589 results about "Risk indicator" patented technology

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention discloses an index anomaly detection and adaptive optimization method and system based on multi-model fusion, and relates to the technical field of intelligent operation and maintenance of a power system. According to the method, a dynamic causal network diagram is constructed based on an operation data stream, wavelet coherence analysis and a Bayesian-space-time diagram structure are fused, an edge weight is updated in real time, and a propagation probability is calculated; calling a plurality of anomaly detection models in parallel, dynamically adjusting fusion weight according to the confidence score and the propagation risk coefficient, and generating a fusion anomaly score result; and for a high-risk index section, extracting time frequency characteristics and topological structure characteristics, inputting a lightweight model to generate a confidence coefficient correction factor, calculating an abnormal influence value, and driving monitoring resource adaptive allocation. According to the method and the system, the model adaptability, the anomaly detection precision and the response efficiency in a complex power scene are improved.

The invention relates to the technical field of industrial chain monitoring and treatment, in particular to a monitoring method and system for industrial chain breakpoint treatment. The method comprises the steps that production, logistics, finance, policy and environment dynamic information is acquired through multi-source data, industrial chain comprehensive characteristics are generated through standardization, fractal dimension embedding expression and cross-dimension fusion, and historical trend dependency is introduced to enhance prospective prediction; a dynamic coupling network is constructed, and risk propagation intensity between nodes is quantified by using a dynamic edge weight, so that cross-level breakpoint propagation analysis is realized; risk indexes are calculated by fusing node features and a network structure, breakpoint candidate nodes are screened in combination with an adaptive threshold value, and a multi-step evolution trend is predicted by adopting a nonlinear propagation function and mapped into a multi-level early warning level. And generating a governance strategy according to the risk level, and evaluating the effect in real time and dynamically adjusting parameters through a closed-loop optimization mechanism. According to the invention, closed-loop management of risk identification, prediction and adaptive treatment is realized.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention discloses a method and system for predicting leakage of a water supply pipe network, and the method comprises the steps: modeling nodes and pipe sections of the pipe network into a graph topological structure, and endowing the nodes and the pipe sections with static attributes; collecting operation data of the water supply network, and constructing time-varying graph data corresponding to the graph topology; combining the time-varying graph data with the static attributes to form space-time input features; constructing a graph time sequence prediction model based on a deep learning framework, performing graph structure feature extraction on node graph features and pipe section graph features of each time step to obtain node space features and pipe section space features, and outputting a node and pipe section space-time representation set; evaluating and analyzing the leakage level of each DMA or pressure partition; generating a pipe section leakage risk space distribution set; constructing a joint loss function, and training and updating the graph time sequence prediction model; and inputting operation data acquired in real time into the trained graph time sequence prediction model, and generating a leakage rate prediction value of each partition and a leakage risk index of each pipe section on line for leakage prediction and operation and maintenance decision.

