1636 results about "Confidence score" patented technology

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention provides a patient vital sign anomaly detection method based on an artificial intelligence technology, and relates to the technical field of data processing, and the method comprises the steps: collecting original sign data of a patient; calculating multi-dimensional characteristic parameters; establishing an individual baseline model, and determining a comprehensive reference interval in the model; the vital sign features monitored in real time are constructed into multi-dimensional feature vectors, the multi-dimensional feature vectors are input into the individual baseline model, and the deviation degree of the real-time multi-dimensional feature vectors in the comprehensive reference interval is calculated; performing a clustering analysis to identify an anomalous aggregation region; performing trend analysis, calculating change direction consistency and continuous change amplitude of the multi-dimensional feature vector, and generating a trend analysis result; calculating an accumulated change index in a continuous time window according to a trend analysis result to obtain a dynamic confidence score; when the dynamic confidence score continuously exceeds an adaptive threshold value, determining that a vital sign abnormal event exists; the autonomy and accuracy of the method for detecting the vital sign abnormity of the patient are improved.

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention relates to the technical field of communication, and discloses a communication state monitoring management method and system. The method comprises the following steps: acquiring a transmission data packet in a communication network in real time, extracting protocol header information and load content of the data packet, and generating an original monitoring data sequence containing a protocol type, a timestamp and load characteristics; and performing multi-dimensional correlation analysis on the original monitoring data sequence, identifying a logical relationship between protocol header information and load content, detecting an abnormal correlation mode, and generating an abnormal event mark set with a confidence score. And constructing an adaptive behavior model based on the original monitoring data sequence and the abnormal event mark set, and capturing normal behavior characteristics of the communication network by dynamically updating model parameters. Transmitting data packets collected in real time are input into the self-adaptive behavior model for deviation degree calculation, abnormal data packets exceeding a deviation threshold value are screened out, and historical abnormal event mark sets are associated to generate abnormal context records.

The invention discloses a knowledge graph completion method based on combined prediction of large and small models, which comprises the following steps: 1, constructing and preprocessing a knowledge graph completion reference data set, and training by adopting a RotatE model to obtain candidate entities generated by the model and confidence scores; 2, constructing a related triad, an adjacent triad and entity long text description based on the query to form a context prompt, inputting the context prompt and the query into a large language model, and performing semantic reordering and scoring by the large language model; and 3, constructing a fine tuning data set, and performing fine tuning on the large language model to obtain the KGC task optimization-oriented large language model. And based on the KGC score, the LLM score and the dynamic weight, outputting a complementation result through joint prediction of a fusion result. According to the method, the structured reasoning ability of the small model and the deep semantic understanding of the large language are effectively combined, the prediction accuracy, robustness and specialty are remarkably improved, and the defects that a single model is weak in generalization ability and insufficient in semantic utilization are overcome.

The invention relates to the technical field of nondestructive testing, and discloses a bimetal composite pipe three-dimensional reconstruction method and system based on multi-source data fusion. The method comprises the following steps: acquiring magnetic flux leakage signals and thickness data of the bimetal composite pipe in a high-pressure environment, and preprocessing the magnetic flux leakage signals and the thickness data to obtain a clean multi-source signal data set; performing time domain and frequency domain feature alignment on the data set to generate a fusion data matrix; extracting a preliminary defect feature set through multi-layer convolution processing; performing classification training on the defect features to obtain a defect type classification result containing confidence scores; if the crack exists, depth fitting is carried out to quantify the crack depth; if the preset risk threshold value is exceeded, generating a three-dimensional defect distribution model and evaluating connectivity; and finally, outputting a quantitative evaluation report of the pipeline risk level. According to the method, efficient fusion of multi-source data, intelligent identification of defect types and three-dimensional visual reconstruction are realized, and the accuracy and evaluation efficiency of pipeline defect detection are remarkably improved.

The invention discloses a steel coil end face defect detection method and system, a training method and electronic equipment, and the steel coil end face defect detection method comprises the steps: driving a two-dimensional image and three-dimensional point cloud collection equipment to synchronously scan the end face of a steel coil through a movement mechanism, and obtaining registered RGB-D multi-modal data; inputting the data into a special detection model, extracting surface texture and geometric structure features, performing dynamic weighted fusion through channel splicing and a cross-modal attention mechanism, and outputting a suspected defect result containing defect types, positions, three-dimensional depth information and confidence; and carrying out geometric feature consistency verification on the suspected defect by combining the original point cloud, and finally determining a real defect. According to the model, deep synergy of texture and geometric features is innovatively realized, the recognition accuracy and robustness of complex defects such as micro cracks and recesses under reflection interference are remarkably improved, and meanwhile, the detection efficiency is guaranteed.

