of intelligent fire safety linkage control, in particular to an intelligent locking linkage control system and method based on fire monitoring, and the system comprises an acquisition module, a modeling module, a simulation engine module, a decision center module and an execution module. The acquisition module acquires physical quantity data of a key area of a building, wherein the physical quantity data comprises high-temperature radiation spectrum offset, aerosol particle swarm distribution characteristics and local heat convection intensity. The modeling module generates an environmental interference confidence index through convolutional neural network fusion features, and starts an incremental clustering algorithm to update an interference knowledge base when the confidence is insufficient. And the execution module dynamically compresses the delay window according to the risk map, and triggers a cross-level linkage mechanism through a people flow density threshold. And the environment sampling frequency adjustment coefficient is fed back to the acquisition module, the execution state data reverse drive modeling module iteratively updates the knowledge base, a Bayesian optimizer is combined to screen high-value characteristics to reconstruct simulation parameters, and full-link closed-loop learning and continuous evolution of the false alarm suppression capability are realized.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle low-altitude complex obstacle detection and avoidance system based on multi-sensor fusion, and the system comprises a multi-mode sensing module which is used for synchronously collecting heterogeneous sensing data of the surrounding environment of an unmanned aerial vehicle; and the sensing fusion processing module is in communication connection with the multi-mode sensing module and is used for carrying out timestamp alignment, space coordinate system unification and deep fusion processing on the heterogeneous sensing data, and all-weather and high-reliability environment sensing is realized through multi-sensor fusion and hardware synchronization. The system has dynamic obstacle prediction and three-dimensional risk map construction capabilities, and realizes crossing from passive obstacle avoidance to active foresight obstacle avoidance. And a safe and smooth flight path is generated in combination with hierarchical planning and model prediction control, so that the autonomous flight safety, reliability and intelligent level of the unmanned aerial vehicle in a low-altitude complex environment are fundamentally improved.

The invention relates to the technical field of safety management, in particular to a mine safety production risk prevention and control method based on multi-source data fusion, and the method comprises the following steps: constructing a coupling parameter set through a micro-seismic sensor and a temperature and humidity sensor, detecting and extracting displacement points through Bayesian regression, and drawing a deformation diagram; determining a displacement area, counting an airflow velocity gradient, extracting a main fracture direction, generating an airflow path diagram, calculating a curvature change rate, positioning a sudden change area, extracting a thermal strain rate and an axial strain rate, generating a smooth curve, identifying a risk point, and expanding a boundary when the overlapping rate exceeds 60%, and redrawing a mine thermal-mechanical coupling risk map. According to the method, a multi-parameter analysis framework is constructed through micro-seismic and temperature and humidity data coupling to extract a deformation active band, difference statistics airflow velocity gradient is combined with a crack direction to correct an airflow path, a sudden change area is positioned based on a curvature change rate mean threshold, and thermal strain and axial strain are extracted to screen risk points. And constructing thermal variable amplitude mapping to represent the rock mass instability risk.

The invention discloses a tower crane monitoring system in an intelligent construction site project, which comprises a multi-modal sensor module, a digital twin modeling module, a dynamic risk map generation module and a display terminal, and is characterized in that the multi-modal sensor module covers the whole domain of a tower crane and multi-dimensional data of real-time operation of the tower crane, so that the problem of data isolation of a traditional system is solved; a tower crane three-dimensional dynamic model is constructed based on real-time multi-dimensional data, stress distribution and motion trail of a physical entity are simulated, the position prediction precision of a lifting hook is improved, and a three-dimensional risk thermodynamic diagram is generated through a dynamic risk map generation module, so that a worker can more visually check the risk level of each part of the tower crane, intelligent early warning is realized, and the working efficiency is improved. And the fault occurrence probability is reduced.

