1022 results about "Risk forecasting" patented technology

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention discloses a storage AGV dynamic path planning system based on multi-objective optimization, and relates to the technical field of storage logistics, and the system comprises a multi-source sensing and data collection module which is used for collecting the operation state, operation environment and external traffic information of an AGV and generating a standardized feature vector; and the cross-modal digital twinning and risk simulation module is used for constructing a virtual twinning body of a warehouse and external traffic and carrying out risk prediction and simulation under the driving of cross-modal sensing data. According to the invention, through the multi-source sensing and data acquisition module, the system can comprehensively acquire the AGV operation state, the operation environment and the external traffic information, and through combination with an advanced data fusion technology, a high-dimensional standardized feature vector is generated, so that an accurate and comprehensive data basis is provided for subsequent path planning and risk prediction; the cross-modal digital twinning and risk simulation module constructs a virtual twinning body of a warehouse and external traffic, and can reflect the dynamic change of the physical world in real time.

The invention relates to the technical field of safety management and risk prediction, in particular to an intelligent safety management and risk prediction method and system based on cloud computing. The method comprises the following steps: dynamically accessing multi-source heterogeneous data through a cloud platform, and forming unified event representation through time alignment and credibility labeling; constructing a hierarchical mixed probability safety twin model, updating dynamic parameters by adopting credibility weighted online variational Bayesian, and outputting a state interface by combining structural adaptation, cross-object graph regularization and physical constraint projection; mapping the twinborn state into a causal feature, constructing an intervening causal graph, generating causal embedding by using a credibility weighted attention network, simulating an intervention operation in an embedding space, and quantifying a risk probability; and generating a multi-candidate security policy, evaluating and sorting through a multi-objective utility function, executing an optimal policy, collecting feedback data, and updating the model and the policy. According to the method, credibility regulation and control, probability twinning and causal intervention are fused, and real-time intelligent decision making of an industrial safety scene is supported.

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention relates to the technical field of micro-service operation and maintenance, and discloses a full-link intelligent fault simulation and assessment defense method in a micro-service scene. The method comprises the following steps: constructing a dynamic dependency graph based on a service registration center and real-time communication traffic; performing matching backtracking on historical faults according to the atlas, and generating a fault injection point list and a propagation path set sensed by the atlas; in the isolated environment, driving the programmable agent to perform multi-dimensional fault injection, and synchronously acquiring full-amount system response signals; performing multi-dimensional deviation calculation on the signal and the base line to form an observation record containing deviation intensity and propagation rate; and training a graph neural network model with time sequence dependence understanding capability based on the record, and carrying out fault mode identification and evolution prediction on an online real-time link, and outputting a risk assessment report. According to the method, the authenticity of fault simulation and the initiative of risk prediction are improved.

The invention provides an intelligent decision-making method based on multi-dimensional risk coupling modeling of new energy ships and related equipment. The method comprises the following steps: fusing multi-modal data of each new energy ship to obtain a target multi-modal feature vector; mining risk implicit association strength by using a large language model, and constructing a risk knowledge graph; performing risk time sequence evolution prediction by adopting a dynamic Bayesian network based on the atlas to obtain a single-ship risk prediction result; constructing a graph structure according to the single ship risk and the operation parameters, and carrying out space coupling modeling by adopting a graph convolutional network to obtain a cluster risk prediction result; and generating an optimal operation and maintenance strategy by adopting reinforcement learning based on a cluster risk prediction result and a reward function by taking a high-fidelity digital twinborn body as a virtual environment. Therefore, according to the method, the problem of closed-loop adaptive control from risk deduction to decision response in a complex navigation scene is effectively solved by constructing a unified modeling mechanism of multi-dimensional risk coupling and linking real-time control strategy generation.