The invention discloses a multi-system collaborative data cross validation management system, and relates to the technical field of logistics data management, and the system comprises an initial credibility grading module which is used for obtaining a data stream and a preset static source reliability factor, and calculating an initial credibility score; the evidence weight dynamic quantification module is used for calculating a timeliness attenuation factor, matching a current service scene, distributing a service rule adjustment factor and determining a final evidence weight score of the data point; the gold record automatic judgment module is used for sequencing the data points; obtaining a highest score and a second high score; and the judgment confidence evaluation module is used for calculating a judgment confidence margin between the highest score and the second highest score, and automatically routing the conflict data set to a manual auditing queue or automatically confirming a golden record. Through quantitative evaluation, dynamic weighting and automatic judgment, the credible gold record is efficiently generated, and refined risk control and non-tampering decision traceability are realized.

The invention relates to a multi-competency intelligent scoring method and system, and belongs to the technical field of intelligent evaluation and talent evaluation. The method comprises the following steps: acquiring multi-source response data of a target evaluation object, executing evidence sufficiency and consistency analysis for each competency dimension, generating a judgment state identifier, and constructing a structural description; performing responsibility distribution on the evaluation evidence, mapping the evaluation evidence into a corresponding evidence model and generating a priority constraint relationship; performing time sequence consistency and stability analysis on the evidence model, and constructing a time sequence constraint rule to prohibit unconstrained score backtracking; and carrying out resolution processing on the continuous undetermined dimension, generating a scoring result through a preset risk scoring rule and a conservative determination strategy, and synchronously generating a scoring basis path and confidence information. According to the method, standard management and control of multi-source evidences and accurate constraint of the whole scoring process are realized, the scoring accuracy and traceability are effectively improved, and reliable support is provided for multi-scene talent evaluation decision.

A method of evaluating the reliability of a response generated by a language model is described herein. The method includes obtaining a query provided to a language model and a response to the query generated by the language model, and applying hallucination evaluation techniques to one or more of the query and the response to generate hallucination scores. The method also includes combining the hallucination scores to obtain a confidence score, and providing an indication of the confidence score for display on a user interface, where the confidence score indicates the reliability of the response.

The invention provides a bridge structure vibration long-term monitoring algorithm and system based on computer vision, and relates to the technical field of beam monitoring. In the prior art, there is no vision-based bridge monitoring algorithm with high precision, strong robustness and shielding self-recovery capability. According to the method, matching point confidence is calculated, and matching points with low confidence scores are removed; calculating the displacement of the bridge at the plurality of feature points, and generating vibration time sequence data; the inherent frequency and the corresponding modal shape are extracted and compared with the reference frequency range and the reference modal shape, the monitoring result is obtained, shielding self-recovery is carried out according to the environmental interference influence, the precision and reliability of bridge monitoring are improved, and the robustness of bridge monitoring activities in a complex scene is improved.

The invention discloses a document auditing method and device based on cooperation of a large model and a rule engine. The method comprises the following steps: analyzing an original document to form an intermediate representation file; extracting entities from the intermediate representation file to form an entity candidate set; inputting the thinking chain cue word into the large model to obtain a first triple, a first confidence coefficient and a first risk level, and calculating a first traceable score of the large model according to the reasoning path node; performing deterministic judgment by using the rule engine to obtain a second triple, a second confidence coefficient, a risk level and a second traceable score; calculating two weighted voting values and obtaining a conflict difference value; taking a conclusion corresponding to the high-weighted voting value as a final conclusion when the conflict difference value is smaller than a preset judgment threshold value; otherwise, starting an artificial rechecking process; and generating audit reports in various formats. According to the method, the advantages of relatively high language understanding ability of a large model and relatively high certainty of a rule engine are exerted, and the problem of missed checking or excessive marking is avoided.

The invention discloses an automatic driving environment sensing method and system based on multispectral fusion, and relates to the field of auxiliary driving, and the method comprises the steps: collecting environment data in a complex environment through a sensor; performing time-space synchronization and standardized preprocessing on the environment data to obtain preprocessed data; constructing a sensor confidence estimation network, and inputting the preprocessed data into the confidence estimation network; features are extracted and fused through a structure combining a shared encoder and a branch encoder, and a real-time confidence score vector of each sensor is output; a preset mapping function of confidence and weight is obtained, the confidence score vector is converted into a dynamic weight vector, and the sum of all components of the weight vector is 1; and integrating the sensing results of the sensors by adopting a weighted fusion strategy, verifying the sensing results of the high-weight sensors through a cross validation mechanism, and outputting final environment sensing data.