The invention provides a mountain area highway emergency rescue path planning method and system. The method comprises the following steps: collecting traffic flow data, meteorological data and field state data of a disaster site; generating a dynamic risk map according to the meteorological data, the field state data and the traffic circulation data; determining rescue constraint conditions of the rescue bodies in the emergency rescue process according to the dynamic position information of the rescue bodies near the disaster site and the rescue body types of the rescue bodies; performing hierarchical scheduling on each rescue body based on the dynamic risk map and all rescue constraint conditions to obtain a space-time conflict matrix; and performing multi-target real-time path planning on each rescue body according to the space-time conflict matrix, and generating a space-time conflict-free collaborative rescue path set. By adopting the scheme of the invention, efficient cooperation of multiple rescue bodies can be realized in the multi-disaster-chain coupling evolution environment of the highways in the mountainous area so as to generate a global optimal rescue path without conflicts in time and space.

The invention provides an intelligent identification and early warning method for scientific and technological consultation project risk factors, and the method comprises the steps: obtaining a calculation cluster use record, training time period reservation data and department task distribution data in a scientific research consultation project, and carrying out the map node mapping of cluster conflict and training reservation data, constructing a resource competition distribution characteristic spectrum containing department cooperation contradictions; when the interference degree exceeds a threshold value, task priorities and resource requirements of all departments are obtained, a task overlapping dependency subgraph is constructed, and task allocation imbalance indexes are quantitatively analyzed; extracting distributed data interface configuration information, detecting inconsistency of a protocol and task distribution data in combination with a task distribution imbalance index, and identifying an influence path of data synchronization delay; and analyzing a dependency path between the data synchronization delay influence path and resource competition risks in the distribution characteristics of computing resource competition, and constructing a risk map containing department cooperation contradictions and linkage risk diffusion to obtain risk factor distribution.

The invention discloses an enterprise production risk hidden danger identification method and system based on AI, and the method comprises the steps: building a dynamic risk map through a deep space-time attention mechanism according to the production environment multi-source data collected in real time; based on the dynamic risk map, carrying out risk propagation modeling by adopting a dynamic edge weight adjusted graph neural network to obtain a risk node set and a diffusion path thereof; according to the risk node set, constructing a dual-stage reinforcement learning model to optimize the disposal schemes, and outputting an optimized disposal scheme set containing priority ranking; based on production state data fed back in real time, a risk disposal instruction set is generated, the dynamic risk map is synchronously updated, and visual early warning signals and disposal path guidance are output through a multi-modal interaction interface. By utilizing the embodiment of the invention, full-process closed-loop management of multi-source data fusion, dynamic risk propagation modeling, intelligent disposal scheme optimization and visual interaction can be realized, so that the intelligent level of enterprise production safety management is improved.

The invention provides a coal mine personnel safety situation dynamic sensing method, device and equipment and a medium, and the method comprises the steps: carrying out the time-space reference unification of the obtained multi-source heterogeneous data of underground operation personnel, and obtaining the time-space synchronization feature data, which comprises positioning data, environment data and inertial motion data; inputting the positioning data and the inertial motion data into a pre-trained meta-learning behavior recognition model to obtain a behavior label of the operator; constructing a dynamic coupling risk map in combination with the behavior label and the environment data; and performing space-time convolution operation on the dynamic coupling risk map to obtain a prediction risk distribution map and generate an individual risk avoiding path. By adopting the method, the underground safety production level of coal mine personnel can be improved.

The invention discloses an automatic driving safety operation system integrating environmental perception and decision reasoning. The system generates and dynamically updates a security risk map covering an operation area by fusing real-time environment perception, historical operation data and traffic management information. On the basis, the collaborative safety decision-making module further carries out behavior modeling and intention prediction on other traffic participants, and in combination with map risks and traffic instructions, a driving strategy is actively adjusted under a dynamic game framework. According to the invention, the safety control is improved from passive response to an active mode of behavior pre-judgment and game dominance, and the safety and reliability of the operating vehicle in a complex environment and the cooperative capability of the operating vehicle and traffic management are obviously enhanced.