The invention relates to the technical field of constructional engineering informatization management, and discloses a constructional engineering full-period collaborative management and risk prediction system, which comprises a resource token definition module, a resource token generation module, a resource management module and a risk prediction module, and is characterized in that physical elements are mapped into discrete resource tokens configured with unit time delay charge rates; the process transactional modeling module is used for packaging the process into an atomic transaction unit containing a request and a release instruction; the discrete event rehearsal simulation engine executes circulation in the virtual time axis and records the hanging duration; the deadlock detection module monitors the occupancy topology in real time to identify a loop waiting closed loop; according to the dynamic priority arbitration logic, an accumulated lag weight value is calculated according to the product of the rate and the duration, resources are forcibly allocated accordingly to eliminate deadlock, the dynamic arbitration algorithm based on the time value gradient is constructed, the physical lag cost is converted into the calculation weight, and the capacity of the system for automatically converging to the optimal solution under complex constraints is improved.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention relates to the technical field of pipeline risk monitoring, and discloses a pipeline risk monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source sensing data of a pipeline operation environment, and carrying out the preprocessing of the data, and obtaining a multi-scale time sequence feature set; and constructing a pipe network diagram model, and embedding the multi-scale time sequence feature set into the pipe network diagram model. And learning the pipe network diagram model by using a space-time diagram attention network to obtain a target prediction result. And constructing an expected economic loss function, and determining an optimal risk threshold based on the expected economic loss function. And comparing the optimal risk threshold with a corrosion event probability prediction value to obtain a risk level, and determining a maintenance priority sequence of each risk point according to the risk level, a historical maintenance record and the importance of a pipeline section. And generating a maintenance work order based on the maintenance priority sequence. According to the invention, dynamic and prospective risk prediction is realized, and the economy and operability of pipeline risk monitoring management are improved.

The invention discloses a substation operation and maintenance task risk digital assessment method and system, and belongs to the technical field of task risk digital assessment, and the method comprises the steps: carrying out the hidden risk dominant modeling based on the structural data of a data lake, generating a personnel and equipment dynamic coupling risk coefficient, and carrying out the hidden risk dominant modeling; the space-time diagram neural network captures a personnel-equipment-environment coupling relationship by fusing spatial topology and time sequence features, the model can be associated with a historical fault mode of adjacent equipment, potential risks are identified in advance, and the causal model can identify the potential risks in advance by calculating causal strength between nodes and revealing hidden risk driving factors. The attention weight mechanism dynamically adjusts the fusion proportion of the space and time features, the method adapts to the complex scene of the transformer substation, the dynamic coupling risk coefficient is combined with the frequency and similarity of the historical accident library, the risk weight is quantified, and the probability of artificial misjudgment is reduced.

The invention discloses a watershed water resource supply and demand risk prediction and evaluation method based on a multilevel Bayesian causal network, and relates to the technical field of water resource supply and demand risk management.The watershed water resource supply and demand risk prediction and evaluation method comprises the steps that a water resource supply and demand risk diagnosis knowledge graph is constructed according to key variables and interrelations input by a user; constructing a multi-level Bayesian causal network structure; estimating conditional probability distribution among the nodes, and performing parameter learning and structure training on the Bayesian causal network; carrying out risk path identification through a reverse Bayesian reasoning method; outputting a posterior probability of water resource supply and demand risk prediction; based on a preset fuzzy character string matching algorithm, typical risk events and risk features are extracted; and according to the posterior probability and the risk characteristics, comprehensively evaluating the water resource supply and demand risk level. The method can improve the systematicness and scientificity of risk identification, is suitable for multi-link and multi-scale risk assessment and scheme comparison and selection in a complex drainage basin, and has high practical value and popularization prospect.