The invention provides a ship power battery state monitoring method and system based on multi-sensor data fusion, and relates to the technical field of batteries, and the method comprises the steps: interactively obtaining multi-mode monitoring data of a ship power battery in a ship matched intelligent power distribution system; generating a mapping confidence score by using the expected output prediction of the reliable twin of the sensor and the residual error of the multi-modal monitoring data; the method comprises the following steps of: after preprocessing multi-modal monitoring data, respectively extracting transient, trend and accumulation characteristics based on a multi-time scale sliding window; inputting the multi-modal feature set into a hierarchical fusion model, executing coupling fusion analysis in a module layer and a cabin ship layer, and outputting comprehensive operation state features; constructing an abnormal probability value according to a multi-dimensional abnormal index calculation result and the mapping confidence score; and monitoring and managing the state of the ship power battery according to the abnormal probability value. According to the invention, the technical problem of low monitoring efficiency of the state of the ship power battery in the prior art can be solved.

The invention discloses a cascade fault diagnosis method based on a root cause intensity power set belief rule base. According to the technical scheme, the method comprises the steps of 1, constructing a power set belief rule base; the conclusion hypothesis unit can clearly describe a single-point fault state, a composite fault state and a causal chain fault state. And 2, introducing a root cause strength parameter: introducing the root cause strength parameter into the conclusion hypothesis unit constructed in the step 1 for quantifying the causal influence capability of the fault state as the root cause. And step 3, reasoning fusion: calculating the matching degree and the activation degree of each rule, and applying an evidence theory-based reasoning fusion algorithm to an output result to obtain power set confidence distribution of each fault hypothesis. And step 4, fault diagnosis and fault root cause positioning are carried out, and a main fault root cause is output, and the main fault root cause is the root cause with the maximum root cause strength. The method is mainly used for realizing accurate diagnosis of cascade faults in a complex system by popularizing a traditional BRB model to power set reasoning and introducing a root cause strength concept.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention relates to the technical field of computer intelligent operation and maintenance and artificial intelligence, in particular to an operation and maintenance scheme generation method, system and device based on cooperation of a large language model and a knowledge graph and a medium. The method comprises the following steps: denoising and abstracting multi-source heterogeneous operation and maintenance data by using a large language model, extracting key fact dimensions, further identifying a fault entity, mapping the fault entity to an operation and maintenance knowledge graph to position an initial anchor point, executing two-stage collaborative reasoning based on the anchor point, and generating a plurality of candidate traceability paths by alternately performing relationship exploration and entity exploration; and finally, reordering the candidate paths based on path length perception and semantic correlation, dynamically controlling the termination and understanding strategy of reasoning by using a comprehensive confidence score, outputting a final solution, effectively overcoming the problems of information redundancy interference reasoning and large model illusion in a complex operation and maintenance scene through the cooperation of a large model and a knowledge graph, and improving the reliability of the system. And the accuracy of root cause positioning and the executable performance of the solution are obviously improved.

The invention provides a business path tracking and fault tracing method and device for a video monitoring system, and relates to the technical field of video monitoring fault diagnos.The video monitoring system is divided into a plurality of layers, and multiple types of data acquisition probes are deployed at key nodes of each layer; when a user initiates a video request, generating a Trace ID to be spread along with a video streaming signaling, and reconstructing an end-to-end service path in combination with node information captured by a probe; associating and fusing the multi-dimensional performance data by taking the TraceID as a key, and establishing an end-to-end total delay decomposition and dynamic health baseline; and positioning a root cause node based on the weighted directed graph, the node anomaly confidence score and the fault propagation consistency, displaying a path, data and a traceability result through a visual interface, and generating a precise alarm. According to the method, service link visualization is realized, traditional equipment alarm is upgraded to service fault root cause positioning, the troubleshooting efficiency is improved, and the method is suitable for an isomerized large-scale video monitoring system.