HyperText Markup Language (HTML) content analysis (HCA) using machine learning is described. A feature vector schema may be generated based on domain names corresponding to HTML webpages and corresponding indications of a status of the HTML webpage. The schema may map each position in a feature vector of a given HTML webpage to a resource identifier. Information may be processed using the schema to generate respective feature vectors. The feature vectors may be used to train a model to generate risk indicators for HTML webpages. A potentially parked domain webpage or a potentially malicious domain webpage may be received. A feature vector for the webpage may be generated and inputted to the model. The model may generate a risk indicator for the webpage. The risk indicator may be output and may cause responsive actions. The model may be updated based on a determination indicating whether the webpage was a parked domain webpage or a malicious domain webpage.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention relates to a safety production equipment risk monitoring method and system, and belongs to the technical field of risk monitoring, and the method comprises the steps: obtaining historical operation data and real-time operation data of to-be-monitored safety production equipment; setting a dynamic threshold according to the historical operation data, predicting the range of the dynamic threshold according to preset target process operation data and the historical operation data through a bidirectional long-short-term memory network, and updating the range of the dynamic threshold in combination with the real-time operation data to obtain an updated dynamic threshold; the comprehensive risk index is calculated according to the updated dynamic threshold value and the real-time operation data through the analytic hierarchy process, the risk of the safety production equipment to be monitored is evaluated according to the comprehensive risk index, an evaluation result is obtained, dynamic monitoring and evaluation of the risk of the safety production equipment are achieved, and the safety production equipment risk evaluation efficiency is improved. The actual operation state of the equipment can be reflected more accurately, potential risks can be found in time, corresponding measures can be taken, and the operation safety and reliability of the equipment are improved.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The invention discloses a hydraulic engineering data intelligent monitoring method and system based on digital twinning, and the method comprises the steps: comprehensively considering the actual physical structure, operation parameters and surrounding environment information of a hydraulic engineering when a hydraulic engineering digital twinning model is constructed, and breaking through the limitation that a conventional monitoring means only pays attention to a single parameter or a few parameters; model association data is utilized and visualized display is carried out, management personnel can visually check states and parameter changes of all parts, the monitoring efficiency and comprehensiveness are improved, and the limitation that a traditional monitoring means lacks effective analysis and mining is solved. A risk assessment formula is adopted to convert data into risk indexes, so that managers can visually know the safety condition, when a risk assessment value exceeds a threshold value, the risk development trend and consequences are simulated and analyzed in combination with a model, problems and risks are found in advance, early warning is triggered, and related personnel can formulate response measures such as flood discharge flow adjustment and structure reinforcement according to simulation results. Disaster loss is reduced, and the safety and reliability of hydraulic engineering are improved.

The invention relates to a quality risk assessment method for a highway bridge engineering construction stage and a medium, and the method comprises the steps: determining the quality influence factor indexes of the highway bridge engineering construction stage, carrying out the dimension reduction through employing a PCA method, and constructing a quality risk index system, wherein the quality risk index system comprises a risk factor layer and a corresponding index layer; based on the quality risk index system, adopting a DEMATEL method to calculate the subjective influence weight of each index; based on the quality risk index system, an improved CRITIC method is adopted to calculate the objective weight of each index; based on the subjective influence weight and the objective weight, determining a comprehensive weight of each index by adopting a minimum identification information theory; and based on the comprehensive weight, combining the determined grade standard interval, and based on a variable fuzzy set theory, carrying out quality risk assessment, and outputting a quality risk assessment result. Compared with the prior art, the method has the advantages of improving the reliability of risk grade evaluation and the like.

The invention provides a construction state monitoring and risk assessment method and device based on BIM multi-mode conversion, and relates to the technical field of building information models. According to the method, a standardized image mode is generated by analyzing and extracting component information of a BIM model, and a BIM text mode is generated by using natural language description; constructing a graph structure mode based on space and construction logic, and realizing unified alignment and deep fusion of multi-modal data through multi-level modal alignment and a cross-modal attention mechanism to obtain a cross-modal fusion representation which is used for inputting a state recognition model and automatically detecting an execution deviation so as to monitor a construction state; and then introducing a deviation conduction mechanism to quantitatively calculate a comprehensive risk index of the component so as to carry out risk assessment. According to the method, the fusion representation which not only keeps semantic consistency but also conforms to construction logic can be obtained, the abstract cross-modal semantic features are converted into quantifiable and interpretable construction states and risk indexes, and powerful support is provided for intelligent analysis and application in a construction scene.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

The invention relates to a medical decision-making method and device based on a neural symbol hybrid model, and a storage medium, and relates to the technical field of medical information processing. The method comprises the following steps: firstly, fusing multi-modal clinical data of a target object through an attention weighting mechanism to obtain multi-modal fusion representation; then, on the basis of a medical mask enhancement mechanism of a high-risk index, associating the high-risk index of the structure perception type cross-modal attention network constructed on the basis of multi-modal fusion representation with a medical index mask to obtain a joint feature vector of the target object; and inputting the joint feature vector into a neural network decision layer to obtain a neural network recommendation vector, and inputting the joint feature vector into a medical knowledge graph to obtain a symbol rule recommendation vector. And finally, inputting the neural network recommendation vector, the symbol rule recommendation vector and the joint feature vector into a three-layer neural symbol fusion network to obtain a target decision suggestion. Therefore, the accuracy, interpretability and clinical suitability of medical intelligent decision making are improved.