The invention relates to the technical field of financial risk prediction, and discloses a financial risk assessment intelligent prediction method based on big data. The method comprises the following steps: collecting historical transaction flow data of a target evaluation object from a multi-source heterogeneous financial database, and obtaining macroeconomic indexes and industry fluctuation parameters in real time; performing abnormal value filtering on the historical transaction flow data to obtain a basic feature set of the risk assessment dimension, and determining a feature updating frequency according to the basic feature set; obtaining a financial market fund flow map, obtaining a risk conduction coefficient of the target evaluation object in a transaction period through association rule mining, and determining a risk diffusion rate in combination with a macroeconomic index; and finally, according to the characteristic updating frequency and the risk diffusion rate, predicting the potential risk trigger point in the duration of the financial product. According to the method, multi-source data are integrated, the risk evolution law is dynamically captured, and the comprehensiveness and timeliness of financial risk prediction are improved.

The invention relates to the technical field of high-risk operation risk early warning, in particular to a high-risk operation personnel dynamic risk early warning method and system based on multi-modal fusion, and the system synchronously collects physiological signals, behavior data and environment data of workers through a multi-modal data collection module, carries out the denoising, normalization and time alignment of a data preprocessing module, and carries out the early warning of the dynamic risk of the workers. Key features are extracted by the feature extraction module, sequential feature extraction and cross-modal collaborative learning are carried out by the multi-modal data fusion module adopting a Transform model, finally, a dynamic comprehensive risk index is output by the risk prediction module, and an early warning output module sets an early warning level according to the risk index and gives an alarm. According to the dynamic risk early-warning method and system for the high-risk operating personnel based on multi-modal fusion, the risk can be predicted 5-10 minutes in advance, the false alarm rate is reduced to 5% or below, the composite risk identification accuracy rate exceeds 90%, the method and system can adapt to individual differences, and the safety of the high-risk operating personnel is effectively guaranteed.

The invention discloses a risk assessment method and system based on a Bayesian network and an evidence theory, and relates to the technical field of risk intelligent assessment, and the method comprises the steps: determining an assessment dimension and an assessment index of a high-investment low-income risk of an educational institution in MOOC learning according to a human-cargo-field model and an industry report; constructing a MOOC learning risk assessment index system according to the assessment dimensions and the assessment indexes; constructing a Bayesian network structure according to the MOOC learning risk assessment index system; obtaining questionnaire data and expert opinions according to the MOOC learning risk assessment index system, and determining Bayesian network parameters based on the questionnaire data and the expert opinions; and inputting the Bayesian network parameters into the Bayesian network structure to obtain an assessment result of the high-investment low-income risk, the assessment result including a risk prediction result, a key risk factor and a sensitivity analysis result. According to the method, the high-investment and low-income risk in MOOC learning can be accurately evaluated.

The invention relates to the technical field of supply chain risk prediction based on causal reasoning, and discloses a supply chain risk prediction method and device based on causal reasoning, equipment and a medium. The method comprises the following steps: constructing a double-layer knowledge graph of a regulation layer knowledge graph and a logistics physical layer knowledge graph; real-time fusion and characterization of geographical regulation and logistics operation multi-source heterogeneous data are realized, the perception real-time performance and coverage dimension of risk events are improved, then a multi-level causal chain is automatically generated by introducing a large language model, a complex conduction path of risks in a supply chain network can be deeply inferred, and the risk prediction accuracy is improved. According to the method, the defect that hidden association mining is insufficient in a traditional method is overcome, reasoning of risk traceability is improved, and finally a risk prediction result is obtained by mapping causal chain nodes to a logistics physical layer knowledge graph and performing probabilistic calculation. The method has the beneficial effects that the risk prediction of the specified supply chain is realized, and the accuracy of the risk prediction result is improved.

The invention discloses an electric power cross-operation dynamic risk prediction and early warning method, system, equipment and medium based on multi-modal data fusion, and relates to the technical field of intelligent electric power systems, and the method comprises the steps: collecting multi-source data of a cross-operation site in real time, including personnel spatial position data, equipment state data, environmental parameters and operation flow information; performing multi-modal fusion processing on the multi-source data, and constructing space-time risk association features; constructing a dynamic risk factor matrix, performing fusion calculation on the static risk reference value and the dynamic correction value, and dynamically updating the weight of each factor along with time; predicting a risk change trend based on the combination of a time sequence prediction model and a graph structure modeling method; and generating a three-dimensional visual risk thermodynamic diagram based on a prediction result, and triggering a multi-level early warning mechanism when a risk value exceeds a threshold value. The image is reconstructed through multi-modal fusion processing, and key edge features can be recovered in complex environments such as low illumination, high dynamic range and fast motion.