The invention discloses an AI-driven multi-tenant cloud computing resource optimal allocation method and system, and belongs to the field of artificial intelligence, and the method comprises the steps: constructing a tenant resource dynamic portrait, and compressing the tenant resource dynamic portrait into a tensor containing a resource type, a time window and disturbance density through a three-dimensional residual attention network based on a resource request, a response time delay and a load jitter sequence; extracting resource competition conflicts among tenants by using a gating map neural encoder, and outputting a conflict risk map; a hierarchical scheduling space is constructed based on the atlas, a throughput maximization and return optimal strategy is trained, and a periodic switching controller is adopted to realize exploration and convergence balance; carrying out robustness screening on scheduling actions, and eliminating sensitive actions by utilizing a self-adaptive annealing mechanism to generate a stable strategy set; and executing resource allocation and continuously collecting microcosmic utilization data to guide subsequent strategy fine adjustment. The method has the beneficial effects that efficient and stable allocation of resource scheduling is realized, and the resource utilization rate and the system robustness in a multi-tenant cloud environment are improved.

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention discloses a method for guiding accurate maintenance, operation and maintenance of an urban pipe network by using an SWMM model. The method comprises the following steps: carding and generating a pipe network image map, confirming key nodes, confirming boundary adjustment, building a pipe network model, operating the SWMM model, checking sedimentation risk prediction and evaluation of a pipe section in a simulation area in an SWMM output report, and giving a sedimentation risk map. On the basis of the SWMM model, in combination with the pipe network topological relation, the actual sewage flow direction, the sewage discharge amount and the sewage discharge process, an intra-region drainage pipe network model is built, full-process and full-region simulation of a drainage system is achieved, the process of pipe networks of all key nodes and key regions can be quantified, the trend can be displayed, and the state can be evaluated; the defect that branch pipe sections and nodes in the middle cannot be detected in the traditional operation and maintenance process of a pipe network is overcome.

The invention discloses a dam seepage intelligent monitoring and risk early warning method, system and device based on an expert large model and a storage medium, and belongs to the field of hydraulic engineering monitoring, and the method comprises the steps: collecting multi-source heterogeneous data from a dam seepage monitoring system, and marking structural domain labels according to data sources and structural attributes; executing corresponding feature fusion processing on different types of data according to the structural domain labels, and constructing a structured feature vector group in a unified format; inputting the feature vector group into a structure-driven expert large model, activating a bound expert path according to a structural domain label, and executing multi-task reasoning; the multi-task reasoning result is input into a dam seepage virtual twin system, a simulation module is driven to conduct seepage diffusion evolution simulation, and a space risk map is generated; and generating an early warning signal and an emergency plan text according to the risk map and the multi-task reasoning result.

The invention provides a quadruped robot path optimization method and system, and the method comprises the steps: building an overlapping operation window recognition mechanism through obtaining the state information and topographic data of a robot, generating a risk map based on a conflict probability, and constructing a hierarchical task chain; establishing path-terrain mapping, identifying a vortex region through an energy flow channel and flow field rotation characteristics, and forming a path dominant region; performing task density analysis on the dominant region, and forming a complementary rhythm through peak-valley separation and frequency analysis to generate an equilibrium flow; main and auxiliary path assignment is carried out on the balanced flow, and high-density path reserve is formed through load analysis and compression processing; performing risk detection on the main path to obtain an early warning, triggering reserve release according to the early warning, and performing cross weaving with the main path to form a reinforcing network; a cooperative propagation matrix is constructed based on a reinforcement network, flow field diffusion analysis is carried out to generate discrete path configuration, a convergence center is established, a unified control command is output, and the cooperative path optimization problem under the complex terrain is effectively solved.

The invention discloses a big data-based marine disaster risk prevention and control early warning system and method, and belongs to the technical field of marine disaster monitoring and early warning. The invention discloses a big data-based marine disaster risk prevention and control early warning system and method. The system comprises a multi-source data fusion module, an intelligent modeling module and a real-time early warning module, the method comprises the following steps: cleaning and storing multi-source data after wavelet transform downsampling through an edge computing node; a space-time Transform network is adopted to extract space-time features, a Coriolis force correction equation is combined to calibrate a predicted value, and the disaster risk probability and tide level and wind speed key parameters in the next 24 hours are generated; graded early warning is triggered based on a dynamic risk map, and second-level pushing is carried out through multiple channels such as a mobile network and emergency broadcast. The tide level prediction error is reduced to be smaller than or equal to 0.2 m, the early warning response delay is smaller than 10 seconds, and the problems that a traditional system is high in false alarm rate and updating lags are effectively solved.