The invention discloses a gestational diabetes risk intelligent prediction method based on multi-modal data fusion, and particularly relates to the technical field of data processing, and the method comprises the steps: S1, setting a prediction window, S2, obtaining risk detection data, S3, constructing a multi-modal time sequence matrix, S4, carrying out risk analysis, S5, constructing a gestational risk change curve, and S6, carrying out visual display. According to the method, detection is carried out through physiological risk indexes and biochemical risk indexes, multi-modal time sequence matrix construction is carried out based on risk detection data, the data acquisition accuracy is met, a matrix structure of time periods and multiple indexes is formed, correlation analysis is carried out on pregnancy risks and detection time periods, and a pregnancy risk change curve is constructed from the correlation analysis; the dynamic evolution of the risk along with pregnancy is visually displayed, the risk occurrence time can be accurately positioned through inflection point detection, key detection of the risk occurrence time is facilitated, and a structured foundation is laid for the follow-up risk change trend and fluctuation amplitude.

The invention provides a fault map learning-based early abnormality diagnosis method and system for an anion exchange membrane electrolysis system, and relates to the technical field of fault diagnosis. The method comprises the following steps of: acquiring multi-working-condition operation measurement and constructing a time sequence characteristic fragment, establishing a fault map containing parts, working conditions and measurement nodes by combining a part relationship and working condition dependency, training and calibrating a map learning model on the fault map, and extracting a self-adaptive baseline; and projecting the feature fragments to a fault map, outputting multi-level risk indexes, generating a comprehensive risk score and an anomaly candidate set, and implementing multi-scale threshold judgment and stability test in combination with a baseline to form early warning and disposal suggestions. And performing further attribution analysis on the abnormal candidate set, identifying key components, and reinjecting maintenance feedback to update the atlas and the model to form a continuously optimized knowledge base. According to the method, causal correlation modeling and interpretable diagnosis are realized, and the accuracy and timeliness of early abnormality identification of the AEM electrolysis system are effectively improved.

The invention discloses a financial analysis-based transaction anomaly detection method and system, and the method comprises the steps: obtaining transaction network data, calculating a risk index through node transaction data, and marking the risk index as an abnormal node if the risk index exceeds a threshold value; obtaining transaction links associated with the abnormal nodes to form a to-be-detected set, performing time sequence segmentation processing on the to-be-detected set to generate transaction segmentation points, and collecting multi-dimensional data to construct a transaction feature matrix; calculating the mode similarity between a to-be-detected link and a normal link, screening the high-similarity normal link, clustering the feature matrix of the high-similarity normal link, and generating a reference clustering center; and by calculating the deviation degree between the feature matrix of the link to be detected and the reference center, determining that the link is an abnormal transaction link if the threshold value is super dynamic. According to the invention, through multi-dimensional node modeling, time sequence dynamic feature analysis and mode matching, accurate identification of abnormal transaction nodes and links is realized, and detection comprehensiveness and prevention and control accuracy are improved.

The invention discloses an intelligent monitoring method and system based on dual-mode communication, and relates to the technical field of dual-mode communication, and the method comprises the steps: obtaining basic data information, extracting multi-dimensional signal features to construct a spectrum feature library, and calculating the real-time noise similarity; calculating a cross mode support degree between the double signals according to the multi-dimensional signal features; acquiring propagation characteristics of all nodes in a second range, and generating a secondary anomaly propagation graph; analyzing causal features according to the secondary anomaly propagation graph, determining a causal path, identifying a risk index to obtain a risk index, and predicting a threat level; the noise interference condition is quickly identified, different conditions of the communication problem and the noise interference are effectively distinguished, and the noise identification accuracy is improved; time domain complementarity and space domain collaboration of an HPLC mode and an HRF mode are quantified, a single-mode sensing blind area is eliminated, a feature fusion strategy is dynamically adjusted, and monitoring accuracy is improved.