The invention discloses a food safety risk prediction method based on production data analysis, and belongs to the technical field of food safety, and the method specifically comprises the steps: collecting the production parameter time sequence data of a single raw material unit after each processing procedure in real time; associating the historical production parameter sequence with the corresponding sampling inspection result to construct a training sample set, and training to obtain a risk quantification association model; predicting a real-time production parameter sequence by using the model to obtain a risk value; dividing risk levels according to the risk values, endowing the high-risk units with unique sequence identifiers, and identifying high-risk products from the final products according to the unique sequence identifiers; and checking the high-risk product and updating the model according to a result. According to the method, accurate risk prediction of individual raw material units is realized through multi-process time sequence data analysis, a whole-process closed-loop management system from prediction, identification tracking to model updating is established, and the accuracy and timeliness of food safety risk identification are remarkably improved.

The invention discloses an urban intelligent water affair risk dynamic identification and early warning method based on deep learning, and the method comprises the following steps: S1, collecting the water pressure, flow, residual chlorine concentration, elevation, rainfall, valve state, pump station state and accident label of each node in a water supply network, and constructing a time alignment data sequence; s2, constructing a dynamic adjacency matrix according to the pipe network connection relation and the event state information; s3, inputting the data sequence and the dynamic adjacency matrix into an improved space-time diagram wavelet neural network to generate space-time feature representation; s4, multi-scale features are extracted and fused through the high-frequency branches and the low-frequency branches; s5, constructing a hyperedge set, executing graph structure propagation, and generating a risk representation tensor; s6, inputting the risk representation tensor into the risk prediction network, and outputting a node risk probability and a confidence interval; and S7, determining a risk level according to the risk probability and the confidence interval, and generating a corresponding early warning signal. According to the invention, fine modeling and dynamic early warning of urban water supply risks are realized.

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.

The invention discloses a transformer substation switching operation risk pre-control method and system based on deep reinforcement learning, and relates to the technical field of power system automation, and the method comprises the steps: collecting transformer substation equipment data in real time, processing the collected data, generating a standardized time series data set, building a system topology structure represented by multiple graphs based on the processed data, and carrying out the pre-control of the transformer substation switching operation risk. Integrating dynamic characteristics, based on a system topological structure, generating a switching operation decision sequence, dynamically adjusting a risk boundary threshold, based on a real-time system state, evaluating the security of the decision sequence, intercepting operations which do not conform to a security boundary, performing reward attribution on contributions of operation sequence steps, and constructing a digital twin environment; the model performance is improved through interactive calibration of real data and simulation data. According to the substation switching operation risk pre-control method based on deep reinforcement learning, high-precision risk prediction is realized, the early warning time is sufficient, and the operation sequence is optimized, so that the safety of substation switching operation is remarkably improved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention relates to the technical field of industrial vehicle safety control, in particular to a forklift safety online industrial monitoring management system which comprises a data fusion unit used for collecting kinematics data, position and attitude information and load distribution data of a vehicle and conducting space-time alignment processing on the collected data to generate a real-time state vector; the margin calculation unit is used for calculating a plurality of independent safety margin components and carrying out fusion processing based on the safety margin components to generate a comprehensive safety margin; the risk prediction unit is used for predicting a future safety margin value based on the time sequence of the comprehensive safety margin, and comparing and analyzing the future safety margin value with a preset safety threshold and a preset warning threshold to generate a risk level; the intervention control unit is used for generating and executing a corresponding hierarchical intervention strategy in response to the risk level; according to the invention, a complete technical link from multi-source data sensing to closed-loop control is constructed, and the conversion from passive safety to active safety is realized.