The invention provides a power distribution communication network fault management process optimization method, which is based on a fault evolution reverse deduction technology of historical repair knowledge, and realizes efficient fault root cause positioning and repair recommendation by combining a dynamic space-time atlas and a graph database technology. The method comprises the following steps: constructing a dynamic space-time atlas, and storing power distribution communication network topology and fault data; constructing a fault causal relationship model, and describing a fault state transition probability and an evolution causal chain; multi-path hypothesis testing is executed, possible fault sources and evolution paths are generated, and confidence scores are distributed; similar fault modes and repair measures are retrieved from a historical repair knowledge base, and a directional repair strategy is recommended; and space-time backtracking analysis is realized, and the whole fault evolution process is visually displayed. According to the method, the root cause positioning efficiency and accuracy are remarkably improved, the fault processing flow is improved, the fault prediction capability is improved, the preventive maintenance level is enhanced, and continuous accumulation and optimized application of maintenance knowledge are realized.

The invention discloses a large language model knowledge verification and improvement method and device and a medium. The method comprises the steps that task description, specific problems and example data input by a user are received; in combination with a preset heuristic cue word and structured processing, generating a logic expression, and removing redundant contradiction rules to form a logic knowledge base; inputting specific questions into the large language model to generate preliminary answers, and performing symbolization conversion on the preliminary answers to extract entities, relationships and attributes so as to obtain symbolized propositions; matching the symbolized proposition with the logic knowledge base, if a conflict is matched, generating a candidate correction scheme through deductive reasoning, determining an optimal scheme according to a predefined correction cost function, feeding back the optimal scheme to the large language model, and regenerating an answer; and iterating the related steps until the answer passes verification, and outputting a final answer, a confidence score and an interpretability report. Through the deductive constraint and bidirectional feedback mechanism, the LLM output reliability is improved, the knowledge base cost is reduced, and dynamic updating and interpretability of knowledge are realized.

The invention discloses a sparse visual angle three-dimensional reconstruction method and system based on voxel grid constraint, and belongs to the technical field of visual three-dimensional reconstruction. Constructing a three-dimensional voxel grid of a self-adaptive scene scale based on point cloud distribution, and dividing the point cloud to the corresponding voxel grid; fusing the geometric features of the fast point feature histogram and the confidence score in the grid, generating a geometric confidence comprehensive measure, and screening key points to initialize Gaussian primitives; designing a voxel grid constrained gradient clipping strategy, limiting Gaussian primitive error diffusion through a distance attenuation coefficient, and adaptively optimizing grid distribution in combination with a dynamic grid deletion and addition mechanism; and finally, carrying out iterative training by using a 3D Gaussian splash radiation field loss function to realize high-fidelity static scene reconstruction under a sparse view angle. According to the method, scene geometric priori is introduced, an optimization strategy based on voxel grid constraint is designed to effectively control excessive diffusion or drift of Gaussian primitives, generation of artifacts is reduced, and meanwhile, the situation that robustness is reduced due to the influence of priori quality is avoided.

The invention discloses a power distribution network single-phase grounding reason diagnosis method based on confidence and ambiguity evaluation. The method comprises the following steps: acquiring original diagnostic data corresponding to a waveform to be detected; determining a confidence level corresponding to the original diagnosis data according to a preset grading rule based on the similarity distance corresponding to each of the plurality of neighbor sample waveforms in the original diagnosis data; determining a plurality of candidate reasons based on the single-phase grounding reasons corresponding to the plurality of neighbor sample waveforms; based on the number of the plurality of neighbor sample waveforms, determining occurrence frequencies corresponding to the plurality of candidate reasons; determining target ambiguity based on the occurrence frequencies corresponding to the candidate reasons and the reason label semantic association matrix; and based on the confidence level and the target ambiguity, adaptively generating a diagnosis result. According to the method, the technical problem that the information completeness of the conclusion output by the existing analysis method is insufficient, so that an objective judgment basis for the conclusion reliability is lacked when the operation and maintenance personnel make decisions is solved.

The invention relates to the technical field of encrypted communication, and discloses an intelligent analysis method and system based on multi-source data, and the method comprises the steps: obtaining multi-dimensional metadata of an encrypted communication flow, and generating a feature vector set; calculating a state transition probability of a communication session by using a Markov chain model, dividing the communication session according to a time window and mapping the communication session to a discrete state, and generating a session state evolution sequence; analyzing a session state evolution sequence based on a hierarchical topic model, mapping the state sequence to a behavior topic through potential Dirichlet allocation, mapping the behavior topic to an intention category through a hierarchical Dirichlet process, and outputting a communication intention category and a confidence score; according to the method, the limitation that static feature analysis cannot reflect the complete life cycle of the session is overcome, the problem of semantic gaps caused by limited metadata information dimensions is solved, and the accuracy and robustness of intention recognition are improved.