The invention discloses an intelligent road traffic risk early warning method and system based on vehicle-road cooperation, and relates to the technical field of traffic early warning, and the method comprises the steps: unifying real-time collected road data to a UTM coordinate system through combining time alignment and coordinate conversion, and constructing standardized spatio-temporal data; based on the standardized spatio-temporal data, using a hypergraph neural network to construct a dynamic hypergraph, obtaining node risk features, and generating a comprehensive risk index; according to the risk index feature weight distribution vector, risk root cause probability distribution is obtained through intervention calculation, and a risk level and an early warning type are output; real-time road data are safely aggregated through federal learning, and the dynamic hypergraph is updated in combination with differential privacy. According to the method, the dynamic hypergraph is constructed, the hypergraph neural network is used for capturing the high-order interaction relation between the vehicles, the problem that complex space-time interaction modes between the vehicles are difficult to capture is solved, and the extraction efficiency of node risk features is improved.

The invention relates to the technical field of data analysis, can be applied to the field of medical health and financial science and technology businesses, and discloses a cross-domain abnormal reason determination method, which comprises the following steps: preprocessing heterogeneous data, and storing the preprocessed data into a unified database; extracting time sequence data from the unified database, inputting the time sequence data into a Transform encoder, and outputting a feature vector; inputting the feature vector into an interpretable elevator for training, extracting a causal contribution value, and performing standardization processing on the causal contribution value to obtain a causal influence score; sorting the causal influence scores according to a system health target, and outputting a processing sequence; and according to the processing sequence, recording and tracing the abnormal reason of the risk index through the explainable shape function and feature interaction of the elevator. According to the method, time series data in heterogeneous data are converted into feature vectors through a Transform encoder, causal contribution values are extracted through training of an interpretable elevator and standardized to obtain CIS, the negative CIS is sorted according to a system health target, a processing sequence is output, and abnormal reasons are traced according to EBM interpretability.

The invention discloses an environmental component detection data processing method and system in occupational health, and belongs to the technical field of environmental data processing, and the method comprises the steps: obtaining the original detection data of a multi-source detection device, obtaining the real-time position information of a worker, carrying out the matching, generating a structured monitoring matrix, and calculating a dynamic risk value; generating a real-time risk vector, identifying a combined pollution scene, calculating an enhancement effect, generating a superposition risk index, comparing the superposition risk index with an occupational contact limit value standard, generating a risk grading signal, carrying out short-term exposure trend analysis, and predicting future risk grade change. And generating an early warning decision instruction containing the current risk level and the predicted risk level to execute a double-path intervention strategy, and generating an equipment control signal for driving the physical equipment to act. According to the invention, multi-source monitoring data is dynamically associated with personnel positions, and multi-dimensional risk calculation, trend prediction and closed-loop control are carried out, so that accurate dynamic assessment and prospective early warning intervention of individual risks can be realized.

The invention discloses a plant gas monitoring equipment periodic data management method, system, equipment and medium, and relates to the technical field of data processing, and the method comprises the following steps: obtaining monitored gas and a to-be-monitored plant, dividing into a plurality of monitoring areas, obtaining area information, setting gas monitoring equipment, and obtaining periodic concentration data; acquiring to-be-managed data, acquiring a concentration change rate, and acquiring a static risk index; obtaining the ith monitoring area, taking the monitoring area communicated with the ith monitoring area as an effective influence area, and obtaining a dynamic risk index according to the area information and the concentration change rate of the ith monitoring area and the area information and the concentration change rate of the effective influence area; and obtaining a gas risk index corresponding to the to-be-monitored plant according to the static risk index and the dynamic risk index of each monitoring area, and obtaining an early warning strategy according to the gas risk index corresponding to the to-be-monitored plant. The method has the advantages of accurate and effective early warning, accurate risk positioning and ineffective early warning suppression.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