The invention provides a cloud AI data leakage risk prediction and control method and system based on a flow map, and relates to the technical field of cloud computing data security. Based on the collected multi-source log data, constructing a time series data flow knowledge graph; inputting the knowledge graph into a space-time diagram risk prediction model, and obtaining a dynamic risk score of an entity and a predicted potential data leakage path by fusing a space-time diagram neural network and risk conduction simulation; according to the dynamic risk score and the potential data leakage path, gradient dynamic security management and control measures are generated and executed, and the measures comprise differentiated access control actions triggered according to the risk level; and generating a visual audit report of the data access link based on the risk prediction result and the security management and control process. Through a time sequence graph reflecting dynamic flow of data and utilizing STGNN to carry out risk conduction modeling and prediction, the active defense capability of cloud AI data security is improved, the accuracy of risk identification is effectively improved, and service interference is effectively reduced.

The invention discloses a side slope protection system based on slide-resistant piles, and belongs to the technical field of side slope treatment. The system comprises a data acquisition module, a digital twin modeling module, a multi-pile cooperative control module, an anti-slide pile parameter adjustment module, a stress change capture module and a risk prediction and early warning module. Multi-dimensional data are acquired in real time through the data acquisition module, the multi-pile cooperative control module divides a core group and a cooperative group, virtual parameters are dynamically adjusted in combination with the parameter adjustment module, the stress trend is tracked through the stress change capture module, and the risk prediction and early warning module generates multi-level early warning. The technical effects of slide-resistant pile supporting parameter dynamic optimization, pile group collaborative unloading, stability intelligent prediction and risk active early warning are achieved, and the problems that in the prior art, slide-resistant pile static design cannot adapt to slope dynamic changes, multi-pile collaboration is insufficient, risk assessment lags behind, and the parameter adjustment cost is high are solved.

The invention provides a progress risk combined prediction method and system for a construction task, and relates to the technical field of machine learning, and the method comprises the steps: obtaining a plurality of processes based on the construction task, and obtaining a dynamic resource parameter and an environment interference parameter; performing correlation analysis on the plurality of processes, and obtaining a process influence network; constructing a plurality of risk predictors, predicting the construction risks of the plurality of processes based on the dynamic resource parameters and the environmental interference parameters, and obtaining a plurality of predicted process risks; and obtaining a progress joint risk prediction result based on the process influence network and the prediction process risk. The technical problem of insufficient progress risk prediction precision of the construction task in the prior art is solved.

The invention relates to the field of automatic driving and traffic safety, in particular to a fine-grained multi-task driving risk prediction method fused with a trajectory prediction auxiliary task. Comprising the following steps: step 1, automatically labeling risk labels based on trajectory data; 2, designing a main task of the driving risk prediction model; 3, auxiliary task design of the driving risk prediction model; and 4, constructing and training a driving risk prediction model. An experiment result based on a disclosed NGSIM data set shows that the precision and robustness of a driving risk prediction model can be remarkably improved by introducing trajectory prediction as an auxiliary task.

The invention discloses an energy corridor forest fire risk prediction method and system based on multi-source data fusion, and the method comprises the steps: constructing a fusion data set, and determining risk evolution parameters; if the risk evolution parameter exceeds a preset threshold value, triggering a vegetation difference analysis function to obtain a section heterogeneity label; after section heterogeneity labels are obtained, time-space non-uniformity compensation correction is conducted on historical data through a time sequence change tracking method, the risk transition probability of each section is calculated, and potential critical points are judged; if the potential critical point is judged to be high in probability, activating a difference early warning mechanism, generating a targeted risk level map according to a section heterogeneity label and a risk transition probability, and obtaining a dynamic early warning signal; and carrying out iterative verification on the dynamic early warning signal by adopting a real-time update flow in the fusion data set, adjusting a risk level in combination with prediction output of the long and short-term memory network, and determining a final risk prediction result. According to the invention, the timeliness and accuracy of fire risk prediction are improved.