The invention discloses a data security management method and device based on dynamic encryption and real-time anomaly detection, and the method comprises the steps: obtaining data from a diversified data source, and obtaining standard data; dynamically encrypting the standard data to obtain encrypted data; performing feature extraction on the encrypted data to obtain data stream features; performing anomaly detection processing on the data stream features by using a detection engine to obtain a detection result; when the detection result is normal, marking the encrypted data as a safe state, and storing the encrypted data into the distributed nodes; when the detection result is abnormal, the danger is handled according to the confidence score, and a danger assessment result is obtained; and according to a risk assessment result, optimizing parameters of the detection engine, and generating a log. Therefore, the problems of static encryption failure, detection lag, response stiffness and the like in the prior art are effectively solved.

The invention discloses a large language model reasoning acceleration method and device based on two-stage speculative decoding and a storage medium, and the method comprises the steps: constructing and initializing a Trie tree, and inserting a historical corpus, and phrase sequences in a document library or a code library into the Trie tree one by one; in the reasoning process, longest prefix matching is carried out based on a Trie tree, and a candidate draft sequence is generated by adopting branch backtracking and recursive search; performing confidence evaluation on the candidate draft sequence, calculating a joint confidence score of the sequence through probability multiplication and a Top-K screening mechanism, and judging whether the joint confidence score reaches a confidence threshold; if the accumulated confidence of the candidate sequence reaches a threshold value, skipping a small model generation stage, and directly entering large model verification; otherwise, entering a small model draft completion stage; and the final large model takes the replaced and updated draft sequence as final output. According to the method, adaptive acceleration of the decoding process can be realized, and the long text reasoning delay of the large language model is remarkably reduced while the generation quality is ensured.

The invention discloses an abnormal data processing method and system for a virtual game, and relates to the technical field of big data processing. Receiving an interaction data frame sequence uploaded by the terminal equipment, and analyzing to obtain an input equipment sampling point set and a displacement coordinate vector; performing feature extraction on the sampling point set of the input equipment to obtain a high-dimensional feature vector; mapping the high-dimensional feature vector to a preset behavior feature space, calculating a multi-dimensional statistical distance of the high-dimensional feature vector in the behavior feature space, and generating a first abnormal confidence coefficient; calling space mapping data corresponding to the virtual scene; performing discretization ray stepping detection in the space mapping data based on the displacement coordinate vector to generate a second abnormal confidence coefficient; performing fusion calculation on the first abnormal confidence coefficient and the second abnormal confidence coefficient based on a dynamic weighting algorithm to obtain a comprehensive abnormal score; and judging abnormal data through the comprehensive abnormal score, and executing a state rollback operation for the virtual object.

The invention relates to a visual identification and trend prediction method and system for wrinkles of a continuous strip-shaped material, and the method comprises the steps: collecting a continuous image sequence of the surface of the continuous strip-shaped material, obtaining a standardized image frame, and synchronously obtaining a working condition state vector; inputting the standardized image frame into a wrinkle identification model, and outputting wrinkle region information, wrinkle category information and corresponding confidence; constructing a wrinkle index vector at the current moment according to the wrinkle region information, the wrinkle category information and the corresponding confidence coefficient; filtering and time sequence modeling are carried out on the wrinkle index vectors at the current and historical moments, and wrinkle index prediction values, trend labels and prediction uncertainty at multiple moments in the future are output; and constructing a future risk index and security constraint set according to the wrinkle index prediction value, the prediction uncertainty and the working condition state vector, solving an optimal parameter packet, and mapping the optimal parameter packet into an executable control instruction for output.

The invention discloses a video monitoring and AI linkage intelligent alarm verification system, and particularly relates to the technical field of video analysis, which comprises the following steps: carrying out dual anomaly preliminary screening by using a flow field structure entropy and a signal track singular value ratio, eliminating environmental noise interference, and constructing a multi-mode normal state baseline; generating a multi-modal event report containing a spatio-temporal context, uploading the multi-modal event report to a cloud, inputting the multi-modal event report into a physical perception cross attention network, configuring a physical embedding vector into a query vector and configuring a visual embedding vector into a key vector and a value vector through an asymmetric feature fusion architecture, and performing multi-modal event report analysis; actively guiding the attention weight distribution of the model on the video picture by using the change trend of the physical parameters, and outputting a confidence score based on the weighted fusion feature; performing closed-loop parameter correction on the baseline model by utilizing online incremental learning based on a verification result; the problems of high false alarm rate caused by lack of physical logic constraints and poor anti-interference capability in a complex environment in traditional monitoring are effectively solved.