The invention relates to the technical field of intelligent medical information, in particular to a urinary surgery risk AI intelligent assessment method and system. The method comprises the steps that intraoperative data in the urinary surgery operation process are collected in real time; inputting the intraoperative data into a risk feature fusion AI model, performing context association analysis of different stages of the operation, and fusing each intraoperative multi-source modal feature to generate a risk assessment comprehensive feature set; analyzing the risk assessment comprehensive feature set by using a risk assessment prediction AI model, dynamically adjusting the priority and prediction weight of each risk index in combination with a doctor judgment priority weight adjustment mechanism, and predicting to obtain an intra-operative real-time comprehensive risk score; and visually displaying the intraoperative real-time comprehensive risk score and the risk prompt information in a terminal in real time. According to the AI intelligent assessment method, the causal relationship between the risks is constructed by associating the data of each stage of the operation with doctor judgment, and the AI intelligent assessment method capable of dynamically predicting the key risks in the operation process is realized.

The invention discloses a reinforcement learning unmanned ship path control method based on double-track regulation and control random network distillation. The method comprises the operation steps that an unmanned ship builds a path tracking simulation environment and a kinetic model; the unmanned ship builds a core algorithm flexible action evaluation algorithm framework; the unmanned ship deploys a priority experience playback pool based on quality and success guidance; an uncertainty perception and risk perception mechanism is introduced into the unmanned ship; the unmanned ship builds a success rate-based reward attenuation and cold start module, and the unmanned ship calculates a total reward and designs a reward softening mechanism to smooth the total reward; the unmanned ship imports hyper-parameters of all the modules, starts training circulation in a simulation environment, and dynamically adjusts exploration intensity and the like; according to the method, uncertainty and risk indexes are introduced, the exploration intensity of the intelligent agent is controlled, the intelligent agent is prevented from making dangerous actions, and the robustness is improved; a priority experience playback pool based on quality and success guidance is introduced, high-quality samples are better played back, and strategy convergence is accelerated.

The invention discloses a transformer substation hidden danger point risk index model construction method, system and device and a medium, and belongs to the technical field of transformer substation risk assessment, and the method comprises the steps: collecting multi-dimensional data related to the operation of a target transformer substation; screening key factors influencing the risk of the hidden danger point, and determining the weight of each key factor; analyzing the multi-dimensional data by using a machine learning model, and identifying hidden danger points; constructing a risk assessment model and calculating risk indexes according to the weights of the key factors and the identified hidden danger points; and outputting the risk level of the hidden danger point and corresponding early warning information based on the risk index. By combining multi-dimensional data and utilizing a machine learning algorithm, the equipment operation state can be comprehensively analyzed, potential hidden dangers can be deeply mined, a large amount of historical data can be efficiently processed and analyzed, potential hidden danger points can be automatically recognized, the hidden danger point recognition accuracy is remarkably improved, early warning is ensured, equipment faults are effectively prevented, and the method is suitable for popularization and application. The operation risk of the substation is reduced.

A building-integrated artificial intelligence system forming a continuous ambient neural field is disclosed. The system includes a distributed multimodal sensor lattice, an on-premise symbolic cognition engine, and an adaptive environmental control kernel operating entirely at the building edge without reliance on external cloud services. Sensor data from optical, thermal, acoustic, airflow, pressure, structural, electrical, and chemical modalities are transformed into non-identifying occupancy vectors, behavioral glyphs, risk indicators, and environmental state descriptors. A privacy-governed policy graph determines sensor permissions, redaction thresholds, consent conditions, emergency overrides, and jurisdiction-specific compliance parameters. The neural field predicts occupancy loads, optimizes HVAC, ventilation, and lighting, detects accidents and structural anomalies, classifies emergent risks, and generates redacted event capsules for audit and emergency dispatch. A federated topology enables multiple buildings to exchange compressed symbolic templates to improve predictive accuracy without transmitting raw data. The system provides a universal, regulation-aligned AI nervous system for autonomous building operations.